{ "schema_version": "1.0", "slug": "viator", "name": "Viator", "subtitle": "Cross-border cases", "description": "Cases that name a jurisdiction outside the developer’s headquarters jurisdiction.", "note": "Headquarters and case locations describe the organizations and settings. They do not establish where a model was trained or whether data crossed a border.", "filters": [ { "key": "origin", "label": "Developer headquarters", "options": [ { "value": "Canada", "label": "Canada", "count": 2 }, { "value": "China", "label": "China", "count": 1 }, { "value": "Finland", "label": "Finland", "count": 2 }, { "value": "France", "label": "France", "count": 7 }, { "value": "Israel", "label": "Israel", "count": 2 }, { "value": "Japan", "label": "Japan", "count": 1 }, { "value": "Netherlands", "label": "Netherlands", "count": 5 }, { "value": "South Korea", "label": "South Korea", "count": 1 }, { "value": "Switzerland", "label": "Switzerland", "count": 1 }, { "value": "United Kingdom", "label": "United Kingdom", "count": 9 }, { "value": "United States", "label": "United States", "count": 67 } ] }, { "key": "destination", "label": "Case jurisdiction", "options": [ { "value": "Algeria", "label": "Algeria", "count": 1 }, { "value": "Argentina", "label": "Argentina", "count": 2 }, { "value": "Australia", "label": "Australia", "count": 5 }, { "value": "Austria", "label": "Austria", "count": 3 }, { "value": "Azerbaijan", "label": "Azerbaijan", "count": 1 }, { "value": "Bangladesh", "label": "Bangladesh", "count": 1 }, { "value": "Belgium", "label": "Belgium", "count": 1 }, { "value": "Brazil", "label": "Brazil", "count": 4 }, { "value": "Bulgaria", "label": "Bulgaria", "count": 1 }, { "value": "Burkina Faso", "label": "Burkina Faso", "count": 1 }, { "value": "Cambodia", "label": "Cambodia", "count": 2 }, { "value": "Canada", "label": "Canada", "count": 5 }, { "value": "China", "label": "China", "count": 3 }, { "value": "Denmark", "label": "Denmark", "count": 2 }, { "value": "Egypt", "label": "Egypt", "count": 1 }, { "value": "Germany", "label": "Germany", "count": 5 }, { "value": "Greece", "label": "Greece", "count": 2 }, { "value": "Guatemala", "label": "Guatemala", "count": 1 }, { "value": "Guyana", "label": "Guyana", "count": 1 }, { "value": "Hungary", "label": "Hungary", "count": 1 }, { "value": "India", "label": "India", "count": 11 }, { "value": "Indonesia", "label": "Indonesia", "count": 2 }, { "value": "Iran", "label": "Iran", "count": 1 }, { "value": "Italy", "label": "Italy", "count": 3 }, { "value": "Jamaica", "label": "Jamaica", "count": 1 }, { "value": "Japan", "label": "Japan", "count": 5 }, { "value": "Kazakhstan", "label": "Kazakhstan", "count": 1 }, { "value": "Kenya", "label": "Kenya", "count": 4 }, { "value": "Kiribati", "label": "Kiribati", "count": 1 }, { "value": "Malawi", "label": "Malawi", "count": 2 }, { "value": "Mexico", "label": "Mexico", "count": 1 }, { "value": "Nepal", "label": "Nepal", "count": 1 }, { "value": "Netherlands", "label": "Netherlands", "count": 3 }, { "value": "Nigeria", "label": "Nigeria", "count": 3 }, { "value": "Norway", "label": "Norway", "count": 1 }, { "value": "Oman", "label": "Oman", "count": 1 }, { "value": "Pakistan", "label": "Pakistan", "count": 1 }, { "value": "Peru", "label": "Peru", "count": 2 }, { "value": "Philippines", "label": "Philippines", "count": 2 }, { "value": "Portugal", "label": "Portugal", "count": 1 }, { "value": "Republic of the Congo", "label": "Republic of the Congo", "count": 1 }, { "value": "Romania", "label": "Romania", "count": 1 }, { "value": "Russia", "label": "Russia", "count": 1 }, { "value": "Senegal", "label": "Senegal", "count": 1 }, { "value": "Singapore", "label": "Singapore", "count": 2 }, { "value": "South Africa", "label": "South Africa", "count": 1 }, { "value": "South Korea", "label": "South Korea", "count": 1 }, { "value": "Spain", "label": "Spain", "count": 2 }, { "value": "Switzerland", "label": "Switzerland", "count": 2 }, { "value": "Taiwan", "label": "Taiwan", "count": 2 }, { "value": "Tanzania", "label": "Tanzania", "count": 1 }, { "value": "Thailand", "label": "Thailand", "count": 2 }, { "value": "Togo", "label": "Togo", "count": 1 }, { "value": "Turkey", "label": "Turkey", "count": 5 }, { "value": "Turkmenistan", "label": "Turkmenistan", "count": 1 }, { "value": "Uganda", "label": "Uganda", "count": 1 }, { "value": "Ukraine", "label": "Ukraine", "count": 1 }, { "value": "United Arab Emirates", "label": "United Arab Emirates", "count": 1 }, { "value": "United Kingdom", "label": "United Kingdom", "count": 9 }, { "value": "United States", "label": "United States", "count": 7 }, { "value": "Venezuela", "label": "Venezuela", "count": 1 }, { "value": "Vietnam", "label": "Vietnam", "count": 1 } ] } ], "count": 98, "cases": [ { "case_id": "AIBD-0023", "case_url": "records/AIBD-0023.html", "reason": "The developer is headquartered in United States; the case names Netherlands.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Netherlands" }, { "label": "Case location", "value": "Varsseveld, Gelderland, Netherlands" } ], "filters": { "origin": "United States", "destination": [ "Netherlands" ] }, "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-0042", "case_url": "records/AIBD-0042.html", "reason": "The developer is headquartered in United Kingdom; the case names Argentina, Brazil, Denmark, Germany, India, Italy, Japan, Mexico, Russia, Singapore, South Korea, Spain, United States.", "details": [ { "label": "Developer headquarters", "value": "United Kingdom" }, { "label": "Case jurisdictions", "value": "Argentina, Brazil, Denmark, Germany, India, Italy, Japan, Mexico, Russia, Singapore, South Korea, Spain, United Kingdom, United States" }, { "label": "Case location", "value": "Multicountry trials (ACTT-2, COV-BARRIER, and RECOVERY): Argentina; Brazil; Denmark; Germany; India; Italy; Japan; Mexico; Russia; Singapore; South Korea; Spain; United Kingdom; United States" } ], "filters": { "origin": "United Kingdom", "destination": [ "Argentina", "Brazil", "Denmark", "Germany", "India", "Italy", "Japan", "Mexico", "Russia", "Singapore", "South Korea", "Spain", "United Kingdom", "United States" ] }, "record": { "id": "AIBD-0042", "title": "Baricitinib, identified with BenevolentAI, reduced COVID-19 deaths in trials", "summary": "Researchers used the Benevolent Platform to identify baricitinib as a candidate treatment for COVID-19.", "date_observed": "2021-09-01", "date_basis": "source publication", "date_precision": "day", "benefit_status": "mixed", "source_collection": null, "source_entry": null, "account": "BenevolentAI researchers reported in February 2020 that their knowledge graph had helped them identify baricitinib, an arthritis drug, as a possible treatment for COVID-19. They proposed clinical testing after reviewing its effects on inflammation and proteins involved in viral entry. In ACTT-2, 1,033 hospitalized adults received remdesivir with either baricitinib or a placebo. Median recovery took 7 days with baricitinib and 8 days with the placebo.\n\nCOV-BARRIER enrolled 1,525 adults. By day 28, 8% of patients receiving baricitinib had died, compared with 13% receiving a placebo. Both groups received standard care. Mortality was a secondary outcome. The trial did not meet its primary outcome of reduced disease progression, so its mortality result was not conclusive under the planned procedure for multiple comparisons.\n\nThe subsequent RECOVERY trial enrolled 8,156 patients. Mortality, its primary outcome, was 12% with baricitinib and 14% with usual care alone.", "category": "benefit created", "unexpected": false, "purpose": "Researchers searched for existing drugs that might treat COVID-19, using a biomedical knowledge graph that combines databases with information extracted from scientific papers.", "system": "Benevolent Platform", "model_version": null, "developer": "BenevolentAI", "deployer": "BenevolentAI research team", "location": "Multicountry trials (ACTT-2, COV-BARRIER, and RECOVERY): Argentina; Brazil; Denmark; Germany; India; Italy; Japan; Mexico; Russia; Singapore; South Korea; Spain; United Kingdom; United States", "domain": "Biomedicine / infectious disease therapeutics", "segment": "Hospitalized COVID-19 patients requiring supplemental oxygen or ventilatory support", "benefit_type": "Improved Physical Health", "taxonomy_note": "The clinical benefit is reduced mortality and faster recovery. AI assisted the researchers who proposed the drug for testing.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 5, "reason": "Fewer patients died with baricitinib in COV-BARRIER and RECOVERY. RECOVERY confirmed lower 28-day mortality as its primary outcome in 8,156 patients." }, { "group": "organizational", "depth": 2, "reason": "ACTT-2 reported median recovery of 7 days with baricitinib and 8 days without it. That could free hospital capacity. A separate economic model estimated lower hospital costs, but neither establishes realized staffing or cash savings." }, { "group": "societal", "depth": 4, "reason": "WHO recommended baricitinib with corticosteroids for severe or critical COVID-19. That recommendation supports wider access to a treatment that reduced deaths in trials. Worldwide uptake and population-wide effects were not measured." } ], "ai_role": { "status": "assessed", "statement": "BenevolentAI researchers used the Benevolent Platform to search biomedical relationships and examine networks of proteins. They selected baricitinib after reviewing its targets, dosage, and pharmacology. The clinical investigators then tested the drug. None of these trials compared discovery with AI against discovery without it. The original correspondence and methods paper document AI-assisted selection. FDA authorization and WHO guidance concern the treatment." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Marconi VC, Ramanan AV, de Bono S, et al. (2021). Efficacy and safety of baricitinib for the treatment of hospitalised adults with COVID-19 (COV-BARRIER). Lancet Respir Med. Results and Figure 2; supplementary appendix 6, pp. 9–14, including the prespecified testing hierarchy.", "title": "Efficacy and safety of baricitinib for the treatment of hospitalised adults with COVID-19 (COV-BARRIER): a randomised, double-blind, parallel-group, placebo-controlled phase 3 trial", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "date": "2021-09-01", "written_by": "evaluator", "quote": "The 28-day all-cause mortality was 8% (n=62) for baricitinib and 13% (n=100) for placebo (hazard ratio [HR] 0·57 [95% CI 0·41–0·78]; nominal p=0·0018)" }, { "kind": "peer-reviewed study", "citation": "RECOVERY Collaborative Group. “Baricitinib in Patients Admitted to Hospital with COVID-19 (RECOVERY): A Randomised, Controlled, Open-Label, Platform Trial and Updated Meta-Analysis.” The Lancet 400 (2022): 359–368. Table 2, Figure 3, and Data sharing. https://doi.org/10.1016/S0140-6736(22)01109-6.", "title": "Baricitinib in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial and updated meta-analysis", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/", "date": "2022-07-28", "written_by": "independent", "quote": null }, { "kind": "other", "citation": "Richardson P, Griffin I, Tucker C, Smith D, Oechsle O, Phelan A, Rawling M, Savory E, Stebbing J (2020). Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. Lancet.", "title": "Baricitinib as potential treatment for 2019-nCoV acute respiratory disease", "url": "https://europepmc.org/article/PMC/PMC7137985", "date": "2020-02-04", "written_by": "makers", "quote": "BenevolentAI's knowledge graph is a large repository of structured medical information, including numerous connections extracted from scientific literature by machine learning." }, { "kind": "peer-reviewed study", "citation": "Daniel P. Smith et al. “Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19.” Frontiers in Pharmacology 12 (July 28, 2021): 709856. https://doi.org/10.3389/fphar.2021.709856.", "title": "Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19", "url": "https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.709856/full", "date": "2021-07-28", "written_by": "makers", "quote": null }, { "kind": "peer-reviewed study", "citation": "Kalil AC, Patterson TF, Mehta AK, et al. (2021). Baricitinib plus Remdesivir for Hospitalized Adults with Covid-19. N Engl J Med. Table 2 and trial oversight; supplementary appendix, pp. 19–23; protocol NCT04401579.", "title": "Baricitinib plus Remdesivir for Hospitalized Adults with Covid-19", "url": "https://europepmc.org/article/PMC/PMC7745180", "date": "2020-12-11", "written_by": "independent", "quote": "median time to recovery of 7 days (95% confidence interval [CI], 6 to 8), as compared with 8 days (95% CI, 7 to 9)" }, { "kind": "press release", "citation": "U.S. Food and Drug Administration. “Coronavirus (COVID-19) Update: FDA Authorizes Drug Combination for Treatment of COVID-19.” November 19, 2020. Authorization, evidentiary standard, and ACTT-2 results.", "title": "FDA Authorizes Drug Combination for Treatment of COVID-19", "url": "https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-drug-combination-treatment-covid-19", "date": "2020-11-19", "written_by": "independent", "quote": "the drug baricitinib, in combination with remdesivir, for the treatment of suspected or laboratory confirmed COVID-19" }, { "kind": "report", "citation": "World Health Organization (2022). WHO recommends two new drugs to treat COVID-19.", "title": "WHO recommends two new drugs to treat COVID-19", "url": "https://www.who.int/news/item/14-01-2022-who-recommends-two-new-drugs-to-treat-covid-19", "date": "2022-01-14", "written_by": "independent", "quote": "The first drug, baricitinib, is strongly recommended for patients with severe or critical COVID-19." }, { "kind": "report", "citation": "BenevolentAI. “BenevolentAI Unaudited Preliminary Results for the Year Ended 31 December 2022,” March 16, 2023, Operational highlights, baricitinib bullet.", "title": "BenevolentAI Unaudited Preliminary Results for the Year Ended 31 December 2022", "url": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-unaudited-preliminary-results-year-ended-31-december-2022/", "date": "2023-03-16", "written_by": "makers", "quote": null }, { "kind": "press release", "citation": "BenevolentAI. “BenevolentAI’s platform-derived hypothesis for COVID-19 treatment validated in US NIAID randomised control trial.” September 14, 2020. “About BenevolentAI,” headquarters statement.", "title": "BenevolentAI’s platform-derived hypothesis for COVID-19 treatment validated in US NIAID randomised control trial", "url": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentais-platform-derived-hypothesis-covid-19-treatment-validated-us-niaid-randomised-control-trial/", "date": "2020-09-14", "written_by": "makers", "quote": null }, { "kind": "peer-reviewed study", "citation": "Robert Ohsfeldt et al. “Cost-Effectiveness of Baricitinib Compared With Standard of Care: A Modeling Study in Hospitalized Patients With COVID-19 in the United States.” Clinical Therapeutics 43, no. 11 (2021): 1877–1893.e4. Tables IV–V and supplemental table S6. https://doi.org/10.1016/j.clinthera.2021.09.016.", "title": "Cost-Effectiveness of Baricitinib Compared With Standard of Care: A Modeling Study in Hospitalized Patients With COVID-19 in the United States", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8487786/", "date": "2021-10-04", "written_by": "evaluator", "quote": null } ], "replication": { "produced_by": [ "BenevolentAI researchers combined a biomedical knowledge graph with expert review to select baricitinib.", "Clinical investigators separately tested the drug alongside the care available in 2020 and 2021." ], "must_hold": [ "The clinical comparisons concern hospitalized patients and the eligibility criteria of each trial.", "Background treatment differed: remdesivir in ACTT-2, and mostly corticosteroids in COV-BARRIER and RECOVERY.", "Reproducing the AI selection would also require the January 2020 graph, software configuration, and researchers’ decisions. The complete snapshot is not public." ], "echoes": [ { "date": "2021-09-01", "setting": "COV-BARRIER: 1,525 adults. Lower mortality was a secondary finding after an unsuccessful primary comparison.", "model_version": null, "outcome": "recurred", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/" }, { "date": "2022-07-28", "setting": "RECOVERY: 8,156 patients in 159 U.K. hospitals. Lower 28-day mortality was the primary finding. These were further tests of the drug, not repetitions of the AI discovery process.", "model_version": null, "outcome": "recurred", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/" } ] }, "paired_incident": null, "headline": "An arthritis drug helped patients with severe COVID-19", "metric": "28-day all-cause mortality, a secondary endpoint, was 8% versus 13%", "comparator": { "against": "ACTT-2: remdesivir plus placebo versus remdesivir plus baricitinib", "before": "8 days (95% CI, 7 to 9)", "after": "7 days (95% confidence interval [CI], 6 to 8)", "basis": "measured", "quote": "Patients receiving baricitinib had a median time to recovery of 7 days (95% confidence interval [CI], 6 to 8), as compared with 8 days (95% CI, 7 to 9)", "url": "https://europepmc.org/article/PMC/PMC7745180" }, "later": { "status": "persisted", "date": "2022-07-28", "note": "RECOVERY independently found lower 28-day mortality with baricitinib. WHO had recommended its use with corticosteroids for severe or critical COVID-19 in January 2022. The recommendation does not establish worldwide uptake.", "quote": null, "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/" }, "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": "Paired with AI Incident Database incident 225 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/225", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Recategorised from specific harm prevented to 1 because the sources show an AI-generated repurposing hypothesis confirmed at cohort level by randomized trials rather than a specific harm prevented for an identifiable beneficiary, and, on the fact-checker's other findings, made the ACTT-2 quote verbatim, relabelled the Lancet correspondence letter as “other”, used the exact source titles, replaced the unsupported “hospitals worldwide” with the trial sponsors as deployers, and stated in the summary and account that COV-BARRIER missed its primary endpoint and that its 28-day mortality reduction was a secondary endpoint.", "source": "Richardson P, et al. Lancet 2020 (https://europepmc.org/article/PMC/PMC7137985); Kalil AC, et al. N Engl J Med 2021 (https://europepmc.org/article/PMC/PMC7745180); Marconi VC, et al. Lancet Respir Med 2021 (https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/)", "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/article/PMC/PMC7745180; https://www.who.int/news/item/14-01-2022-who-recommends-two-new-drugs-to-treat-covid-19", "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": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.benevolent.com/contact-us/; https://en.wikipedia.org/wiki/National_Institute_of_Allergy_and_Infectious_Diseases", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions 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://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the AI hypothesis, recovery and mortality findings, and regulatory actions to their original sources. Put COV-BARRIER first for the mortality claim, preserved the failed primary endpoint and added the multiplicity limitation. Removed the generic FDA drug-list page from active evidence because it adds no case-specific support beyond the retained authorization. Distinguished NIAID-led ACTT-2 from the Lilly-sponsored COV-BARRIER analysis.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "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://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "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://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "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.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-unaudited-preliminary-results-year-ended-31-december-2022/", "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://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "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://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Added the developers’ original methods paper, identified the SVO component and January 2020 workflow, and clarified the scientists’ role in selecting baricitinib. The exact model release remains unreported.", "source": "https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.709856/full", "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": "2026-10-25", "change": "Corrected the developer jurisdiction to its contemporaneous U.K. headquarters and replaced the London-only location with the 14 countries in ACTT-2 and COV-BARRIER. Changed the chronology from the FDA authorization date to September 1, 2021, the original publication date of the COV-BARRIER mortality findings. Trial countries do not imply a separately demonstrated benefit in every country.", "source": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentais-platform-derived-hypothesis-covid-19-treatment-validated-us-niaid-randomised-control-trial/", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Rewrote the account and resolved all mandatory fields. Added RECOVERY and the original economic model, corrected the AI operator, distinguished projected wider benefits from observed patient outcomes, replaced WHO guidance counted as replication, and removed an unrelated Watson incident. Missing model and attribution details remain explicit. Reordered the sources by their contribution and remapped their identifiers.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/", "editor": "AIBD, Codex-assisted review" }, { "date": "2026-10-25", "change": "Added historical control from original corporate filings, documented funding and scientific-resource relationships, a labeled strategic assessment, and derived economic scenarios. Preserved the unreported exact model version, with a documented configuration proxy. Distinguished reported findings, calculations, and inferences throughout the assessment.", "source": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "editor": "AIBD, Codex-assisted review" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: two independent peer-reviewed studies, one independent report, one independent press release, the makers' peer-reviewed study, the makers' report, the makers' press release, the makers' other and two peer-reviewed studies from the evaluators. 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These are treatment valuations, not attributed AI savings.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "record_ids": [ "Ohsfeldt-2021-hospital-model", "Ohsfeldt-2021-payer-model" ] }, "institutional decisions": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "FDA-baricitinib-EUA-2020-11-19", "WHO-baricitinib-guidance-2022-01-14" ], "role": "institutional decisions" }, "provenance": { "kind": "source_review", "basis": "Direct FDA and WHO statements establish the dated institutional decisions.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0006", "AIBD-0042-source-0007" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "record_ids": [ "FDA-baricitinib-EUA-2020-11-19", "WHO-baricitinib-guidance-2022-01-14" ] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "structured", "data": { "actors": [ { "name": "BenevolentAI", "roles": [ "AI developer", "AI provider", "AI operator" ] }, { "name": "BenevolentAI research team", "roles": [ "Graph analysis", "Drug selection" ] }, { "name": "NIAID and the ACTT-2 Study Group", "roles": [ "Clinical investigators", "ACTT-2 sponsor" ] }, { "name": "Eli Lilly and Company", "roles": [ "Baricitinib supplier", "COV-BARRIER sponsor", "Economic-model funder" ] }, { "name": "COV-BARRIER investigators", "roles": [ "Clinical investigators" ] }, { "name": "University of Oxford and the RECOVERY Collaborative Group", "roles": [ "Trial sponsor", "Clinical investigators" ] }, { "name": "Gilead Sciences", "roles": [ "Remdesivir supplier for ACTT-2" ] }, { "name": "U.S. Food and Drug Administration", "roles": [ "Regulator" ] }, { "name": "World Health Organization", "roles": [ "Guideline body" ] }, { "name": "Hospitalized patients with COVID-19", "roles": [ "Treatment recipients", "Clinical beneficiaries" ] }, { "name": "Medical Decision Modeling Inc and the economic-study authors", "roles": [ "Economic modelers" ] } ] } }, "provenance": { "kind": "source_review", "basis": "Separated the AI operator from clinical investigators, suppliers, and policy bodies.", "record_fields": [ "developer", "deployer" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0004", "AIBD-0042-source-0009", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "BenevolentAI researchers selected the drug. U.S. government researchers, Lilly, and Oxford organized trials. The FDA authorized treatment, and WHO recommended it." } }, "actors[].ownership_control": { "state": "value_present", "value": { "kind": "structured", "data": { "reported_status": "Historical control is documented in the company’s 2019 accounts.", "entity": "BenevolentAI Limited", "company_number": "09781806", "jurisdiction": "United Kingdom", "as_of": "2019-12-31", "controlling_person": "Kenneth Mulvany, a director and shareholder", "source_basis": "The accounts identify Mulvany as the person who controlled the company.", "aibd_calculated_voting_share": { "person": "Kenneth Patrick Mulvany", "snapshot_date": "2019-10-15", "ordinary_shares_held": 881000, "ordinary_shares_issued": 1831829, "preferred_shares_issued": 208623, "formula": "881,000 ÷ (1,831,829 + 208,623) × 100", "percent": 43.18, "interpretation": "Share of general voting shares in the October register. This is not a measure of all economic equity or proof of majority ownership.", "capital_check": "The December 2019 allotment left the ordinary and preferred counts unchanged. Its new G2 growth shares did not carry ordinary voting rights before an exit, apart from class resolutions.", "confidence": "High for the calculation from the filed snapshot. The snapshot does not establish every beneficial holding on an unobserved January 2020 date." }, "investor_context": { "investor": "Temasek", "jurisdiction": "Singapore", "investment_year": 2019, "reported_amount": "$90 million", "amount_note": "The announcements use the dollar sign without spelling out a currency. The amount is preserved as reported and is not converted or assigned to this research.", "government_relationship": "Temasek’s 2019 report identifies Singapore’s Minister for Finance as its sole shareholder. It also states that Temasek does not direct its portfolio companies’ business decisions.", "control_boundary": "An investment from a state-owned institution does not establish state control of BenevolentAI or direction of the baricitinib research." }, "temporal_scope": "Ownership surrounding the January 2020 discovery, not the developer’s present corporate structure." } }, "provenance": { "kind": "source_review", "basis": "Read original corporate filings and transaction announcements. The controlling person is reported directly; the voting-share percentage is a separate AIBD calculation.", "record_fields": [], "source_refs": [ "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Kenneth Mulvany controlled BenevolentAI at the end of 2019 and held 43.18% of its general voting shares in October.", "editor_note": "The percentage is calculated from his 881,000 shares and the 2,040,452 voting shares in the 15 October 2019 register (reference 12). The December allotment left that total unchanged, but individual holdings could have changed before the January research." } }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "BenevolentAI researchers used the Benevolent Platform to search biomedical relationships and examine networks of proteins. They selected baricitinib after reviewing its targets, dosage, and pharmacology. The clinical investigators then tested the drug. None of these trials compared discovery with AI against discovery without it. The original correspondence and methods paper document AI-assisted selection. FDA authorization and WHO guidance concern the treatment." } } }, "provenance": { "kind": "source_review", "basis": "The authors document AI-assisted candidate selection; no controlled comparison isolates its incremental contribution.", "record_fields": [ "ai_role" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers used the system to find relationships among proteins, diseases, and medicines before selecting baricitinib." } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "BenevolentAI researchers used the Benevolent Platform to search biomedical relationships and examine networks of proteins. They selected baricitinib after reviewing its targets, dosage, and pharmacology. The clinical investigators then tested the drug. None of these trials compared discovery with AI against discovery without it. The original correspondence and methods paper document AI-assisted selection. FDA authorization and WHO guidance concern the treatment." } } }, "provenance": { "kind": "source_review", "basis": "Direct use of AI-assisted discovery preceded separate clinical testing of the proposed drug.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers used AI to investigate a new use for an existing medicine." } }, "assessments[].alternatives": { "state": "value_present", "value": { "kind": "structured", "data": { "personal": "Depth 5 is supported by mortality outcomes. The AI contribution is less certain than the drug benefit.", "organizational": "If only measured organizational gains were admitted, this rating would remain unresolved. The retained depth describes the potential capacity benefit.", "societal": "Depth 4 describes intended wider harm prevention. A recommendation alone does not establish a realized population-wide change." } }, "provenance": { "kind": "source_review", "basis": "Preserved the consequential alternatives rather than averaging different kinds of benefit.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0007", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital and societal grades describe possible benefits. A rule requiring observed benefits would leave those two grades unassigned." } }, "assessments[].assessor_date": { "state": "value_present", "value": { "kind": "structured", "data": { "assessor": "Codex-assisted AIBD review", "reviewed_on": "2026-10-25", "human_approval": "Not represented as an independent human review." } }, "provenance": { "kind": "curatorial", "basis": "Recorded the actual review method without attributing unperformed verification to Peter.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD, assisted by Codex, on 25 October 2026." } }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "Fewer patients died with baricitinib in COV-BARRIER and RECOVERY. RECOVERY confirmed lower 28-day mortality as its primary outcome in 8,156 patients." }, { "group": "organizational", "depth": 2, "reason": "ACTT-2 reported median recovery of 7 days with baricitinib and 8 days without it. That could free hospital capacity. A separate economic model estimated lower hospital costs, but neither establishes realized staffing or cash savings." }, { "group": "societal", "depth": 4, "reason": "WHO recommended baricitinib with corticosteroids for severe or critical COVID-19. That recommendation supports wider access to a treatment that reduced deaths in trials. Worldwide uptake and population-wide effects were not measured." } ] } }, "provenance": { "kind": "source_review", "basis": "Reviewed each stakeholder-specific rationale. Depth describes the stated benefit; support and realization are separate.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0007", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "For patients: 5 for fewer deaths and 2 for faster recovery. The broader assessments remain 2 for organizations and 4 for society." } }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "high", "evidence_word": "studied", "claim_status": "Independently studied", "basis": "Drug effects are supported by randomized trials, including an independent trial. The developers document the AI-assisted selection, but its incremental clinical contribution remains unmeasured.", "interpretation": "The shared solid fill describes the clinical evidence. It does not establish that all organizational or societal effects were measured." } }, "provenance": { "kind": "source_review", "basis": "Recomputed the display from the corrected source types and preserved the distinction between treatment and discovery evidence.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0004", "AIBD-0042-source-0009", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The solid fill denotes independently studied treatment benefits. It does not mean that the projected hospital and societal benefits occurred." } }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "Fewer patients died with baricitinib in COV-BARRIER and RECOVERY. RECOVERY confirmed lower 28-day mortality as its primary outcome in 8,156 patients." }, { "group": "organizational", "depth": 2, "reason": "ACTT-2 reported median recovery of 7 days with baricitinib and 8 days without it. That could free hospital capacity. A separate economic model estimated lower hospital costs, but neither establishes realized staffing or cash savings." }, { "group": "societal", "depth": 4, "reason": "WHO recommended baricitinib with corticosteroids for severe or critical COVID-19. That recommendation supports wider access to a treatment that reduced deaths in trials. Worldwide uptake and population-wide effects were not measured." } ] } }, "provenance": { "kind": "source_review", "basis": "Reviewed each stakeholder-specific rationale. Depth describes the stated benefit; support and realization are separate.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0007", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Lower mortality preserves lives. Faster recovery is a measurable health improvement. Shorter hospital stays could free capacity, and wider access could extend the treatment’s benefits." } }, "assessments[].rubric_version": { "state": "value_present", "value": { "kind": "structured", "data": { "rubric": "AIBD five-level depth scale and three beneficiary groups", "source_file": "aibd/templates/method.html", "source_revision": "4e31895", "applied_on": "2026-10-25", "personal_5": "Life preserved", "organizational_2": "Improved operational capability", "societal_4": "Serious harm averted; wider benefit remains intended rather than measured" } }, "provenance": { "kind": "curatorial", "basis": "Recorded the local rubric used for this review.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD’s five-level scale, applied on 25 October 2026." } }, "assessments[].status_basis": { "state": "value_present", "value": { "kind": "structured", "data": { "personal": "Realized clinical benefit in randomized trials", "organizational": "Potential capacity improvement and modeled costs", "societal": "Observed recommendation, intended wider health benefit" } }, "provenance": { "kind": "source_review", "basis": "Separated realization from the depth of the benefit.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0007", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Trials established a benefit for patients. Hospital savings were modeled, and wider health benefits remain intended." } }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "Fewer patients died with baricitinib in COV-BARRIER and RECOVERY. RECOVERY confirmed lower 28-day mortality as its primary outcome in 8,156 patients." }, { "group": "organizational", "depth": 2, "reason": "ACTT-2 reported median recovery of 7 days with baricitinib and 8 days without it. That could free hospital capacity. A separate economic model estimated lower hospital costs, but neither establishes realized staffing or cash savings." }, { "group": "societal", "depth": 4, "reason": "WHO recommended baricitinib with corticosteroids for severe or critical COVID-19. That recommendation supports wider access to a treatment that reduced deaths in trials. Worldwide uptake and population-wide effects were not measured." } ] } }, "provenance": { "kind": "source_review", "basis": "Reviewed each stakeholder-specific rationale. Depth describes the stated benefit; support and realization are separate.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0007", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Hospitalized patients, hospitals, and the wider population that could receive treatment." } }, "availability": { "state": "value_present", "value": { "kind": "structured", "data": { "historical_ai_access": "BenevolentAI scientists used an internal research platform. The reviewed methods paper does not provide public access to the complete 2020 system.", "public_materials": "Published workflow, source databases, clinical articles, and linked trial materials.", "procurement_price": "Not reported for the discovery system.", "current_access": "Current licensing, geographic availability, and access to the historical snapshot are unresolved.", "treatment": "Baricitinib is a separate prescription medicine; the cited FDA and WHO statements describe historical authorization and guidance, not current individualized treatment advice.", "reviewed_on": "2026-10-25" } }, "provenance": { "kind": "source_review", "basis": "Separated access to the AI workflow from access to the drug.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "BenevolentAI used its internal platform. Its methods are published, but the complete January 2020 system is unavailable. Baricitinib is a prescription medicine." } }, "brief": { "state": "value_present", "value": { "kind": "text", "text": "Researchers used the Benevolent Platform to identify baricitinib for COVID-19 testing. Randomized trials later found faster recovery or fewer deaths among hospitalized patients. The trials tested the drug rather than the additional value of AI-assisted discovery." }, "provenance": { "kind": "source_review", "basis": "The summary draws on the original discovery and clinical publications.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers used AI to identify baricitinib for COVID-19 trials. Clinical investigators later found that patients recovered sooner and fewer died." } }, "case_kind": { "state": "value_present", "value": { "kind": "structured", "data": { "unit": "AI-assisted drug repurposing with subsequent clinical evaluations", "counting": "One discovery episode. The trials and articles are linked evidence, not additional AIBD cases." } }, "provenance": { "kind": "source_review", "basis": "Assigned the unit to the documented discovery episode and its follow-up.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Repurposing a medicine for COVID-19." } }, "case_links[]": { "state": "value_present", "value": { "kind": "structured", "data": { "related_evaluations": [ "NCT04401579", "NCT04421027", "NCT04381936" ], "same_discovery": "The three trials concern the drug selected in the January 2020 BenevolentAI work.", "duplicate_search": "No other existing AIBD record mentions baricitinib." } }, "provenance": { "kind": "curatorial", "basis": "Reviewed the local collection for duplicates and linked the original trial registrations.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "ACTT-2, COV-BARRIER, and RECOVERY tested baricitinib after the initial proposal." } }, "claims[].evidence_links": { "state": "value_present", "value": { "kind": "structured", "data": { "links": [ { "claim": "AIBD-0042-claim-discovery", "support": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "locator": "Original correspondence; Methods and Results of the methods paper" }, { "claim": "AIBD-0042-claim-recovery", "support": [ "AIBD-0042-source-0005" ], "locator": "Table 2" }, { "claim": "AIBD-0042-claim-mortality", "support": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002" ], "locator": "COV-BARRIER Figure 2 and statistical hierarchy; RECOVERY Table 2" }, { "claim": "AIBD-0042-claim-policy", "support": [ "AIBD-0042-source-0006", "AIBD-0042-source-0007" ], "locator": "Dated agency statements" }, { "claim": "AIBD-0042-claim-economics", "support": [ "AIBD-0042-source-0010" ], "locator": "Tables IV–V and supplemental table S6" } ], "qualifications": [ { "claim": "AIBD-0042-claim-mortality", "source": "AIBD-0042-source-0001", "qualification": "The COV-BARRIER primary outcome was not statistically significant; mortality was secondary." }, { "claim": "AIBD-0042-claim-economics", "source": "AIBD-0042-source-0010", "qualification": "Hospital savings reverse to higher costs in the mortality-only scenario." } ] } }, "provenance": { "kind": "source_review", "basis": "Mapped the statements to original findings and their material qualifications.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0004", "AIBD-0042-source-0009", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "References 3–4 describe the discovery, 5 reports recovery, 1–2 report mortality, 6–7 record agency decisions, and 10 estimates costs." } }, "claims[].evidence_strength": { "state": "value_present", "value": { "kind": "structured", "data": { "clinical_outcomes": "Randomized trials support a drug benefit; RECOVERY independently confirms lower mortality.", "ai_use": "Original correspondence and detailed developer methods establish the reported workflow.", "ai_increment": "No controlled estimate of the benefit added by using AI.", "institutional_actions": "Direct agency statements establish the decisions and dates.", "wider_impact": "Hospital capacity and population-wide benefits were not measured. Economic effects are modeled." } }, "provenance": { "kind": "source_review", "basis": "Assessed each causal link separately.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0004", "AIBD-0042-source-0009", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Randomized trials establish the treatment benefit. The developers describe the AI workflow. No trial compares discovery with and without AI." } }, "claims[].missingness": { "state": "value_present", "value": { "kind": "structured", "data": { "model_release": "Not reported", "frozen_discovery_snapshot": "Not reported", "incremental_ai_benefit": "No controlled numerical uplift is reported. AIBD supplies a qualitative inference and documented workflow proxies.", "realized_hospital_savings": "Not reported", "worldwide_beneficiary_count": "Not reported", "control_of_developer_in_2020": "Historical control is established by the 2019 accounts. The separate voting-share calculation uses the October 2019 snapshot.", "cross_border_transfers": "Company funding is documented. Scientific-resource reuse is documented, with geographic links labeled as inferences. No patient-data route or case-level funding allocation is reported.", "inaccessible_upstream_materials": "See source access notes for the Sorrell supplement and the original WHO guideline PDF.", "return_payback": "No source-reported ROI or payment timetable. AIBD supplies labeled value scenarios and a reference ratio.", "strategic_effect": "AIBD provides a qualitative preparedness assessment. No quantitative national advantage is reported or invented." } }, "provenance": { "kind": "source_review", "basis": "Recorded consequential gaps without substituting zero or assuming that missing evidence disproves benefit.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0009", "AIBD-0042-source-0010", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949", "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The publications omit the exact software release, a comparison without AI, observed savings, and the number of people helped worldwide." } }, "claims[].proposition": { "state": "value_present", "value": { "kind": "structured", "data": { "claims": [ { "id": "AIBD-0042-claim-discovery", "statement": "BenevolentAI researchers used their graph to identify baricitinib for COVID-19 testing.", "type": "Original researchers’ account", "sources": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "locator": "Original correspondence; Methods and Results of the methods paper" }, { "id": "AIBD-0042-claim-recovery", "statement": "ACTT-2 found faster recovery with baricitinib added to remdesivir.", "type": "Randomized clinical finding", "sources": [ "AIBD-0042-source-0005" ], "locator": "Table 2" }, { "id": "AIBD-0042-claim-mortality", "statement": "COV-BARRIER and RECOVERY found lower mortality with baricitinib.", "type": "Randomized clinical findings with different primary outcomes", "sources": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002" ], "locator": "COV-BARRIER Figure 2 and statistical hierarchy; RECOVERY Table 2" }, { "id": "AIBD-0042-claim-policy", "statement": "FDA authorized use in November 2020 and WHO recommended use in January 2022.", "type": "Documented institutional decisions", "sources": [ "AIBD-0042-source-0006", "AIBD-0042-source-0007" ], "locator": "Dated agency statements" }, { "id": "AIBD-0042-claim-economics", "statement": "A published model estimated treatment costs and health gains.", "type": "Sponsor-funded model, not observed AI savings", "sources": [ "AIBD-0042-source-0010" ], "locator": "Tables IV–V and supplemental table S6" } ] } }, "provenance": { "kind": "source_review", "basis": "Separated discovery, clinical, institutional, and modeled economic statements.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0004", "AIBD-0042-source-0009", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers used AI to select baricitinib for testing. Clinical investigators subsequently found that patients recovered faster and fewer died." } }, "cross_border_flows[]": { "state": "value_present", "value": { "kind": "structured", "data": { "reported_status": "The funding relationship and reuse of named scientific resources are documented. Geographic knowledge flows are inferred from the resource producers and the U.K. discovery team.", "flows": [ { "type": "Investment", "origin": "Singapore", "destination": "United Kingdom", "from_actor": "Temasek", "to_actor": "BenevolentAI", "year": 2019, "reported_amount": "$90 million", "evidence_status": "Reported by the recipient and its transaction counsel", "scope": "Company-wide development of the platform and drug programs. The sources do not identify a baricitinib budget or the bank settlement route.", "source_refs": [ "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek" ] }, { "type": "Reuse of scientific information", "origin": "Japan", "destination": "United Kingdom", "resource": "KEGG", "evidence_status": "Documented use; geographic relationship inferred", "basis": "The methods paper names KEGG. Its developers trace the resource to the Japanese Human Genome Project.", "source_refs": [ "AIBD-0042-source-0004", "https://www.genome.jp/kegg/kegg1.html" ] }, { "type": "Reuse of scientific information", "origin": "Italy", "destination": "United Kingdom", "resource": "SIGNOR", "evidence_status": "Documented use; geographic relationship inferred", "basis": "The methods paper names SIGNOR. Its resource paper identifies an Italy-led collaboration that also includes U.K. researchers.", "source_refs": [ "AIBD-0042-source-0004", "https://doi.org/10.1093/nar/gkz949" ] }, { "type": "Published research followed by clinical testing", "origin": "United Kingdom", "destination": "U.S.-led ACTT-2 and the countries in the multinational COV-BARRIER trial", "evidence_status": "Documented sequence in the discovery and trial publications", "scope": "The treatment hypothesis crossed research settings. This does not mean that the original AI system was deployed at the trial sites.", "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0001" ] } ], "boundary": "Resource origins do not establish server locations, network routes, or transfers of patient data. The funding amount belongs to the company, not to this individual benefit.", "confidence": "High for the named funding relationship and resource use. Moderate for the inferred geographic relationships." } }, "provenance": { "kind": "source_review", "basis": "Combined direct transaction evidence, original resource histories, and the discovery methods. Kept documented relationships separate from inferred geographic links.", "record_fields": [], "source_refs": [ "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "AIBD-0042-source-0004", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949", "AIBD-0042-source-0003", "AIBD-0042-source-0005", "AIBD-0042-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Singapore’s Temasek invested in BenevolentAI. The U.K. researchers used Japanese and Italian scientific resources before trials in several countries.", "editor_note": "The geographic links follow the producers of KEGG and SIGNOR, which Smith and colleagues name in reference 4. They identify international knowledge reuse, not the route of a data transfer. Temasek funded the company as a whole." } }, "decisions[].action_outcome": { "state": "value_present", "value": { "kind": "structured", "data": { "FDA": { "date": "2020-11-19", "action": "Issued the EUA to Eli Lilly for baricitinib with remdesivir in eligible hospitalized adults and children aged two or older requiring specified respiratory support." }, "WHO": { "date": "2022-01-14", "action": "Strongly recommended baricitinib with corticosteroids for severe or critical COVID-19." }, "later_evidence": "RECOVERY later confirmed lower 28-day mortality. These sources do not quantify adoption caused by the decisions." } }, "provenance": { "kind": "source_review", "basis": "Separated the decisions, later clinical evidence, and unmeasured implementation effects.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The FDA authorized baricitinib with remdesivir on 19 November 2020. WHO recommended it with corticosteroids on 14 January 2022." } }, "decisions[].grounds": { "state": "value_present", "value": { "kind": "structured", "data": { "FDA": "ACTT-2 results and the emergency-use standard: reasonable belief that the combination may be effective, with known and potential benefits outweighing known and potential risks for the authorized population. An EUA differs from full approval.", "WHO": "Guideline review of clinical-trial evidence. WHO recommended baricitinib with corticosteroids and identified access and affordability as conditions for wider benefit.", "AI_scope": "Neither decision assessed the incremental value of the AI discovery method." } }, "provenance": { "kind": "source_review", "basis": "The FDA announcement states its evidentiary standard; WHO states the recommendation and access limitations.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0006", "AIBD-0042-source-0007" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The FDA relied on ACTT-2. WHO weighed the clinical evidence, harms, affordability, and access." } }, "decisions[].institution_task": { "state": "value_present", "value": { "kind": "structured", "data": { "decisions": [ { "id": "FDA-baricitinib-EUA-2020-11-19", "institution": "U.S. Food and Drug Administration", "jurisdiction": "United States", "task": "Whether to authorize emergency use of baricitinib with remdesivir for specified hospitalized patients with COVID-19." }, { "id": "WHO-baricitinib-guidance-2022-01-14", "institution": "World Health Organization", "scope": "International clinical guidance", "task": "Whether to recommend baricitinib for severe or critical COVID-19." } ] } }, "provenance": { "kind": "source_review", "basis": "Read the original dated agency announcements.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0006", "AIBD-0042-source-0007" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The FDA considered emergency authorization. WHO considered treatment guidance for severe or critical COVID-19." } }, "decisions[].success_criterion": { "state": "value_present", "value": { "kind": "structured", "data": { "clinical_question": "Whether adding baricitinib improves recovery or survival with acceptable harms compared with the same background care.", "adoption_question": "Whether eligible patients can obtain treatment and whether clinical benefits persist in routine practice.", "unresolved": "The cited agency statements do not specify a numerical global uptake target or measure the effects caused by adopting their guidance." } }, "provenance": { "kind": "source_review", "basis": "The clinical questions follow the source outcomes; implementation remains a separate unanswered question.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Both agencies sought better survival or recovery with acceptable harms. They did not quantify the additional use or health improvement that their decisions caused." } }, "deployment_stage": { "state": "value_present", "value": { "kind": "structured", "data": { "ai": "Used by BenevolentAI researchers in January 2020", "treatment": "Evaluated in randomized clinical trials and subsequently authorized or recommended", "scope": "The AI application and the drug have different deployment stages." } }, "provenance": { "kind": "source_review", "basis": "The methods paper documents actual use of the graph; clinical and policy sources concern the resulting treatment.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers used the AI system in January 2020. Clinical trials and treatment authorization followed." } }, "economics[].amount": { "state": "value_present", "value": { "kind": "structured", "data": { "hospital_base_case": { "incremental_expenditure_usd": -2436, "incremental_reimbursement_usd": -503, "incremental_net_cost_usd": -1932 }, "hospital_mortality_only": { "incremental_expenditure_usd": 1978, "incremental_reimbursement_usd": 0, "incremental_net_cost_usd": 1978 }, "payer_base_case": { "placebo_soc_total_usd": 329268, "baricitinib_soc_total_usd": 346544, "incremental_total_usd": 17276, "incremental_qalys": 0.6703, "usd_per_qaly_gained": 25774 }, "payer_mortality_only": { "incremental_total_usd": 17902, "incremental_qalys": 0.6664, "usd_per_qaly_gained": 26862 }, "rounding": "The published rounded components do not subtract exactly to the reported net amount of $1,932." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Table IV; appendix Tables S5–S6.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The model estimates US$1,932 less in net hospital costs per patient. Over a lifetime, costs rise US$17,276 and health improves by 0.6703 quality-adjusted life-years." } }, "economics[].costs[]": { "state": "value_present", "value": { "kind": "structured", "data": { "included": [ "Drug acquisition", "Medical care during admission", "Hospital reimbursement for inpatient care", "Postacute care and lifetime healthcare in the payer model", "Missed work in the payer model" ], "unit_inputs": { "baricitinib_2mg_tablet_usd": 75.5, "baricitinib_assumed_dose": "4 mg daily for up to 14 days or discharge", "hospital_day_mechanical_ventilation_usd": 3660, "hospital_day_noninvasive_ventilation_usd": 2450, "hospital_day_supplemental_oxygen_usd": 1828, "hospital_day_no_oxygen_usd": 1818 }, "not_reported": "The sources do not quantify this discovery project’s AI development, data licensing, compute, integration, maintenance, or human review costs." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Costs; Table III; Dosing and administration.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The model includes medicines, hospital care, later healthcare, and missed work. It excludes the costs of developing and operating the AI system." } }, "economics[].counterfactual": { "state": "value_present", "value": { "kind": "structured", "data": { "comparison": "Baricitinib plus standard care versus placebo plus the same standard care in the modeled COV-BARRIER population.", "base_case": "Uses numerical differences in mortality, recovery, and respiratory-support outcomes.", "mortality_only_scenario": "Limits treatment benefit to mortality reduction. Hospital costs rise rather than fall.", "AI_counterfactual": "No matched AI-versus-no-AI discovery scenario." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Model inputs; Analyses; Secondary Analyses.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The researchers compared baricitinib plus standard care with placebo plus the same care. Assuming only lower mortality reverses hospital savings to US$1,978 in additional costs per patient." } }, "economics[].currency_price_basis": { "state": "value_present", "value": { "kind": "structured", "data": { "currency": "USD", "jurisdiction": "United States", "price_basis": "The model uses U.S. dollar cost and payment inputs from 2020–2021. Some older care costs and future healthcare costs were inflated to 2020 USD; home-health and hospice reimbursement inputs use 2021 rates. The article does not establish that every input shares one uniform price year.", "conversion": "No currency conversion was used for these source figures." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Costs section, Table III, appendix input tables.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "U.S. dollars, with inputs from 2020–2021. Some costs were adjusted to 2020 prices, but the inputs do not share one price year." } }, "economics[].gross_net": { "state": "value_present", "value": { "kind": "structured", "data": { "hospital": "Net costs mean expenditures minus reimbursement. Base case: expenditure falls $2,436 and reimbursement falls $503, yielding a reported $1,932 net cost reduction.", "payer": "Incremental total costs include inpatient, postacute, future all-cause healthcare, and indirect costs. More survivors accumulate additional lifetime medical costs.", "exclusions": "No AI development or operating costs, long-COVID burden, COVID-related readmissions, or measured value from using freed beds for other patients." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Costs; Discussion limitations; appendix Tables S5–S6.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Hospital costs are net of reimbursement. Lifetime costs include healthcare and missed work. The model excludes long COVID, COVID-related readmissions, and the value of freed beds." } }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "structured", "data": { "hospital": "Modeled change in hospital expenditure and net costs after reimbursement.", "payer": "Modeled incremental lifetime healthcare and indirect costs, and cost per quality-adjusted life-year.", "AI_boundary": "The model evaluates the treatment that researchers identified with AI assistance. It does not value AI separately." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Population and Perspective, Secondary Analyses, Table IV and supplementary Tables S5–S6.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Estimated hospital savings and the lifetime cost of additional health from treatment with baricitinib. The study assigns no separate monetary value to AI." } }, "economics[].period_unit": { "state": "value_present", "value": { "kind": "structured", "data": { "hospital": "Per modeled hospital admission, over the inpatient stay.", "payer": "Per modeled patient over a lifetime horizon.", "recurrence": "These are modeled per-patient differences, not recurring annual cash flows or a verified total across patients." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Population and Perspective; Model Framework.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Hospital costs cover one admission per patient. Costs to insurers cover each patient’s remaining lifetime." } }, "economics[].realization": { "state": "value_present", "value": { "kind": "structured", "data": { "state": "Modeled estimate", "basis": "Economic simulation using clinical trial effects and administrative cost inputs; no observed hospital ledger or cash saving was reported." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Methods and Secondary Analyses.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The amounts come from an economic model, without observed cash savings." } }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "structured", "data": { "hospital_perspective": "Hospitals bear treatment costs and receive reimbursements. The estimated net saving reflects both components.", "payer_perspective": "Medicare, Medicaid, and private payers; the modeled mix is 52.9%, 19.2%, and 27.9%, respectively. The article also includes missed-work costs in its payer perspective.", "patients": "Clinical gains accrue to patients. The model excludes uninsured patients and does not establish individual out-of-pocket savings.", "AI_provider": "No payment, royalty, revenue, or profit to BenevolentAI is quantified by this analysis." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Population and Perspective; Table III; Costs.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Patients gain health. Hospitals pay for treatment and receive reimbursement from Medicare, Medicaid, and private insurers." } }, "economics[].return_payback": { "state": "value_present", "value": { "kind": "structured", "data": { "reported_status": "The source reports treatment costs and cost-effectiveness ratios, not a financial ROI, AI-project return, or exact payback date.", "aibd_calculated_health_value": { "name": "Incremental net monetary benefit", "perspective": "U.S. payer, per modeled patient over a lifetime", "formula": "Assumed USD value per quality-adjusted life-year × additional quality-adjusted life-years − additional lifetime costs", "source_inputs": { "base_additional_qalys": 0.6703, "base_additional_cost_usd": 17276, "mortality_only_additional_qalys": 0.6664, "mortality_only_additional_cost_usd": 17902 }, "scenarios": [ { "usd_per_qaly": 25000, "base_net_value_usd": -518.5, "mortality_only_net_value_usd": -1242, "assumption": "AIBD sensitivity scenario" }, { "usd_per_qaly": 50000, "base_net_value_usd": 16239, "mortality_only_net_value_usd": 15418, "assumption": "Threshold used in the original study" }, { "usd_per_qaly": 100000, "base_net_value_usd": 49754, "mortality_only_net_value_usd": 48738, "assumption": "AIBD sensitivity scenario" } ], "interpretation": "A valuation of additional health after additional costs, not a cash receipt or an estimate of AI’s separate contribution." }, "aibd_calculated_drug_cost_reference": { "formula": "$75.50 per 2-mg tablet × two tablets per day × 14 days", "full_course_reference_usd": 2114, "base_modeled_hospital_net_benefit_usd": 1932, "base_benefit_as_percent_of_reference": 91.4, "mortality_only_modeled_hospital_net_benefit_usd": -1978, "mortality_only_benefit_as_percent_of_reference": -93.6, "interpretation": "The model’s net hospital benefit divided by a full-course acquisition-cost benchmark. This is a reference ratio, not the model’s actual mean-cost ROI. Treatment could end earlier, dosing could differ, and acquisition prices could differ." }, "payback_assessment": { "accounting_horizon": "The modeled hospital admission", "base_case": "The model ends with lower net hospital costs.", "mortality_only_scenario": "The model ends with higher net hospital costs.", "cash_timing_status": "Purchase, payment, and savings dates are not reported, so an exact cash-payback day is not calculable from these totals." }, "estimation_confidence": "Arithmetic is reproducible from the published inputs. Economic conclusions depend on the model, valuation threshold, and clinical-effect assumptions." } }, "provenance": { "kind": "derivation", "basis": "Calculated the scenario values and reference ratios directly from the published economic model and appendix, with no invented AI attribution or cash-flow schedule.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "At US$50,000 per quality-adjusted life-year, AIBD values the additional health at US$16,239 per patient after costs: (50,000 × 0.6703) − 17,276. Cash returns and payment dates were not reported." } }, "economics[].valuation_method": { "state": "value_present", "value": { "kind": "structured", "data": { "method": "Linked inpatient, discharged, and recovered submodels based on COV-BARRIER effects, with duration of respiratory support rescaled using ACTT-2 data and U.S. administrative cost inputs. Hospital costs are days at each support level multiplied by daily cost, less reimbursement for net cost.", "discount_rate_per_year": 0.03, "sensitivity": "One-way and probabilistic sensitivity analyses (5,000 replications) for the payer model; a mortality-only scenario for both perspectives.", "payer_probabilistic_estimate": "Mean incremental cost $17,373 (95% interval −$3,300 to $38,306) and 0.674 QALYs (95% interval −0.096 to 1.441). These are model uncertainty results, not intervals from observed cash savings.", "source_dependency": "Some COV-BARRIER and Premier Healthcare Database inputs are data on file with Lilly and are not independently recoverable from the publication.", "funder": "Eli Lilly and Company.", "aibd_additional_calculations": "The return/payback field gives labeled net monetary benefit scenarios and a drug-cost reference ratio. These use the published model outputs; they are not new observations." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Model Framework, inputs, sensitivity analyses, Funding and appendix.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0010", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The researchers combined trial results with U.S. costs and applied a 3% annual discount rate. Hospital savings depend on clinical differences that were not all statistically conclusive." } }, "evaluations[].ai_uplift": { "state": "value_present", "value": { "kind": "structured", "data": { "reported_status": "No controlled estimate of additional value from AI is reported.", "aibd_inference": "Automated extraction and graph exploration probably reduced the manual work needed to assemble and examine relevant biomedical relationships. The published workflow supports this inference, but does not establish how much time was saved or whether the same drug would have been selected without AI.", "inference_confidence": "Moderate for assistance with information retrieval and prioritization. A numerical contribution to clinical benefit is not identifiable from these studies.", "documented_workflow_proxies": { "approximately_added_relationships": 40000, "proteins_in_initial_network": 556, "approved_AAK1_inhibitors_examined": 47, "high_affinity_AAK1_candidates": 6, "selected_drug": "Baricitinib", "iteration_timescale": "The developers describe iterations within hourly timeframes." }, "estimation_basis": "The original correspondence and methods paper report these workflow quantities. They measure the scale of the work, not an improvement against a human-only control.", "attribution_boundary": "No share of the clinical or economic result is assigned to AI." } }, "provenance": { "kind": "derivation", "basis": "Derived a qualitative contribution assessment from the documented mechanism and process counts. No causal percentage or time saving was invented.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0005", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The software probably reduced the work needed to assemble and examine relevant biomedical findings.", "editor_note": "Smith and colleagues report about 40,000 added relationships and a starting network of 556 proteins (reference 4). These quantities support an inference of substantial assistance, but the authors did not time a comparable search without AI." } }, "evaluations[].comparator": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": "Baricitinib plus remdesivir versus placebo plus remdesivir, alongside supportive care.", "COV-BARRIER": "Baricitinib plus standard care versus placebo plus standard care; 79.3% received systemic corticosteroids at baseline and 18.9% remdesivir.", "RECOVERY": "Baricitinib plus usual care versus usual care alone. At baseline 95% received corticosteroids and 23% tocilizumab.", "economic_model": "Baricitinib plus standard care versus placebo plus standard care, using the COV-BARRIER population and treatment estimates.", "AI_comparison": "None of these trials randomized use of AI in the drug-discovery process." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Methods and baseline characteristics.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Patients received baricitinib or no baricitinib alongside the same background care. ACTT-2 gave remdesivir to both groups." } }, "evaluations[].design": { "state": "value_present", "value": { "kind": "structured", "data": { "evaluations": [ { "id": "ACTT-2", "design": "Randomized, double-blind, placebo-controlled trial." }, { "id": "COV-BARRIER", "design": "Randomized, double-blind, placebo-controlled phase 3 trial." }, { "id": "RECOVERY-baricitinib", "design": "Randomized, open-label, controlled platform trial." }, { "id": "Ohsfeldt-2021-economic-model", "design": "Simulation using clinical trial estimates, U.S. hospital cost data, published price and utility inputs, and sensitivity analyses." } ] } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Methods.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Investigators randomly assigned treatment in all three trials. ACTT-2 and COV-BARRIER concealed assignments, while RECOVERY disclosed them. The economic study simulated costs and health." } }, "evaluations[].effect": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": { "recovery_rate_ratio": 1.16, "confidence_interval_95": [ 1.01, 1.32 ], "p": 0.03, "median_difference_days": -1, "mortality_hazard_ratio": 0.65, "mortality_confidence_interval_95": [ 0.39, 1.09 ], "mortality_conclusion": "Not statistically conclusive." }, "COV-BARRIER": { "primary_odds_ratio": 0.85, "primary_confidence_interval_95": [ 0.67, 1.08 ], "primary_p": 0.18, "primary_absolute_difference_percentage_points": -2.7, "mortality_hazard_ratio": 0.57, "mortality_confidence_interval_95": [ 0.41, 0.78 ], "mortality_nominal_p": 0.0018, "reported_mortality_absolute_difference_percentage_points": -5.0, "limitation": "Mortality did not meet multiplicity-controlled significance because the preceding primary endpoint failed." }, "RECOVERY": { "age_adjusted_mortality_rate_ratio": 0.87, "confidence_interval_95": [ 0.77, 0.99 ], "p": 0.028, "raw_rates": "514/4148 versus 546/4008; the paper rounds these to 12% and 14%.", "caution": "Do not call the difference in death counts (32) the number of lives saved, because arm sizes differ." }, "economic_model": { "hospital_base_net_cost_difference_usd": -1932, "hospital_mortality_only_cost_difference_usd": 1978, "lifetime_payer_base_cost_difference_usd": 17276 } } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at ACTT-2 Table 2; COV-BARRIER Table 2; RECOVERY Table 2; economic model Table IV, appendix Tables S5–S6.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Median recovery was 7 rather than 8 days in ACTT-2. Deaths by day 28 were 8% versus 13% in COV-BARRIER and 12% versus 14% in RECOVERY." } }, "evaluations[].exposure": { "state": "value_present", "value": { "kind": "structured", "data": { "basis": "Randomized participants are the matched denominators. Treatment-arm sizes are not counts of people proven to have benefited.", "ACTT-2": { "baricitinib_assigned": 515, "control": 518 }, "COV-BARRIER": { "baricitinib_assigned": 764, "control": 761, "safety_analysis_population": 1502 }, "RECOVERY": { "baricitinib_assigned": 4148, "usual_care": 4008 }, "economic_model": "Costs are per modeled eligible adult admission or patient lifetime, according to perspective; no observed number of hospital admissions generating the stated savings." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Participant flow, intention-to-treat populations, model population and perspective.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "ACTT-2 assigned 515 patients to baricitinib and 518 to control. COV-BARRIER assigned 764 and 761. RECOVERY assigned 4,148 and 4,008." } }, "evaluations[].id": { "state": "value_present", "value": { "kind": "structured", "data": { "evaluations": [ { "id": "ACTT-2", "registry": "NCT04401579" }, { "id": "COV-BARRIER", "registry": "NCT04421027" }, { "id": "RECOVERY-baricitinib", "registry": [ "NCT04381936", "ISRCTN50189673" ] }, { "id": "Ohsfeldt-2021-economic-model", "doi": "10.1016/j.clinthera.2021.09.016" } ] } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Methods and article identifiers.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "ACTT-2, COV-BARRIER, RECOVERY, and Ohsfeldt and colleagues’ 2021 economic study." } }, "evaluations[].measurement_method": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": "Clinical status on an eight-category ordinal scale; recovery was the first day in categories 1, 2, or 3 within 28 days. Trial staff collected clinical outcomes.", "COV-BARRIER": "Clinical progression on the NIAID ordinal scale, recorded mortality, and adjudicated safety events; study visits through day 28 and mortality assessment through day 60.", "RECOVERY": "Site staff completed follow-up forms at discharge, death, or day 28. Researchers also linked routinely collected healthcare and registry data.", "economic_model": "Three linked submodels covered inpatient care, discharge, and recovery. Model results are estimates rather than transactions or measured savings." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at ACTT-2 Outcomes; COV-BARRIER Procedures; RECOVERY Procedures; economics Model Framework.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Investigators used clinical assessments, follow-up forms, and health records. The economists combined trial findings with treatment prices and costs of care." } }, "evaluations[].metric": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": { "metric": "Time to recovery", "role": "Primary outcome", "period_days": 28, "baricitinib_median_days": 7, "control_median_days": 8 }, "COV-BARRIER": { "primary": "Progression to high-flow oxygen, noninvasive ventilation, invasive ventilation, ECMO, or death by day 28.", "primary_values_percent": [ 27.8, 30.5 ], "mortality_role": "Prespecified key secondary outcome", "mortality_days": 28, "baricitinib_deaths": 62, "baricitinib_randomized": 764, "control_deaths": 100, "control_randomized": 761 }, "RECOVERY": { "metric": "All-cause mortality", "role": "Primary outcome", "period_days": 28, "baricitinib_deaths": 514, "baricitinib_randomized": 4148, "control_deaths": 546, "control_randomized": 4008 }, "economic_model": { "metrics": [ "Incremental hospital expenditure", "Incremental reimbursement", "Incremental net hospital costs", "Incremental lifetime payer costs", "Quality-adjusted life-years", "Cost per quality-adjusted life-year" ] } } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Main outcomes and results tables; economic model Table IV and supplementary Table S6.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The primary outcomes were recovery time in ACTT-2, disease progression in COV-BARRIER, and deaths within 28 days in RECOVERY. The economists estimated costs and quality-adjusted life-years." } }, "evaluations[].reproduction_materials": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": "NCT04401579; the publication links its protocol, supplementary appendix, disclosures, and data-sharing statement.", "COV-BARRIER": "NCT04421027; supplementary appendix 6 contains protocol details, the statistical plan, and additional outcomes.", "RECOVERY": "NCT04381936 and ISRCTN50189673; the article links the protocol/statistical plan and supplementary appendix; https://www.recoverytrial.net/ supplies trial documents.", "economic_model": "Published methods and an original DOCX appendix provide equations, inputs, and result Tables S1–S6. Some trial and hospital-cost inputs remain proprietary data on file. The full executable economic model and its underlying individual records are not supplied in the reviewed materials.", "AI_system": "The developers publish workflow methods, but not the full proprietary graph, a frozen January 2020 checkpoint, or an executable end-to-end reconstruction." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Supplementary material links, methods and data-sharing sections.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0004", "AIBD-0042-source-0010", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Trial registrations, protocols, analysis plans, and appendices are public. The complete historical AI system and economic model are unavailable, and some cost inputs are unpublished." } }, "evaluations[].setting_sample": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": { "randomized": 1033, "sites": 67, "countries": [ "United States", "Singapore", "South Korea", "Mexico", "Japan", "Spain", "United Kingdom", "Denmark" ], "enrollment_start": "2020-05-08", "enrollment_end": "2020-07-01", "population": "Hospitalized adults with COVID-19." }, "COV-BARRIER": { "randomized": 1525, "sites": 101, "countries_count": 12, "enrollment_start": "2020-06-11", "enrollment_end": "2021-01-15", "population": "Hospitalized adults with COVID-19 and an elevated inflammatory marker; patients already receiving invasive mechanical ventilation were excluded from the main trial." }, "RECOVERY": { "randomized": 8156, "country": "United Kingdom", "enrollment_start": "2021-02-02", "enrollment_end": "2021-12-29", "population": "Hospitalized patients with suspected or confirmed COVID-19; age at least two years under the protocol, with 33 children in the comparison." }, "economic_model": { "country": "United States", "population": "A modeled adult population based on COV-BARRIER; inpatient cost inputs from the Premier Healthcare Database, April 1–September 15, 2020. Uninsured patients excluded." } } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Study design, participants, Results, and economic model Population/Costs.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The trials enrolled hospitalized patients in 2020–2021: 1,033 in ACTT-2, 1,525 in COV-BARRIER, and 8,156 in RECOVERY. The economic study modeled insured U.S. adults." } }, "evaluations[].uncertainty_limitations": { "state": "value_present", "value": { "kind": "structured", "data": { "AI": "No controlled estimate of the AI workflow’s additional contribution; experts chose the candidate, and trials tested the medicine.", "ACTT-2": "Mortality was not the primary endpoint and its confidence interval included no effect. The trial occurred before corticosteroids became routine for these patients.", "COV-BARRIER": "The primary progression endpoint failed, leaving secondary mortality p values nominal under the prespecified hierarchy. Lilly sponsored, designed, and analyzed the trial.", "RECOVERY": "Open-label treatment, an age imbalance addressed in analysis, and a smaller mortality effect than earlier trials. Outcome data were available for more than 99% of randomized patients.", "economic_model": "Lilly funded the study. Base-case hospital savings depend on numerical clinical differences that were not all statistically conclusive; savings reverse in the mortality-only scenario. Some inputs are unpublished data on file. The model excludes long COVID, COVID-related readmissions, uninsured patients, and measured value from freed hospital capacity. Its U.S. costs may not generalize to individual hospitals or other countries." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Trial Discussion/Methods and economic model Discussion, Secondary Analyses, Funding/Conflicts.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "COV-BARRIER investigators did not establish less disease progression, which prevented confirmatory testing of mortality under their analysis plan. RECOVERY participants knew their treatment. Lilly funded COV-BARRIER and the economic study. None compared discovery with and without AI." } }, "external_references[]": { "state": "value_present", "value": { "kind": "structured", "data": { "trial_registrations": [ { "id": "NCT04401579", "trial": "ACTT-2" }, { "id": "NCT04421027", "trial": "COV-BARRIER" }, { "id": "NCT04381936", "trial": "RECOVERY" }, { "id": "ISRCTN50189673", "trial": "RECOVERY" } ], "scope_note": "No harm involving this system and episode is established by the reviewed sources. The prior Watson example involved another system." } }, "provenance": { "kind": "source_review", "basis": "Linked actual trial identifiers and removed an unrelated incident comparison.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "ACTT-2: NCT04401579. COV-BARRIER: NCT04421027. RECOVERY: NCT04381936 and ISRCTN50189673." } }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2022-07-28", "note": "RECOVERY independently found lower 28-day mortality with baricitinib. WHO had recommended its use with corticosteroids for severe or critical COVID-19 in January 2022. The recommendation does not establish worldwide uptake.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/" } } }, "provenance": { "kind": "source_review", "basis": "Recorded further clinical confirmation and kept policy guidance separate from demonstrated uptake.", "record_fields": [ "later" ], "source_refs": [ "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Oxford’s RECOVERY investigators independently confirmed lower mortality in July 2022." } }, "human_oversight": { "state": "value_present", "value": { "kind": "structured", "data": { "discovery": "Scientists iteratively queried the graph and assessed target binding, dose, and pharmacology before selecting the drug.", "clinical_testing": "Trial investigators, ethics committees, and monitoring arrangements governed treatment and follow-up.", "unreported": "The company’s internal software access controls and formal training requirements are not described." } }, "provenance": { "kind": "source_review", "basis": "The methods paper assigns enrichment, querying, and pharmacological assessment to named authors; trials describe their own oversight.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers chose the candidate drug. Clinicians prescribed treatment under trial protocols, with oversight from ethics committees and monitors." } }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0042" } }, "provenance": { "kind": "source_review", "basis": "Preserved the existing case identifier.", "record_fields": [ "id" ], "source_refs": [], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD-0042" } }, "infrastructure_dependencies[]": { "state": "value_present", "value": { "kind": "structured", "data": { "data_sources": [ "KEGG", "Reactome", "STRING", "SIGNOR", "BioGRID", "ChEMBL", "CTD", "GWAS Catalog", "Gene Ontology", "OMIM", "Mondo" ], "software": "BenevolentAI knowledge graph and interactive network tools; SciPy is identified for enrichment analysis.", "unreported": [ "Compute hardware", "Cloud provider", "Hosting country", "Energy and water use", "Complete graph snapshot" ] } }, "provenance": { "kind": "source_review", "basis": "The methods paper names the data sources and software dependencies. Hardware and resource use are not documented.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The researchers used BenevolentAI’s graph, SciPy, and 11 biomedical resources, including KEGG, Reactome, and SIGNOR. Their methods paper does not identify the hardware or hosting provider." } }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "structured", "data": { "personal": "People hospitalized with COVID-19, principally adults who required supplemental oxygen or ventilatory support. Trials evaluated groups rather than identifying which particular people survived because of treatment.", "organizational": "Hospitals that treated eligible patients; shorter recovery is a proxy for resource use, and the monetary benefit is modeled.", "societal": "Health systems with access to baricitinib and the capacity to give it with appropriate clinical supervision. Trials establish patient effects, not an independently measured population-wide effect." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Trial eligibility and results; economic model Population and Perspective.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Hospitalized patients with COVID-19 and the hospitals and health systems treating them." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "structured", "data": { "known_exposure": [ { "study": "ACTT-2", "randomized": 1033, "baricitinib_assigned": 515, "control_assigned": 518 }, { "study": "COV-BARRIER", "randomized": 1525, "baricitinib_assigned": 764, "control_assigned": 761 }, { "study": "RECOVERY", "randomized": 8156, "baricitinib_assigned": 4148, "control_assigned": 4008 } ], "actually_benefiting": "The individual number whose recovery or survival changed because of baricitinib is not directly observed. Randomized comparisons estimate an average treatment effect. Trial enrollment, treatment assignment, patients discharged, and people whose lives were saved are different counts.", "population_total": "No verified worldwide beneficiary count is established by these trial reports." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Results, participant flow and mortality tables.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The trials enrolled 10,714 participants in total, including controls. That total does not identify how many people benefited. The publications give no worldwide count." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health", "taxonomy_note": "The clinical benefit is reduced mortality and faster recovery. AI assisted the researchers who proposed the drug for testing." } }, "provenance": { "kind": "source_review", "basis": "Assigned Improved Physical Health to the documented clinical benefit.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Improved Physical Health" } }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "structured", "data": { "benefits": [ { "id": "AIBD-0042-benefit-mortality", "title": "Fewer deaths", "statement": "In RECOVERY, 12% of patients receiving baricitinib died within 28 days, compared with 14% receiving usual care alone.", "benefit_type": "Improved Physical Health", "status": "reported", "beneficiaries": "Patients hospitalized with COVID-19.", "source_refs": [ "AIBD-0042-source-0002" ], "ratings": [ { "group": "personal", "depth": 5, "reason": "RECOVERY found lower 28-day mortality with baricitinib, a life-preserving benefit." } ], "evidence_word": "studied" }, { "id": "AIBD-0042-benefit-recovery", "title": "Faster recovery", "statement": "In ACTT-2, patients receiving remdesivir recovered in a median of 7 days with baricitinib, compared with 8 days with a placebo.", "benefit_type": "Improved Physical Health", "status": "reported", "beneficiaries": "Patients hospitalized with COVID-19.", "source_refs": [ "AIBD-0042-source-0005" ], "ratings": [ { "group": "personal", "depth": 2, "reason": "ACTT-2 found a shorter median recovery time in a randomized comparison, a measurable improvement in patients’ health." } ], "evidence_word": "studied" } ] } }, "provenance": { "kind": "source_review", "basis": "Separated the two measured clinical benefits already documented in the record. RECOVERY supports lower mortality; ACTT-2 supports faster recovery. The trials evaluated treatment effects, not the incremental value of AI-assisted drug selection. Each benefit has its own source references and beneficiary assessment. The existing hospital and societal assessments remain in the record.", "record_fields": [ "account", "benefit_type" ], "source_refs": [ "AIBD-0042-source-0002", "AIBD-0042-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Fewer deaths: In RECOVERY, 12% of patients receiving baricitinib died within 28 days, compared with 14% receiving usual care alone. Faster recovery: In ACTT-2, patients receiving remdesivir recovered in a median of 7 days with baricitinib, compared with 8 days with a placebo." } }, "outcomes[].distribution": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": "The high-flow oxygen or noninvasive ventilation subgroup had median recovery of 10 versus 18 days (recovery rate ratio 1.51, 95% CI 1.10–2.08), compared with 7 versus 8 days overall. Subgroup estimates have lower precision.", "COV-BARRIER": "Effects were examined by age, sex, region, baseline respiratory support, symptom duration, remdesivir, and corticosteroids. The large mortality difference among patients receiving high-flow oxygen or noninvasive ventilation is a secondary subgroup result subject to the trial’s multiplicity limit. The trial did not establish a positive benefit in every participating country.", "RECOVERY": "No evidence of treatment-effect differences across the six prespecified subgroups (age, sex, ethnicity, symptom duration, respiratory support, corticosteroid use; interaction p values >0.18). This is not proof that all subgroups benefit equally.", "economic_access": "The U.S. cost model excluded uninsured patients because out-of-pocket cost data were unavailable. It does not establish savings in other jurisdictions." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at ACTT-2 Table 2; COV-BARRIER Figures 2–3; RECOVERY Figure 3; economic model Costs.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "ACTT-2 patients on high-flow oxygen or noninvasive ventilation recovered in a median of 10 rather than 18 days. This subgroup estimate is less precise. RECOVERY found no clear differences among subgroups." } }, "outcomes[].duration": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": "Clinical outcomes followed through day 28.", "COV-BARRIER": "Primary and key secondary outcomes through day 28; exploratory mortality through day 60, with 79/764 deaths versus 116/761 (hazard ratio 0.62, 95% CI 0.47–0.83).", "RECOVERY": "The reported main outcomes were assessed at 28 days; a planned six-month analysis is not evidence of a six-month result.", "economic_model": "Hospital estimates cover the admission; payer estimates extend over a modeled lifetime. The lifetime benefit is projected, not observed follow-up." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Trial Outcomes/Results; economic model Population and Perspective.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The trials followed patients for 28 days. COV-BARRIER investigators also examined deaths through day 60 in an exploratory analysis. The economists projected costs and health over a lifetime." } }, "outcomes[].id": { "state": "value_present", "value": { "kind": "structured", "data": { "benefit_ids": [ "AIBD-0042-benefit-mortality", "AIBD-0042-benefit-recovery" ], "stakeholder_assessment_ids": { "personal": "AIBD-0042-outcome-personal", "organizational": "AIBD-0042-outcome-organizational", "societal": "AIBD-0042-outcome-societal" } } }, "provenance": { "kind": "curatorial", "basis": "Assigned separate stable identifiers to mortality and recovery while retaining the earlier stakeholder-assessment identifiers.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD-0042-benefit-mortality, AIBD-0042-benefit-recovery" } }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "Fewer patients died with baricitinib in COV-BARRIER and RECOVERY. RECOVERY confirmed lower 28-day mortality as its primary outcome in 8,156 patients." }, { "group": "organizational", "depth": 2, "reason": "ACTT-2 reported median recovery of 7 days with baricitinib and 8 days without it. That could free hospital capacity. A separate economic model estimated lower hospital costs, but neither establishes realized staffing or cash savings." }, { "group": "societal", "depth": 4, "reason": "WHO recommended baricitinib with corticosteroids for severe or critical COVID-19. That recommendation supports wider access to a treatment that reduced deaths in trials. Worldwide uptake and population-wide effects were not measured." } ] } }, "provenance": { "kind": "source_review", "basis": "Preserved the personal, organizational, and societal targets with explicit distinctions in their support.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0007", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "People, organizations, and society." } }, "outcomes[].status": { "state": "value_present", "value": { "kind": "structured", "data": { "personal": "Observed treatment benefit in trials", "organizational": "Potential capacity benefit and modeled financial result, not measured realized savings", "societal": "International treatment recommendation observed; population-wide effects not measured" } }, "provenance": { "kind": "source_review", "basis": "Separated observed patient outcomes from projected or unmeasured wider effects.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0007", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Patient benefits were observed. Hospital savings were modeled. WHO recommended wider treatment. These studies did not measure population-wide effects." } }, "outcomes[].tradeoffs": { "state": "value_present", "value": { "kind": "structured", "data": { "ACTT-2": "Serious adverse events occurred in 16.0% with baricitinib plus remdesivir and 21.0% with remdesivir alone. Venous thromboembolism occurred in 4.1% versus 3.1%, a difference of 1.0 percentage point (95% CI −1.3 to 3.3).", "COV-BARRIER": "Among 750 baricitinib recipients and 752 placebo recipients in the safety population, serious adverse events occurred in 110 (15%) versus 135 (18%), serious infections in 64 (9%) versus 74 (10%), and venous thromboembolism in 20 (3%) versus 19 (3%). These findings concern short treatment and follow-up and do not establish absence of rare or long-term harms.", "RECOVERY": "No significant excess in the prespecified infection, thrombotic, or bleeding outcomes was detected. Investigators reported 12 suspected serious adverse reactions attributed to baricitinib, including four serious infections other than COVID-19, three bowel perforations, and two pulmonary emboli.", "costs": "The model estimated lower hospital net costs only under its base-case assumptions. Its mortality-only scenario estimated higher hospital costs, and the lifetime payer model estimated higher overall cost because more patients survived." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Trial safety results; RECOVERY Results and appendix p. 61 reference; economic model Secondary Analyses.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Serious adverse events affected 15% of baricitinib recipients and 18% of controls in COV-BARRIER. RECOVERY investigators reported 12 suspected serious reactions. Short follow-up leaves rare and long-term harms uncertain." } }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "structured", "data": { "assessment": "Expected in purpose: researchers deliberately sought an existing medicine that could help people with COVID-19. Mortality reduction was a prespecified trial outcome, although it was secondary in COV-BARRIER. A failed progression endpoint and favorable mortality result are different findings, not grounds for calling the benefit accidental." } }, "provenance": { "kind": "source_review", "basis": "Reviewed the original source at Methods paper Introduction and workflow; trial prespecified Outcomes.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0004", "AIBD-0042-source-0001", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers deliberately sought a treatment, and investigators planned to measure mortality." } }, "places[]": { "state": "value_present", "value": { "kind": "structured", "data": { "location": "Multicountry trials (ACTT-2, COV-BARRIER, and RECOVERY): Argentina; Brazil; Denmark; Germany; India; Italy; Japan; Mexico; Russia; Singapore; South Korea; Spain; United Kingdom; United States", "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "developer_jurisdictions": [ "United Kingdom" ], "case_jurisdictions": [ "Argentina", "Brazil", "Denmark", "Germany", "India", "Italy", "Japan", "Mexico", "Russia", "Singapore", "South Korea", "Spain", "United Kingdom", "United States" ], "location_label": "Trial locations", "location_note": "Trial countries, with results pooled across sites. RECOVERY was conducted in the United Kingdom. Puerto Rico is included under the United States.", "ai_operation": "BenevolentAI research team, with author affiliations in London and Brooklyn. The exact compute location is not reported.", "clinical_evaluators": [ "ACTT-2: NIAID, United States", "COV-BARRIER: Eli Lilly, United States", "RECOVERY: University of Oxford, United Kingdom" ] } }, "provenance": { "kind": "source_review", "basis": "Separated the AI operator’s jurisdiction from trial locations and evaluators. Headquarters does not establish compute location.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0009", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "BenevolentAI operated in the United Kingdom. Trials ran in Argentina, Brazil, Denmark, Germany, India, Italy, Japan, Mexico, Russia, Singapore, South Korea, Spain, the United Kingdom, and the United States." } }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Researchers searched for existing drugs that might treat COVID-19, using a biomedical knowledge graph that combines databases with information extracted from scientific papers." } }, "provenance": { "kind": "source_review", "basis": "The original investigators state their intended search for a COVID-19 treatment.", "record_fields": [ "purpose" ], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers sought existing medicines that could treat COVID-19." } }, "record_dates": { "state": "value_present", "value": { "kind": "structured", "data": { "discovery": { "start": "2020-01", "end": "2020-01", "precision": "month" }, "first_ai_publication": "2020-02-04", "actt2_enrollment": { "start": "2020-05-08", "end": "2020-07-01" }, "cov_barrier_enrollment": { "start": "2020-06-11", "end": "2021-01-15" }, "recovery_enrollment": { "start": "2021-02-02", "end": "2021-12-29" }, "case_date": "2021-09-01", "case_date_basis": "Original publication of the COV-BARRIER mortality findings", "date_precision": "day", "first_database_revision": "2026-10-20", "database_discovery_note": "No separate discovery date is recorded.", "last_reviewed": "2026-10-25" } }, "provenance": { "kind": "source_review", "basis": "Separated discovery, study periods, publication, and editorial dates.", "record_fields": [ "date_observed", "date_basis", "date_precision", "revisions" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers identified baricitinib in January 2020 and reported it on 4 February 2020. COV-BARRIER mortality results appeared on 1 September 2021." } }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "BenevolentAI researchers combined a biomedical knowledge graph with expert review to select baricitinib.", "Clinical investigators separately tested the drug alongside the care available in 2020 and 2021." ], "must_hold": [ "The clinical comparisons concern hospitalized patients and the eligibility criteria of each trial.", "Background treatment differed: remdesivir in ACTT-2, and mostly corticosteroids in COV-BARRIER and RECOVERY.", "Reproducing the AI selection would also require the January 2020 graph, software configuration, and researchers’ decisions. The complete snapshot is not public." ], "echoes": [ { "date": "2021-09-01", "setting": "COV-BARRIER: 1,525 adults. Lower mortality was a secondary finding after an unsuccessful primary comparison.", "outcome": "recurred", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/" }, { "date": "2022-07-28", "setting": "RECOVERY: 8,156 patients in 159 U.K. hospitals. Lower 28-day mortality was the primary finding. These were further tests of the drug, not repetitions of the AI discovery process.", "outcome": "recurred", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/" } ] } } }, "provenance": { "kind": "source_review", "basis": "Replaced the WHO recommendation with a genuine subsequent clinical trial. Different trial protocols and endpoints remain explicit.", "record_fields": [ "replication" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "COV-BARRIER and RECOVERY both found fewer deaths with baricitinib. The trials tested the medicine, without repeating the AI discovery process." } }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "BenevolentAI researchers combined a biomedical knowledge graph with expert review to select baricitinib.", "Clinical investigators separately tested the drug alongside the care available in 2020 and 2021." ], "must_hold": [ "The clinical comparisons concern hospitalized patients and the eligibility criteria of each trial.", "Background treatment differed: remdesivir in ACTT-2, and mostly corticosteroids in COV-BARRIER and RECOVERY.", "Reproducing the AI selection would also require the January 2020 graph, software configuration, and researchers’ decisions. The complete snapshot is not public." ], "echoes": [ { "date": "2021-09-01", "setting": "COV-BARRIER: 1,525 adults. Lower mortality was a secondary finding after an unsuccessful primary comparison.", "outcome": "recurred", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/" }, { "date": "2022-07-28", "setting": "RECOVERY: 8,156 patients in 159 U.K. hospitals. Lower 28-day mortality was the primary finding. These were further tests of the drug, not repetitions of the AI discovery process.", "outcome": "recurred", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/" } ] } } }, "provenance": { "kind": "source_review", "basis": "Distinguished further drug trials from replication of the discovery workflow.", "record_fields": [ "replication" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Testing the treatment again requires comparable patients and background care. Repeating the discovery requires the original graph, software configuration, and researchers’ decisions." } }, "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 225 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/225", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Recategorised from specific harm prevented to 1 because the sources show an AI-generated repurposing hypothesis confirmed at cohort level by randomized trials rather than a specific harm prevented for an identifiable beneficiary, and, on the fact-checker's other findings, made the ACTT-2 quote verbatim, relabelled the Lancet correspondence letter as “other”, used the exact source titles, replaced the unsupported “hospitals worldwide” with the trial sponsors as deployers, and stated in the summary and account that COV-BARRIER missed its primary endpoint and that its 28-day mortality reduction was a secondary endpoint.", "source": "Richardson P, et al. Lancet 2020 (https://europepmc.org/article/PMC/PMC7137985); Kalil AC, et al. N Engl J Med 2021 (https://europepmc.org/article/PMC/PMC7745180); Marconi VC, et al. Lancet Respir Med 2021 (https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/)", "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/article/PMC/PMC7745180; https://www.who.int/news/item/14-01-2022-who-recommends-two-new-drugs-to-treat-covid-19", "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": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.benevolent.com/contact-us/; https://en.wikipedia.org/wiki/National_Institute_of_Allergy_and_Infectious_Diseases", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions 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://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the AI hypothesis, recovery and mortality findings, and regulatory actions to their original sources. Put COV-BARRIER first for the mortality claim, preserved the failed primary endpoint and added the multiplicity limitation. Removed the generic FDA drug-list page from active evidence because it adds no case-specific support beyond the retained authorization. Distinguished NIAID-led ACTT-2 from the Lilly-sponsored COV-BARRIER analysis.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "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://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "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://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the system identity beyond the original case report. 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Trial countries do not imply a separately demonstrated benefit in every country.", "source": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentais-platform-derived-hypothesis-covid-19-treatment-validated-us-niaid-randomised-control-trial/", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Rewrote the account and resolved all mandatory fields. Added RECOVERY and the original economic model, corrected the AI operator, distinguished projected wider benefits from observed patient outcomes, replaced WHO guidance counted as replication, and removed an unrelated Watson incident. Missing model and attribution details remain explicit. Reordered the sources by their contribution and remapped their identifiers.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/", "editor": "AIBD, Codex-assisted review" }, { "date": "2026-10-25", "change": "Added historical control from original corporate filings, documented funding and scientific-resource relationships, a labeled strategic assessment, and derived economic scenarios. Preserved the unreported exact model version, with a documented configuration proxy. Distinguished reported findings, calculations, and inferences throughout the assessment.", "source": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "editor": "AIBD, Codex-assisted review" } ] } }, "provenance": { "kind": "source_review", "basis": "Recorded the corrections with prior versions recoverable in repository history.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0009", "AIBD-0042-source-0010", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949", "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Ownership, funding, and economic estimates were added on 25 October 2026. 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Authorization, evidentiary standard, and ACTT-2 results.", "title": "FDA Authorizes Drug Combination for Treatment of COVID-19", "url": "https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-drug-combination-treatment-covid-19", "date": "2020-11-19", "written_by": "independent", "quote": "the drug baricitinib, in combination with remdesivir, for the treatment of suspected or laboratory confirmed COVID-19" }, { "kind": "report", "citation": "World Health Organization (2022). WHO recommends two new drugs to treat COVID-19.", "title": "WHO recommends two new drugs to treat COVID-19", "url": "https://www.who.int/news/item/14-01-2022-who-recommends-two-new-drugs-to-treat-covid-19", "date": "2022-01-14", "written_by": "independent", "quote": "The first drug, baricitinib, is strongly recommended for patients with severe or critical COVID-19." }, { "kind": "report", "citation": "BenevolentAI. “BenevolentAI Unaudited Preliminary Results for the Year Ended 31 December 2022,” March 16, 2023, Operational highlights, baricitinib bullet.", "title": "BenevolentAI Unaudited Preliminary Results for the Year Ended 31 December 2022", "url": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-unaudited-preliminary-results-year-ended-31-december-2022/", "date": "2023-03-16", "written_by": "makers" }, { "kind": "press release", "citation": "BenevolentAI. “BenevolentAI’s platform-derived hypothesis for COVID-19 treatment validated in US NIAID randomised control trial.” September 14, 2020. “About BenevolentAI,” headquarters statement.", "title": "BenevolentAI’s platform-derived hypothesis for COVID-19 treatment validated in US NIAID randomised control trial", "url": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentais-platform-derived-hypothesis-covid-19-treatment-validated-us-niaid-randomised-control-trial/", "date": "2020-09-14", "written_by": "makers" }, { "kind": "peer-reviewed study", "citation": "Robert Ohsfeldt et al. “Cost-Effectiveness of Baricitinib Compared With Standard of Care: A Modeling Study in Hospitalized Patients With COVID-19 in the United States.” Clinical Therapeutics 43, no. 11 (2021): 1877–1893.e4. 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Confirmation Statement CS01, October 15, 2019.", "locator": "Share capital, p. 2; shareholder details, pp. 4–6 (unnumbered leaves).", "date": "2019-10-15", "contribution": "The historical voting-share counts used for the labeled AIBD calculation.", "relationship": "Original company filing, with the ordinary and preferred share rights stated explicitly.", "access_note": "Original scanned statement and relevant shareholder pages inspected." }, { "url": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "citation": "BenevolentAI Limited. Return of Allotment SH01, December 20, 2019. Filed January 8, 2020.", "locator": "pp. 2–3.", "date": "2019-12-20", "contribution": "A capital check showing that the new G2 growth shares did not carry ordinary voting rights before an exit.", "relationship": "Original company filing. This checks share-class rights, not changes in every beneficial owner.", "access_note": "Original scanned return and share-rights pages inspected." }, { "url": "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "citation": "Covington & Burling LLP. “Covington Advises BenevolentAI on $90M Investment from Temasek.” October 15, 2019.", "locator": "¶¶ 1–3.", "date": "2019-10-15", "contribution": "A participant’s confirmation of Singaporean investment in the developer.", "relationship": "The law firm advised BenevolentAI on the transaction. It is a firsthand participant, not an independent financial evaluator.", "access_note": "Original counsel announcement inspected." }, { "url": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "citation": "BenevolentAI. “BenevolentAI Announces $90 Million Investment from Temasek.” September 17, 2019.", "locator": "Opening paragraph; funding-purpose paragraph; About BenevolentAI.", "date": "2019-09-17", "contribution": "The original funding announcement, its date, and the intended company-wide uses of the capital.", "relationship": "The recipient’s own announcement. It does not allocate funding to the later baricitinib work.", "access_note": "Original developer announcement inspected." }, { "url": "https://www.genome.jp/kegg/kegg1.html", "citation": "Kanehisa Laboratories. “KEGG Database.”", "locator": "KEGG overview and project history.", "contribution": "The Japanese origin of a biomedical resource that the discovery researchers used.", "relationship": "Resource developers’ account of its history. Geographic reuse is inferred by combining it with the baricitinib methods paper.", "access_note": "Original resource overview inspected." }, { "url": "https://doi.org/10.1093/nar/gkz949", "citation": "Luana Licata et al. “SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 Update.” Nucleic Acids Research 48, no. D1 (2020): D504–D510. https://doi.org/10.1093/nar/gkz949.", "locator": "Author affiliations; database access and download description.", "date": "2019-10-29", "contribution": "An Italy-led resource, with U.K. collaborators, that the discovery researchers reused.", "relationship": "Original resource paper. It establishes its producers and access conditions, not server or patient-data routes.", "access_note": "Original resource paper inspected." }, { "url": "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf", "citation": "Temasek Holdings. Temasek Overview 2019.", "locator": "p. 38, “Relating to Our Shareholder.”", "date": "2019", "contribution": "The investor’s government ownership and its stated separation from portfolio-company business decisions.", "relationship": "The investor’s own institutional account. It does not establish government control of BenevolentAI or the research.", "access_note": "Original report downloaded and read after browser access failed." } ] } }, "provenance": { "kind": "source_review", "basis": "Preserved the full clinical and discovery citations and added precise locators for corporate and geographic evidence.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0009", "AIBD-0042-source-0010", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949", "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "References 1–10 cover discovery, trials, and costs. References 11–18 cover ownership, funding, and geography." } }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0009", "AIBD-0042-source-0010", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949", "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf" ], "role": "Ten main works in reviewed order, followed by identifiers for the metadata sources" }, "provenance": { "kind": "source_review", "basis": "Kept the main evidence identifiers stable and used direct URLs to identify additional metadata sources.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0009", "AIBD-0042-source-0010", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949", "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD-0042-source-0001 through AIBD-0042-source-0010, plus eight supplementary references." } }, "sources[].language": { "state": "value_present", "value": { "kind": "structured", "data": { "language": "English", "translation_used": false, "scope": "All retained source passages were read in English. 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No translation was needed." } }, "sources[].relationship": { "state": "value_present", "value": { "kind": "structured", "data": { "sources": [ { "source_ref": "AIBD-0042-source-0001", "relationship": "Original trial by clinicians and the drug sponsor; not an independent evaluation of the AI discovery process." }, { "source_ref": "AIBD-0042-source-0002", "relationship": "Oxford-led trial, independent of BenevolentAI and the drug manufacturer. Baricitinib came from routine NHS stock." }, { "source_ref": "AIBD-0042-source-0003", "relationship": "BenevolentAI authors and an adviser report their own hypothesis." }, { "source_ref": "AIBD-0042-source-0004", "relationship": "Original methods paper from the developers. It adds technical detail, not an independent evaluation of AI’s clinical benefit." }, { "source_ref": "AIBD-0042-source-0005", "relationship": "NIAID controlled trial design and implementation. Lilly and Gilead supplied medicines and provided protocol input, but did not fund ACTT-2." }, { "source_ref": "AIBD-0042-source-0006", "relationship": "Original regulator statement; its efficacy figures derive from ACTT-2." }, { "source_ref": "AIBD-0042-source-0007", "relationship": "Original WHO recommendation based on trial synthesis, not another clinical experiment." }, { "source_ref": "AIBD-0042-source-0008", "relationship": "Developer’s retrospective identification, not independent verification of clinical benefit." }, { "source_ref": "AIBD-0042-source-0009", "relationship": "Original developer statement. Its clinical claims are not counted as additional verification of the trials." }, { "source_ref": "AIBD-0042-source-0010", "relationship": "Economic study funded by Eli Lilly, with company-employed coauthors. Trial inputs and cost assumptions are modeled, not another clinical experiment." } ], "caution": "An original source is not necessarily independent. FDA and WHO assess treatment evidence; they do not independently measure the contribution of the AI discovery workflow.", "metadata_sources": [ { "source_ref": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "relationship": "Original statutory company filing. The control statement is from the company’s accounts, not an inference from later ownership." }, { "source_ref": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "relationship": "Original company filing, with the ordinary and preferred share rights stated explicitly." }, { "source_ref": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "relationship": "Original company filing. This checks share-class rights, not changes in every beneficial owner." }, { "source_ref": "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "relationship": "The law firm advised BenevolentAI on the transaction. It is a firsthand participant, not an independent financial evaluator." }, { "source_ref": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "relationship": "The recipient’s own announcement. It does not allocate funding to the later baricitinib work." }, { "source_ref": "https://www.genome.jp/kegg/kegg1.html", "relationship": "Resource developers’ account of its history. Geographic reuse is inferred by combining it with the baricitinib methods paper." }, { "source_ref": "https://doi.org/10.1093/nar/gkz949", "relationship": "Original resource paper. It establishes its producers and access conditions, not server or patient-data routes." }, { "source_ref": "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf", "relationship": "The investor’s own institutional account. It does not establish government control of BenevolentAI or the research." } ] } }, "provenance": { "kind": "source_review", "basis": "Reviewed authors, funding, and document purpose separately from study design.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0009", "AIBD-0042-source-0010", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949", "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The developers supplied four sources. Four were independent, and two involved the drug sponsor. Lilly funded COV-BARRIER and the economic study. Manufacturers supplied ACTT-2 medicines and protocol advice, without funding the trial." } }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "lead_source_ref": "AIBD-0042-source-0001", "retained_source_order": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0009", "AIBD-0042-source-0010" ], "origins": [ { "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "contribution": "Lilly-funded COV-BARRIER: mortality and disease progression in 1,525 adults. Mortality was a secondary finding after an unsuccessful primary comparison.", "locator": "Results, Figure 2; supplementary appendix 6, pp. 9–14 and Figure S2. Table 1, p. 1411; publication date, p. 1407.", "relationship": "Original trial by clinicians and the drug sponsor; not an independent evaluation of the AI discovery process.", "source_refs": [ "AIBD-0042-source-0001" ], "display_name": "The Lancet Respiratory Medicine" }, { "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/", "source_refs": [ "AIBD-0042-source-0002" ], "display_name": "The Lancet", "contribution": "RECOVERY: an independent randomized trial of 8,156 patients, with 28-day mortality as its primary outcome.", "locator": "Table 2; Figure 3; Methods; Declaration of interests; Data sharing.", "relationship": "Oxford-led trial, independent of BenevolentAI and the drug manufacturer. Baricitinib came from routine NHS stock." }, { "url": "https://europepmc.org/article/PMC/PMC7137985", "contribution": "The original February 2020 proposal to test baricitinib, using BenevolentAI’s graph.", "locator": "Lancet 395:e30–e31, text, figure, and references, corrected June 18, 2020.", "relationship": "BenevolentAI authors and an adviser report their own hypothesis.", "source_refs": [ "AIBD-0042-source-0003" ], "display_name": "The Lancet" }, { "url": "https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.709856/full", "source_refs": [ "AIBD-0042-source-0004" ], "locator": "Materials and Methods; Results, opening paragraphs and figure 3; Discussion.", "display_name": "Frontiers in Pharmacology", "contribution": "The developers explain the graph, algorithms, and scientists’ role in selecting baricitinib.", "relationship": "Original methods paper from the developers. It adds technical detail, not an independent evaluation of AI’s clinical benefit." }, { "url": "https://europepmc.org/article/PMC/PMC7745180", "contribution": "NIAID-led ACTT-2: recovery time in 1,033 adults. Drug suppliers provided medicines and protocol input but did not fund the trial.", "locator": "Table 2; Trial Oversight; supplementary appendix, pp. 19–23; NCT04401579. Methods, Design, paragraph 2.", "relationship": "NIAID controlled trial design and implementation. Lilly and Gilead supplied medicines and provided protocol input, but did not fund ACTT-2.", "source_refs": [ "AIBD-0042-source-0005" ] }, { "url": "https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-drug-combination-treatment-covid-19", "contribution": "FDA’s November 2020 authorization and its evidentiary basis. The decision concerns the drug, not the AI discovery method.", "locator": "November 19, 2020 announcement, authorization link and ACTT-2 paragraphs.", "relationship": "Original regulator statement; its efficacy figures derive from ACTT-2.", "source_refs": [ "AIBD-0042-source-0006" ] }, { "url": "https://www.who.int/news/item/14-01-2022-who-recommends-two-new-drugs-to-treat-covid-19", "contribution": "WHO’s January 2022 recommendation. Guidance is not another clinical trial or a measure of worldwide uptake.", "locator": "January 14, 2022 statement, baricitinib recommendation and guideline link.", "relationship": "Original WHO recommendation based on trial synthesis, not another clinical experiment.", "source_refs": [ "AIBD-0042-source-0007" ] }, { "url": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-unaudited-preliminary-results-year-ended-31-december-2022/", "contribution": "The developer identifies the January 2020 discovery system as the Benevolent Platform.", "locator": "Operational highlights, baricitinib bullet.", "relationship": "Developer’s retrospective identification, not independent verification of clinical benefit.", "source_refs": [ "AIBD-0042-source-0008" ], "display_name": "BenevolentAI" }, { "url": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentais-platform-derived-hypothesis-covid-19-treatment-validated-us-niaid-randomised-control-trial/", "contribution": "BenevolentAI’s London headquarters during the 2020 work.", "locator": "About BenevolentAI, final paragraph: London headquarters.", "relationship": "Original developer statement. Its clinical claims are not counted as additional verification of the trials.", "source_refs": [ "AIBD-0042-source-0009" ], "display_name": "BenevolentAI" }, { "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8487786/", "source_refs": [ "AIBD-0042-source-0010" ], "display_name": "Clinical Therapeutics", "contribution": "A Lilly-funded model of U.S. treatment costs. Its hospital saving depends on the scenario and does not measure savings attributable to AI.", "locator": "Tables IV–V; supplemental table S6; Participants and Methods; Conflicts of Interest.", "relationship": "Economic study funded by Eli Lilly, with company-employed coauthors. Trial inputs and cost assumptions are modeled, not another clinical experiment." } ], "claim_chains": [ { "claim": "Mortality and failure of the primary endpoint", "path": [ "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "Supplementary appendix 6: Statistical analysis, pp. 9–10; Select secondary outcomes, pp. 11–12; Figure S2, p. 14" ], "finding": "The failure of the primary endpoint left no alpha for confirmatory testing of key secondary endpoints. The mortality p-value is nominal." }, { "claim": "Recovery time", "path": [ "https://europepmc.org/article/PMC/PMC7745180", "Table 2; supplementary appendix and original/final protocols" ], "finding": "ACTT-2 compared baricitinib plus remdesivir with placebo plus remdesivir. Its mortality comparison was not statistically conclusive." }, { "claim": "AI contribution", "path": [ "https://europepmc.org/article/PMC/PMC7137985", "Figure and original reference 6: Sorrell et al., Structure 24 (2016):401–411, doi:10.1016/j.str.2015.12.015", "https://pmc.ncbi.nlm.nih.gov/articles/PMC4780864/" ], "finding": "The correspondence is the origin of the AI-assisted prioritization claim. Sorrell et al. (2016), Figure 3B and the ligand-interaction results, supply original laboratory evidence of baricitinib binding to AAK1. That biochemical result does not establish the proposed antiviral mechanism or the clinical contribution of the AI discovery process." }, { "claim": "Regulatory follow-up", "path": [ "https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-drug-combination-treatment-covid-19", "https://www.fda.gov/media/143822/download", "https://www.who.int/news/item/14-01-2022-who-recommends-two-new-drugs-to-treat-covid-19", "https://iris.who.int/bitstream/handle/10665/351006/WHO-2019-nCoV-therapeutics-2022.1-eng.pdf" ], "finding": "The current FDA letter is a later reissuance with authorization history. The original WHO January 2022 PDF was identified but returned 403; the dated WHO announcement was read." }, { "claim": "System identity", "path": [ "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-unaudited-preliminary-results-year-ended-31-december-2022/" ], "finding": "Benevolent Platform, using its machine-learning-built knowledge graph and AAK1 inhibitor screening" }, { "claim": "Underlying workflow and named component", "path": [ "https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.709856/full" ], "finding": "The authors name SVO as a component and describe a workflow that combined computational analysis with expert selection. They do not establish an exact model release." }, { "claim": "Developer jurisdiction and geographic scope", "path": [ "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentais-platform-derived-hypothesis-covid-19-treatment-validated-us-niaid-randomised-control-trial/", "https://pmc.ncbi.nlm.nih.gov/articles/PMC8409066/", "https://europepmc.org/article/PMC/PMC7745180", "https://europepmc.org/article/PMC/PMC7137985" ], "finding": "The original correspondence and company statement identify London, United Kingdom, as the discovery team’s base. COV-BARRIER and ACTT-2 together cover 14 countries, with Puerto Rico included under the United States. These are trial settings, not separately demonstrated benefits in every country. The AI operator is BenevolentAI’s research team; clinical trial investigators evaluated the drug. Specific cross-border relationships are assessed separately." }, { "claim": "Further clinical confirmation", "path": [ "https://pmc.ncbi.nlm.nih.gov/articles/PMC9333998/", "Table 2 and Figure 3" ], "finding": "RECOVERY tested baricitinib against usual care. It did not replicate the AI discovery process." }, { "claim": "Economic estimates", "path": [ "https://pmc.ncbi.nlm.nih.gov/articles/PMC8487786/", "Tables IV–V", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx", "Supplemental table S6" ], "finding": "The model projects payer costs and hospital net costs. It does not measure cash savings or isolate an AI contribution." }, { "claim": "Historical ownership and control", "path": [ "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "p. 43, note 22", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0" ], "finding": "The accounts identify Kenneth Mulvany as the controller at year-end 2019. A separate AIBD calculation describes his voting-share fraction in the October register." }, { "claim": "Cross-border company funding", "path": [ "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek" ], "finding": "The company and its counsel report investment from Singapore-headquartered Temasek. The amount is company-wide and is not a measured benefit or research budget." }, { "claim": "Cross-border scientific resources", "path": [ "AIBD-0042-source-0004", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949" ], "finding": "Named resource use is documented. The country-to-country description is an AIBD inference from the resource producers and the discovery team." }, { "claim": "AIBD monetary scenarios", "path": [ "AIBD-0042-source-0010", "Tables IV–V", "https://ars.els-cdn.com/content/image/1-s2.0-S0149291821003891-mmc1.docx", "Supplemental table S6" ], "finding": "The return/payback assessment derives net monetary benefit and a full-course cost reference ratio from published inputs. Neither is an observed cash return or an AI-specific return." } ], "limitations": [ "No trial randomized the discovery process to AI versus no AI. The full proprietary graph and ranking run are unavailable.", "COV-BARRIER was sponsor-designed and sponsor-analyzed. Its mortality inference is nominal under the prespecified testing hierarchy.", "The cited recommendation establishes guidance, not measured worldwide adoption or a count of lives saved.", "The original Sorrell et al. article was read. Its linked supplementary Table S1 was identified, but the download returned 403 or a verification page and the supplementary archive timed out; no unavailable table contents were inferred.", "The authors’ measured recovery times do not establish realized hospital staffing or cash savings.", "The FDA source is a news release about an authorization, not a finding about AI effectiveness." ], "excluded_or_consolidated_sources": [ { "url": "https://www.fda.gov/drugs/emergency-preparedness-drugs/coronavirus-covid-19-drugs", "reason": "Generic, changing drug directory; no additional claim in the current case requires it." }, { "url": "https://incidentdatabase.ai/cite/225", "reason": "IBM Watson for Oncology is a different system and a different clinical use. Its incident does not document a harm in this baricitinib discovery or treatment episode." } ], "review_scope": "Reviewed all required fields for this discovery episode. The ten main works establish discovery, clinical effects, institutional decisions, and modeled treatment costs. Additional corporate filings, transaction accounts, and resource histories support ownership and geographic assessment. AIBD calculations and interpretations are labeled separately from reported findings.", "homepage_source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0003" ], "metadata_sources": [ { "url": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "citation": "BenevolentAI Limited. Annual Report and Consolidated Financial Statements, 31 December 2019. Filed December 30, 2020.", "locator": "p. 43, note 22; p. 40, note 18.", "date": "2019-12-31", "contribution": "Historical company control and share-class rights immediately before the January 2020 research.", "relationship": "Original statutory company filing. The control statement is from the company’s accounts, not an inference from later ownership.", "access_note": "Original scanned filing inspected, including a visual check of note 22." }, { "url": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "citation": "BenevolentAI Limited. Confirmation Statement CS01, October 15, 2019.", "locator": "Share capital, p. 2; shareholder details, pp. 4–6 (unnumbered leaves).", "date": "2019-10-15", "contribution": "The historical voting-share counts used for the labeled AIBD calculation.", "relationship": "Original company filing, with the ordinary and preferred share rights stated explicitly.", "access_note": "Original scanned statement and relevant shareholder pages inspected." }, { "url": "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "citation": "BenevolentAI Limited. Return of Allotment SH01, December 20, 2019. Filed January 8, 2020.", "locator": "pp. 2–3.", "date": "2019-12-20", "contribution": "A capital check showing that the new G2 growth shares did not carry ordinary voting rights before an exit.", "relationship": "Original company filing. This checks share-class rights, not changes in every beneficial owner.", "access_note": "Original scanned return and share-rights pages inspected." }, { "url": "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "citation": "Covington & Burling LLP. “Covington Advises BenevolentAI on $90M Investment from Temasek.” October 15, 2019.", "locator": "¶¶ 1–3.", "date": "2019-10-15", "contribution": "A participant’s confirmation of Singaporean investment in the developer.", "relationship": "The law firm advised BenevolentAI on the transaction. It is a firsthand participant, not an independent financial evaluator.", "access_note": "Original counsel announcement inspected." }, { "url": "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "citation": "BenevolentAI. “BenevolentAI Announces $90 Million Investment from Temasek.” September 17, 2019.", "locator": "Opening paragraph; funding-purpose paragraph; About BenevolentAI.", "date": "2019-09-17", "contribution": "The original funding announcement, its date, and the intended company-wide uses of the capital.", "relationship": "The recipient’s own announcement. It does not allocate funding to the later baricitinib work.", "access_note": "Original developer announcement inspected." }, { "url": "https://www.genome.jp/kegg/kegg1.html", "citation": "Kanehisa Laboratories. “KEGG Database.”", "locator": "KEGG overview and project history.", "contribution": "The Japanese origin of a biomedical resource that the discovery researchers used.", "relationship": "Resource developers’ account of its history. Geographic reuse is inferred by combining it with the baricitinib methods paper.", "access_note": "Original resource overview inspected." }, { "url": "https://doi.org/10.1093/nar/gkz949", "citation": "Luana Licata et al. “SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 Update.” Nucleic Acids Research 48, no. D1 (2020): D504–D510. https://doi.org/10.1093/nar/gkz949.", "locator": "Author affiliations; database access and download description.", "date": "2019-10-29", "contribution": "An Italy-led resource, with U.K. collaborators, that the discovery researchers reused.", "relationship": "Original resource paper. It establishes its producers and access conditions, not server or patient-data routes.", "access_note": "Original resource paper inspected." }, { "url": "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf", "citation": "Temasek Holdings. Temasek Overview 2019.", "locator": "p. 38, “Relating to Our Shareholder.”", "date": "2019", "contribution": "The investor’s government ownership and its stated separation from portfolio-company business decisions.", "relationship": "The investor’s own institutional account. It does not establish government control of BenevolentAI or the research.", "access_note": "Original report downloaded and read after browser access failed." } ], "interpretation_boundary": "Reported findings, direct calculations, and AIBD inferences are distinguished in the affected fields. A completed assessment does not imply that the exact proprietary model or a causal AI contribution is known." } }, "provenance": { "kind": "source_review", "basis": "Traced clinical, discovery, policy, economic, corporate, and geographic statements to their separate origins. Recorded the basis of derived calculations and interpretations.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0002", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0009", "AIBD-0042-source-0010", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI0NjgzNDI0M2FkaXF6a2N4/document?format=pdf&download=0", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI1NDE0MDc3M2FkaXF6a2N4/document?format=pdf&download=0", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949", "https://tr19.temasekreview.com.sg/downloads/Temasek-Review-2019-Overview.pdf" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The FDA and WHO based their conclusions on clinical trials. BenevolentAI’s announcements repeat its discovery account and the trial results." } }, "strategic_assessments[]": { "state": "value_present", "value": { "kind": "structured", "data": { "assessments": [ { "id": "AIBD-0042-strategic-uk-preparedness", "assessment_type": "AIBD qualitative inference", "assessed_on": "2026-10-25", "perspective": "United Kingdom public health preparedness and biomedical research capacity", "domain": "Pandemic preparedness", "reported_status": "The sources document the research network and treatment results. They do not quantify a change in national capability or an advantage attributable to AI.", "documented_basis": "A U.K. discovery team used international biomedical resources after Singaporean investment in the company. Researchers subsequently tested the drug in U.S.-led, multinational, and U.K. trials.", "aibd_inference": "This sequence illustrates a route from existing medicines to clinical tests during a new disease outbreak. Its value for U.K. preparedness depends on access to international knowledge, expert pharmacology, trial capacity, and medicines.", "capability_change": "The researchers produced a testable treatment hypothesis and the trials established a clinical benefit. The record does not identify how much sooner the hypothesis emerged because of AI.", "operational_dependency": "The published trial protocols administer a medicine without using BenevolentAI’s discovery software. AIBD therefore infers that continuing access to that software was not necessary for the treatment regimen that the trials evaluated.", "confidence": "Moderate for this qualitative assessment. No numerical national advantage, time saving, or strategic score is assigned.", "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949" ] } ] } }, "provenance": { "kind": "derivation", "basis": "Prepared a named, dated strategic assessment from the documented institutions and protocols. Marked the capability interpretation as AIBD inference.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0005", "AIBD-0042-source-0001", "AIBD-0042-source-0002", "https://find-and-update.company-information.service.gov.uk/company/09781806/filing-history/MzI4NjgwMDAzNWFkaXF6a2N4/document?format=pdf&download=0", "https://www.benevolent.com/news-and-media/press-releases-and-in-media/benevolentai-announces-90-million-investment-temasek/", "https://www.cov.com/en/news-and-insights/news/2019/10/covington-advises-benevolentai-on-90m-investment-from-temasek", "https://www.genome.jp/kegg/kegg1.html", "https://doi.org/10.1093/nar/gkz949" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "This work may strengthen the United Kingdom’s ability to find treatments during a new outbreak.", "editor_note": "The inference rests on the U.K. team’s January 2020 proposal and the subsequent international trials (references 1–5). Those studies establish a useful treatment, but give no estimate of how much earlier AI made it available." } }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Researchers used the Benevolent Platform to identify baricitinib as a candidate treatment for COVID-19.", "account": "BenevolentAI researchers reported in February 2020 that their knowledge graph had helped them identify baricitinib, an arthritis drug, as a possible treatment for COVID-19. They proposed clinical testing after reviewing its effects on inflammation and proteins involved in viral entry. In ACTT-2, 1,033 hospitalized adults received remdesivir with either baricitinib or a placebo. Median recovery took 7 days with baricitinib and 8 days with the placebo.\n\nCOV-BARRIER enrolled 1,525 adults. By day 28, 8% of patients receiving baricitinib had died, compared with 13% receiving a placebo. Both groups received standard care. Mortality was a secondary outcome. The trial did not meet its primary outcome of reduced disease progression, so its mortality result was not conclusive under the planned procedure for multiple comparisons.\n\nThe subsequent RECOVERY trial enrolled 8,156 patients. Mortality, its primary outcome, was 12% with baricitinib and 14% with usual care alone." } }, "provenance": { "kind": "source_review", "basis": "Separated clinical results from the unmeasured additional contribution of AI.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers used the Benevolent Platform to identify baricitinib as a candidate treatment for COVID-19." } }, "systems[].autonomy_permissions": { "state": "value_present", "value": { "kind": "structured", "data": { "operating_mode": "Interactive analysis directed by scientists", "selection_authority": "Researchers chose the candidate drug and proposed clinical testing.", "clinical_authority": "No autonomous prescribing or treatment execution is described.", "technical_permissions": "API permissions, access controls, and escalation rules are not reported." } }, "provenance": { "kind": "source_review", "basis": "The documented workflow preserves candidate selection and clinical decisions with people.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Scientists directed the analysis and selected the drug. Clinicians controlled treatment." } }, "systems[].component_role": { "state": "value_present", "value": { "kind": "structured", "data": { "roles": [ "Extract biomedical relationships from literature", "Retrieve relationships from a knowledge graph", "Display and analyze protein networks" ], "human_decision": "Scientists selected baricitinib after pharmacological assessment." } }, "provenance": { "kind": "source_review", "basis": "Methods and Author Contributions distinguish automated analysis from scientist selection.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The system extracted biomedical relationships, searched the graph, and analyzed networks of proteins." } }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "structured", "data": { "period": "January 2020", "workflow": "Coronavirus literature was added to the graph, followed by iterative queries and network analysis.", "unreported": [ "Hardware", "Hosting location", "Model checkpoint", "Exact application release", "Frozen graph snapshot", "Compute budget" ], "reported_status": "The exact model release and frozen graph snapshot are not reported.", "aibd_configuration_proxy": "BenevolentAI’s January 2020 coronavirus-augmented knowledge graph, combined with SVO relationship extraction, graph-pattern queries, and interactive protein-network analysis.", "proxy_basis": "Date and components are documented in the original methods paper. They identify a historical workflow, not an executable version or checkpoint.", "proxy_confidence": "High for the documented period and components; insufficient for exact computational reproduction.", "documented_components": { "relationship_extraction": "SVO extracts relationships from sentences using an unsupervised, rule-based approach.", "network_analysis": "Graph-pattern queries and an interactive protein-network tool with Louvain community detection.", "initial_network": { "proteins": 556, "interactions": 2286, "modules": 16 }, "human_review": "Researchers considered target affinity, free plasma concentration, pharmacokinetics, and dose when selecting baricitinib." } } }, "provenance": { "kind": "source_review", "basis": "Recorded a historically documented configuration proxy while leaving the exact model-version field unreported.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers added relationships from coronavirus papers to the knowledge graph. They used the Louvain algorithm to group connected proteins and examined the networks with interactive tools." } }, "systems[].inputs_outputs": { "state": "value_present", "value": { "kind": "structured", "data": { "inputs": [ "Biomedical databases", "Scientific literature about coronaviruses, proteins, pathways, and drugs" ], "outputs": [ "Relationships among biological entities", "Protein interaction networks", "Candidate drugs for expert review" ], "language": "English-language search examples are documented; the full language coverage of the corpus is not reported.", "patient_data": "The discovery workflow describes biomedical literature and databases, not individual trial-patient records." } }, "provenance": { "kind": "source_review", "basis": "Read Materials and Methods and Results in the original methods paper.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The system drew from biomedical literature and databases to produce relationships and protein networks that researchers used to assess candidate drugs." } }, "systems[].model_name": { "state": "value_present", "value": { "kind": "structured", "data": { "named_component": "SVO", "role": "Rule-based extraction of sentence-level relationships", "other_methods": [ "Graph pattern queries", "Louvain community detection", "Protein interaction network analysis" ], "single_model": "No single model represents the entire workflow." } }, "provenance": { "kind": "source_review", "basis": "The methods paper names the component and algorithms.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "SVO, a rule-based model that extracts relationships from sentences." } }, "systems[].model_version": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "The original methods paper does not identify an exact model release, checkpoint, or frozen January 2020 graph snapshot.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "reason": "The original methods paper does not identify an exact model release, checkpoint, or frozen January 2020 graph snapshot.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The configuration used in January 2020.", "editor_note": "Smith and colleagues describe the January 2020 work but give no release identifier for SVO (reference 4, p. 4)." } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Benevolent Platform" } }, "provenance": { "kind": "source_review", "basis": "The original workflow is identified retrospectively as the Benevolent Platform. This is a product name, not a model release.", "record_fields": [ "system" ], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0008", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Benevolent Platform" } }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "BenevolentAI", "application_operator": "BenevolentAI research team", "named_component_provider": "BenevolentAI" } }, "provenance": { "kind": "source_review", "basis": "The authors describe their own application and research workflow.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0003", "AIBD-0042-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "BenevolentAI" } }, "taxonomy_crosswalks[]": { "state": "value_present", "value": { "kind": "structured", "data": { "mapping": [ { "taxonomy": "Trustible AI Benefits Framework", "version": "May 1, 2024; AIBD local taxonomy snapshot", "category": "Improved Physical Health", "relationship": "Exact category assignment", "rationale": "Lower mortality and faster recovery concern patients’ physical health." } ], "other_taxonomies": "No crosswalk to an incident or risk taxonomy is asserted." } }, "provenance": { "kind": "curatorial", "basis": "Applied the existing local benefit taxonomy, independently of the depth scale.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The benefit matches Trustible’s “Improved Physical Health” category, published on 1 May 2024." } }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Baricitinib, identified with BenevolentAI, reduced COVID-19 deaths in trials", "headline": "An arthritis drug helped patients with severe COVID-19" } }, "provenance": { "kind": "source_review", "basis": "The title describes the drug benefit and identifies the discovery relationship.", "record_fields": [ "title", "headline" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0003", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "An arthritis drug helped patients with severe COVID-19" } }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "a8e1b0052ad9c7eff39b20f5389ce8ba29abb827c2271690fcac5264ab7db26f" ], "role": "SHA-256 of normalized case data" }, "provenance": { "kind": "curatorial", "basis": "Recomputed after integrating the sourced metadata and labeled estimates.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0006", "AIBD-0042-source-0007", "AIBD-0042-source-0008", "AIBD-0042-source-0004", "AIBD-0042-source-0009", "AIBD-0042-source-0002", "AIBD-0042-source-0010" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Revision a8e1b005, reviewed on 25 October 2026." } }, "workflow": { "state": "value_present", "value": { "kind": "text", "text": "BenevolentAI scientists added coronavirus literature to their graph, queried it, examined protein networks, and assessed candidate drugs. Clinical investigators subsequently tested baricitinib in separate trials." }, "provenance": { "kind": "source_review", "basis": "Read the discovery workflow and trial oversight sections.", "record_fields": [], "source_refs": [ "AIBD-0042-source-0001", "AIBD-0042-source-0005", "AIBD-0042-source-0003", "AIBD-0042-source-0004", "AIBD-0042-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Scientists searched the graph, examined protein networks, and assessed medicines. Clinical investigators then tested baricitinib." } } }, "summary": { "total": 91, "addressed": 91, "known": 90, "reviewed": 91, "record_mapped": 0, "not_assessed": 0, "complete": true, "by_state": { "value_present": 90, "not_reported": 1, "unresolved": 0, "not_assessed": 0, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0061", "case_url": "records/AIBD-0061.html", "reason": "The developer is headquartered in China; the case names Germany.", "details": [ { "label": "Developer headquarters", "value": "China" }, { "label": "Case jurisdictions", "value": "Germany" }, { "label": "Case location", "value": "Hamburg, Germany" } ], "filters": { "origin": "China", "destination": [ "Germany" ] }, "record": { "id": "AIBD-0061", "title": "DeepSeek-R1 dialogue leads a 34-year-old man to a genetically confirmed familial Mediterranean fever diagnosis after roughly three decades of symptoms", "summary": "Patient's DeepSeek-R1 dialogue proposed FMF; MEFV genetic testing confirmed it and colchicine stopped attacks after ~30 years of symptoms.", "date_observed": "2025-06-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A 34-year-old man of Egyptian origin began a dialogue with the DeepSeek-R1-0528 model on 28 June 2025. He had recurrent fever, migratory abdominal pain, arthralgia, myalgia and erythematous skin lesions since early childhood. The model refined its differential toward familial Mediterranean fever (FMF). It recommended specialist evaluation, inflammatory markers, genetic testing and colchicine under supervision. He arranged genetic testing, which found two pathogenic MEFV variants (p.Met694Val and p.Val726Ala). He obtained colchicine 0.5 mg twice daily from an Egyptian pharmacy. His attacks ceased, but alanine aminotransferase rose to 160 U/L. Colchicine was temporarily discontinued. His liver enzymes were monitored by his primary care physician. After they improved it was resumed at 0.5 mg daily under close supervision. He presented to the Department of Medicine III, University Medical Center Hamburg-Eppendorf, Germany, in February 2026, already taking 0.5 mg daily. The clinic confirmed FMF and he has remained attack-free.", "category": "benefit created", "unexpected": false, "purpose": "DeepSeek-R1 is a general-purpose reasoning chatbot. Answering a user's medical questions with a differential diagnosis falls within its general question-answering function. It is not a validated medical device.", "system": "DeepSeek-R1", "model_version": "DeepSeek-R1-0528", "developer": "DeepSeek", "deployer": null, "location": "Hamburg, Germany", "domain": "Healthcare - rheumatology / autoinflammatory disease", "segment": "Patient-initiated use of a consumer LLM", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Inflammatory attacks that had recurred since early childhood ceased on colchicine after a genetically confirmed diagnosis. The benefit is life-changing, tempered by a documented transaminase rise from self-dosing before medical supervision." } ], "ai_role": { "status": "assessed", "statement": "The large language model (LLM) generated the FMF hypothesis and the suggested work-up over a multi-turn dialogue. Confirmation came from MEFV genotyping and clinical response to colchicine. The authors stress that the model repeatedly advised specialist supervision. The self-directed genetic testing and self-medication were the patient's own actions." }, "evidence": [ { "kind": "case report", "citation": "Pauz J, Kremer P, Xiong T, Krusche M (2026). LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report. EULAR Rheumatology Open 2(4):100244.", "title": "LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "date": "2026-09-29", "written_by": "independent", "quote": "The patient nevertheless translated the generated diagnostic hypothesis into autonomous genetic testing and self-medication." }, { "kind": "case report", "citation": "Pauz J, Kremer P, Xiong T, Krusche M (2026). EULAR Rheumatology Open 2(4):100244 (second quotation).", "title": "LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "date": "2026-09-29", "written_by": "independent", "quote": "The model did not simply produce a correct diagnosis from a static symptom list but continuously refined its differential diagnosis" } ], "replication": { "produced_by": [ "A patient with a long-standing, characteristic but undiagnosed phenotype describing it in detail to a reasoning LLM over multiple turns", "Access to genetic testing for the suspected condition" ], "must_hold": [ "Genetic or clinical confirmation by a specialist before the diagnosis is accepted", "Treatment initiated under medical supervision (the case documents hepatotoxicity from self-dosing)" ], "echoes": [] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1609", "title": "ChatGPT-4o Allegedly Discouraged Florida Pastor From Seeking Care Before Pulmonary Embolism", "url": "https://incidentdatabase.ai/cite/1609", "severity": null, "group": "personal", "basis": "Same task, a consumer chatbot assessing a patient's own symptoms in dialogue: ChatGPT-4o attributed Scott Winters's dizziness and pain to benign causes and discouraged hospital care before a massive pulmonary embolism, the inverse of DeepSeek-R1's correct rare diagnosis." }, "headline": "DeepSeek-R1 proposes FMF; MEFV testing confirms the diagnosis", "metric": "colchicine stopped attacks after ~30 years of symptoms", "comparator": { "against": "almost three decades of conventional investigations", "before": "Despite almost 3 decades of symptoms, extensive investigations, including contrast-enhanced abdominal CT and MRI, gastroscopy, colonoscopy, and urological assessment, failed to establish a diagnosis.", "after": "the diagnosis of FMF was confirmed", "basis": "measured", "quote": "Despite almost 3 decades of symptoms, extensive investigations, including contrast-enhanced abdominal CT and MRI, gastroscopy, colonoscopy, and urological assessment, failed to establish a diagnosis.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/" }, "later": { "status": "persisted", "date": "2026-09-29", "note": "After a colchicine pause for raised liver enzymes and resumption at a lower dose, the patient remained attack-free under clinic follow-up as of publication.", "quote": "The patient has remained free of attacks with regular clinical and laboratory follow-up.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/" }, "value": null, "developer_country": "China", "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 1609 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1609", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Account corrected so that the colchicine pause after the ALT rise to 160 U/L and the resumption at 0.5 mg daily are placed before the February 2026 Hamburg presentation, monitored by the primary care physician, as the source states, and the stray “(day not given; first day used)” note was removed because the date observed is the exactly stated dialogue start date of 28 June 2025.", "source": "Pauz J, Kremer P, Xiong T, Krusche M (2026). EULAR Rheumatology Open 2(4):100244, https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "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/PMC13651492/; https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "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/DeepSeek", "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": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent case report. 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"evaluations", "display": { "answer": "colchicine stopped attacks after ~30 years of symptoms", "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": "1609", "title": "ChatGPT-4o Allegedly Discouraged Florida Pastor From Seeking Care Before Pulmonary Embolism", "url": "https://incidentdatabase.ai/cite/1609", "group": "personal", "basis": "Same task, a consumer chatbot assessing a patient's own symptoms in dialogue: ChatGPT-4o attributed Scott Winters's dizziness and pain to benign causes and discouraged hospital care before a massive pulmonary embolism, the inverse of DeepSeek-R1's correct rare diagnosis." } } }, "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": "2026-09-29", "note": "After a colchicine pause for raised liver enzymes and resumption at a lower dose, the patient remained attack-free under clinic follow-up as of publication.", "quote": "The patient has remained free of attacks with regular clinical and laboratory follow-up.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from 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"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 34-year-old man of Egyptian origin began a dialogue with the DeepSeek-R1-0528 model on 28 June 2025. He had recurrent fever, migratory abdominal pain, arthralgia, myalgia and erythematous skin lesions since early childhood. The model refined its differential toward familial Mediterranean fever (FMF). It recommended specialist evaluation, inflammatory markers, genetic testing and colchicine under supervision. He arranged genetic testing, which found two pathogenic MEFV variants (p.Met694Val and p.Val726Ala). He obtained colchicine 0.5 mg twice daily from an Egyptian pharmacy. His attacks ceased, but alanine aminotransferase rose to 160 U/L. Colchicine was temporarily discontinued. His liver enzymes were monitored by his primary care physician. After they improved it was resumed at 0.5 mg daily under close supervision. He presented to the Department of Medicine III, University Medical Center Hamburg-Eppendorf, Germany, in February 2026, already taking 0.5 mg daily. The clinic confirmed FMF and he has remained attack-free.", "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": "Inflammatory attacks that had recurred since early childhood ceased on colchicine after a genetically confirmed diagnosis. The benefit is life-changing, tempered by a documented transaminase rise from self-dosing before medical supervision." } ] } }, "provenance": { "kind": "legacy_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": "DeepSeek-R1 is a general-purpose reasoning chatbot. Answering a user's medical questions with a differential diagnosis falls within its general question-answering function. It is not a validated medical device." } }, "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": "Hamburg, Germany", "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": "DeepSeek-R1 is a general-purpose reasoning chatbot. Answering a user's medical questions with a differential diagnosis falls within its general question-answering function. It is not a validated medical device." } }, "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": "2025-06-28", "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 1609 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1609", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Account corrected so that the colchicine pause after the ALT rise to 160 U/L and the resumption at 0.5 mg daily are placed before the February 2026 Hamburg presentation, monitored by the primary care physician, as the source states, and the stray “(day not given; first day used)” note was removed because the date observed is the exactly stated dialogue start date of 28 June 2025.", "source": "Pauz J, Kremer P, Xiong T, Krusche M (2026). EULAR Rheumatology Open 2(4):100244, https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "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/PMC13651492/; https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "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/DeepSeek", "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 patient with a long-standing, characteristic but undiagnosed phenotype describing it in detail to a reasoning LLM over multiple turns", "Access to genetic testing for the suspected condition" ], "must_hold": [ "Genetic or clinical confirmation by a specialist before the diagnosis is accepted", "Treatment initiated under medical supervision (the case documents hepatotoxicity from self-dosing)" ], "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": "Paired with AI Incident Database incident 1609 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1609", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Account corrected so that the colchicine pause after the ALT rise to 160 U/L and the resumption at 0.5 mg daily are placed before the February 2026 Hamburg presentation, monitored by the primary care physician, as the source states, and the stray “(day not given; first day used)” note was removed because the date observed is the exactly stated dialogue start date of 28 June 2025.", "source": "Pauz J, Kremer P, Xiong T, Krusche M (2026). EULAR Rheumatology Open 2(4):100244, https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "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/PMC13651492/; https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "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/DeepSeek", "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 - rheumatology / autoinflammatory disease", "segment": "Patient-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": "case report", "citation": "Pauz J, Kremer P, Xiong T, Krusche M (2026). LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report. EULAR Rheumatology Open 2(4):100244.", "title": "LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "date": "2026-09-29", "written_by": "independent", "quote": "The patient nevertheless translated the generated diagnostic hypothesis into autonomous genetic testing and self-medication." }, { "kind": "case report", "citation": "Pauz J, Kremer P, Xiong T, Krusche M (2026). EULAR Rheumatology Open 2(4):100244 (second quotation).", "title": "LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "date": "2026-09-29", "written_by": "independent", "quote": "The model did not simply produce a correct diagnosis from a static symptom list but continuously refined its differential diagnosis" } ] } }, "provenance": { "kind": "legacy_record", "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-0061-source-0001", "AIBD-0061-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": "case report", "citation": "Pauz J, Kremer P, Xiong T, Krusche M (2026). LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report. EULAR Rheumatology Open 2(4):100244.", "title": "LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "date": "2026-09-29", "written_by": "independent", "quote": "The patient nevertheless translated the generated diagnostic hypothesis into autonomous genetic testing and self-medication." }, { "kind": "case report", "citation": "Pauz J, Kremer P, Xiong T, Krusche M (2026). EULAR Rheumatology Open 2(4):100244 (second quotation).", "title": "LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13651492/", "date": "2026-09-29", "written_by": "independent", "quote": "The model did not simply produce a correct diagnosis from a static symptom list but continuously refined its differential diagnosis" } ] } }, "provenance": { "kind": "legacy_record", "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": "Patient's DeepSeek-R1 dialogue proposed FMF; MEFV genetic testing confirmed it and colchicine stopped attacks after ~30 years of symptoms.", "account": "A 34-year-old man of Egyptian origin began a dialogue with the DeepSeek-R1-0528 model on 28 June 2025. He had recurrent fever, migratory abdominal pain, arthralgia, myalgia and erythematous skin lesions since early childhood. The model refined its differential toward familial Mediterranean fever (FMF). It recommended specialist evaluation, inflammatory markers, genetic testing and colchicine under supervision. He arranged genetic testing, which found two pathogenic MEFV variants (p.Met694Val and p.Val726Ala). He obtained colchicine 0.5 mg twice daily from an Egyptian pharmacy. His attacks ceased, but alanine aminotransferase rose to 160 U/L. Colchicine was temporarily discontinued. His liver enzymes were monitored by his primary care physician. After they improved it was resumed at 0.5 mg daily under close supervision. He presented to the Department of Medicine III, University Medical Center Hamburg-Eppendorf, Germany, in February 2026, already taking 0.5 mg daily. The clinic confirmed FMF and he has remained attack-free." } }, "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 suggested a possible diagnosis and further investigations during a conversation." }, "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": "DeepSeek-R1-0528." }, "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": "DeepSeek-R1-0528" } }, "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": "DeepSeek-R1-0528.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "DeepSeek-R1" } }, "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": "DeepSeek" } }, "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": "DeepSeek-R1 dialogue leads a 34-year-old man to a genetically confirmed familial Mediterranean fever diagnosis after roughly three decades of symptoms", "headline": "DeepSeek-R1 proposes FMF; MEFV testing confirms the diagnosis" } }, "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": [ "f7dee2b7bd11fcc94de8c213c28d421373f6f5d0a588ac396fac359dcb478eed" ], "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 34-year-old man of Egyptian origin began a dialogue with the DeepSeek-R1-0528 model on 28 June 2025. He had recurrent fever, migratory abdominal pain, arthralgia, myalgia and erythematous skin lesions since early childhood. The model refined its differential toward familial Mediterranean fever (FMF). It recommended specialist evaluation, inflammatory markers, genetic testing and colchicine under supervision. He arranged genetic testing, which found two pathogenic MEFV variants (p.Met694Val and p.Val726Ala). He obtained colchicine 0.5 mg twice daily from an Egyptian pharmacy. His attacks ceased, but alanine aminotransferase rose to 160 U/L. Colchicine was temporarily discontinued. His liver enzymes were monitored by his primary care physician. After they improved it was resumed at 0.5 mg daily under close supervision. He presented to the Department of Medicine III, University Medical Center Hamburg-Eppendorf, Germany, in February 2026, already taking 0.5 mg daily. The clinic confirmed FMF and he has remained attack-free.", "replication": { "produced_by": [ "A patient with a long-standing, characteristic but undiagnosed phenotype describing it in detail to a reasoning LLM over multiple turns", "Access to genetic testing for the suspected condition" ], "must_hold": [ "Genetic or clinical confirmation by a specialist before the diagnosis is accepted", "Treatment initiated under medical supervision (the case documents hepatotoxicity from self-dosing)" ], "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-0025", "case_url": "records/AIBD-0025.html", "reason": "The developer is headquartered in France; the case names Germany.", "details": [ { "label": "Developer headquarters", "value": "France" }, { "label": "Case jurisdictions", "value": "Germany" }, { "label": "Case location", "value": "Mannheim, Germany" } ], "filters": { "origin": "France", "destination": [ "Germany" ] }, "record": { "id": "AIBD-0025", "title": "Withings ScanWatch ECG QT measurement leading to congenital long QT syndrome diagnosis and beta-blocker therapy", "summary": "Withings ScanWatch ECGs showing prolonged QT led to a KCNH2 long-QT diagnosis and nadolol for a father and daughter (Germany).", "date_observed": "2024-07-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "An asymptomatic 39-year-old man came to the cardiology outpatient clinic of University Medical Centre Mannheim, Germany, because of QT prolongation recorded with his Withings ScanWatch. He had made its single-lead electrocardiogram (ECG) recordings out of curiosity. The watch displayed values such as QT 506 ms and QTc 457 ms. Genetic screening found a previously unpublished nonsense variant in KCNH2, establishing congenital long QT syndrome. Nadolol 40 mg daily shortened his QTc by 50–70 ms to 430 ms. His 7-year-old daughter carried the same variant with a borderline QTc of 460 ms and was also prescribed nadolol. The case report appeared on 4 July 2024 (event dates not reported).", "category": "benefit created", "unexpected": false, "purpose": "The ScanWatch records a single-lead ECG and, in the version described, includes a QT monitoring function. Flagging a prolonged QT interval was within what it was built to do.", "system": "Withings ScanWatch ECG with QT monitoring function", "model_version": "Withings ScanWatch (generation not stated)", "developer": "Withings", "deployer": null, "location": "Mannheim, Germany", "domain": "Healthcare (cardiology; clinical genetics)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Two identifiable people (father and daughter) received a genetic long QT diagnosis and prophylactic beta-blockade against sudden arrhythmic death. A life-changing benefit, though no event was imminent." } ], "ai_role": { "status": "assessed", "statement": "The watch's automated ECG analysis displayed the QT and QTc values that prompted the clinic visit. No automatic alert is described. The report does not say who first noticed the values. Diagnosis rested on clinical ECGs and genetic testing." }, "evidence": [ { "kind": "case report", "citation": "Rudic B, Kauferstein S, Akin I, Borggrefe M (2024). Can wearables outscore general practitioners? Congenital long QT syndrome diagnosis initiated by a smartwatch. HeartRhythm Case Reports 10(10):699-701.", "title": "Can wearables outscore general practitioners? Congenital long QT syndrome diagnosis initiated by a smartwatch", "url": "https://europepmc.org/article/PMC/PMC11628798", "date": "2024-07-04", "written_by": "independent", "quote": "A 39-year-old otherwise healthy male patient presented in our outpatient clinic owing to QT prolongation recorded with his smartwatch (ScanWatch; Withings SA, Issy les Moulineaux, France)." } ], "replication": { "produced_by": [ "The wearer records ScanWatch ECGs and the QT function reports a prolonged interval", "The wearer or a clinician takes the displayed values to a cardiology service", "Clinical ECG and genetic testing confirm an inherited channelopathy" ], "must_hold": [ "The QT function is available in the wearer's region and firmware", "Recordings are of adequate quality", "Cardiogenetic evaluation and family screening are accessible" ], "echoes": [] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1666", "title": "Oura Smart Ring Purchaser in California Alleged Company Overstated AI Sleep-Tracking Accuracy, Leading to Overpayment", "url": "https://incidentdatabase.ai/cite/1666", "severity": null, "group": "personal", "basis": "Consumer wearable inferring a health measure from sensor signals: ScanWatch's QT values proved true, while Oura's sleep-staging algorithm was allegedly less accurate than claimed, harming ring purchasers who relied on it." }, "headline": "Withings ScanWatch ECGs lead to a long-QT diagnosis and nadolol", "metric": "the watch displayed values such as QT 506 ms and QTc 457 ms", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The only cited source is the case report itself; no later development was found.", "quote": null, "url": null }, "value": null, "developer_country": "France", "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 1666 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1666", "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": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Withings", "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": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent case report. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0025", "catalog_version": "1.0.0", "source_fingerprint": "54ddaa87132a140f49ec348d1e858593a0f13c0462ab85a371d81ee001cfde88", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0025-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": "Withings" } }, "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 automated ECG analysis displayed the QT and QTc values that prompted the clinic visit. No automatic alert is described. The report does not say who first noticed the values. Diagnosis rested on clinical ECGs and genetic testing." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The watch's automated ECG analysis displayed the QT and QTc values that prompted the clinic visit. No automatic alert is described. The report does not say who first noticed the values. 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claimed, harming ring purchasers who relied on it." } } }, "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": "The only cited source is the case report itself; 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-0025" } }, "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": "An asymptomatic 39-year-old man came to the cardiology outpatient clinic of University Medical Centre Mannheim, Germany, because of QT prolongation recorded with his Withings ScanWatch. He had made its single-lead electrocardiogram (ECG) recordings out of curiosity. The watch displayed values such as QT 506 ms and QTc 457 ms. Genetic screening found a previously unpublished nonsense variant in KCNH2, establishing congenital long QT syndrome. Nadolol 40 mg daily shortened his QTc by 50–70 ms to 430 ms. His 7-year-old daughter carried the same variant with a borderline QTc of 460 ms and was also prescribed nadolol. The case report appeared on 4 July 2024 (event dates 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": "Two identifiable people (father and daughter) received a genetic long QT diagnosis and prophylactic beta-blockade against sudden arrhythmic death. A life-changing benefit, though no event 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": "The ScanWatch records a single-lead ECG and, in the version described, includes a QT monitoring function. Flagging a prolonged QT interval was within 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": { "location": "Mannheim, Germany", "developer_country": "France" } }, "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 ScanWatch records a single-lead ECG and, in the version described, includes a QT monitoring function. Flagging a prolonged QT interval was within 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": "2024-07-04", "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 1666 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1666", "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": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Withings", "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": [ "The wearer records ScanWatch ECGs and the QT function reports a prolonged interval", "The wearer or a clinician takes the displayed values to a cardiology service", "Clinical ECG and genetic testing confirm an inherited channelopathy" ], "must_hold": [ "The QT function is available in the wearer's region and firmware", "Recordings are of adequate quality", "Cardiogenetic evaluation and family screening are accessible" ], "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": "Paired with AI Incident Database incident 1666 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1666", "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": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Withings", "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 (cardiology; clinical genetics)", "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": "case report", "citation": "Rudic B, Kauferstein S, Akin I, Borggrefe M (2024). Can wearables outscore general practitioners? Congenital long QT syndrome diagnosis initiated by a smartwatch. HeartRhythm Case Reports 10(10):699-701.", "title": "Can wearables outscore general practitioners? Congenital long QT syndrome diagnosis initiated by a smartwatch", "url": "https://europepmc.org/article/PMC/PMC11628798", "date": "2024-07-04", "written_by": "independent", "quote": "A 39-year-old otherwise healthy male patient presented in our outpatient clinic owing to QT prolongation recorded with his smartwatch (ScanWatch; Withings SA, Issy les Moulineaux, France)." } ] } }, "provenance": { "kind": "legacy_record", "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-0025-source-0001" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "Rudic B, Kauferstein S, Akin I, Borggrefe M (2024). Can wearables outscore general practitioners? Congenital long QT syndrome diagnosis initiated by a smartwatch. HeartRhythm Case Reports 10(10):699-701.", "title": "Can wearables outscore general practitioners? Congenital long QT syndrome diagnosis initiated by a smartwatch", "url": "https://europepmc.org/article/PMC/PMC11628798", "date": "2024-07-04", "written_by": "independent", "quote": "A 39-year-old otherwise healthy male patient presented in our outpatient clinic owing to QT prolongation recorded with his smartwatch (ScanWatch; Withings SA, Issy les Moulineaux, France)." } ] } }, "provenance": { "kind": "legacy_record", "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": "Withings ScanWatch ECGs showing prolonged QT led to a KCNH2 long-QT diagnosis and nadolol for a father and daughter (Germany).", "account": "An asymptomatic 39-year-old man came to the cardiology outpatient clinic of University Medical Centre Mannheim, Germany, because of QT prolongation recorded with his Withings ScanWatch. He had made its single-lead electrocardiogram (ECG) recordings out of curiosity. The watch displayed values such as QT 506 ms and QTc 457 ms. Genetic screening found a previously unpublished nonsense variant in KCNH2, establishing congenital long QT syndrome. Nadolol 40 mg daily shortened his QTc by 50–70 ms to 430 ms. His 7-year-old daughter carried the same variant with a borderline QTc of 460 ms and was also prescribed nadolol. The case report appeared on 4 July 2024 (event dates 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 watch analyzed ECG recordings and displayed QT and QTc values." }, "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 a Withings ScanWatch." }, "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": "Withings ScanWatch (generation not stated)" } }, "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": "Withings ScanWatch ECG with QT monitoring function" } }, "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": "Withings" } }, "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": "Withings ScanWatch ECG QT measurement leading to congenital long QT syndrome diagnosis and beta-blocker therapy", "headline": "Withings ScanWatch ECGs lead to a long-QT diagnosis and nadolol" } }, "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": [ "54ddaa87132a140f49ec348d1e858593a0f13c0462ab85a371d81ee001cfde88" ], "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": "An asymptomatic 39-year-old man came to the cardiology outpatient clinic of University Medical Centre Mannheim, Germany, because of QT prolongation recorded with his Withings ScanWatch. He had made its single-lead electrocardiogram (ECG) recordings out of curiosity. The watch displayed values such as QT 506 ms and QTc 457 ms. Genetic screening found a previously unpublished nonsense variant in KCNH2, establishing congenital long QT syndrome. Nadolol 40 mg daily shortened his QTc by 50–70 ms to 430 ms. His 7-year-old daughter carried the same variant with a borderline QTc of 460 ms and was also prescribed nadolol. The case report appeared on 4 July 2024 (event dates not reported).", "replication": { "produced_by": [ "The wearer records ScanWatch ECGs and the QT function reports a prolonged interval", "The wearer or a clinician takes the displayed values to a cardiology service", "Clinical ECG and genetic testing confirm an inherited channelopathy" ], "must_hold": [ "The QT function is available in the wearer's region and firmware", "Recordings are of adequate quality", "Cardiogenetic evaluation and family screening are accessible" ], "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-0301", "case_url": "records/AIBD-0301.html", "reason": "The developer is headquartered in United States; the case names Netherlands.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Netherlands" }, { "label": "Case location", "value": "Amsterdam, the Netherlands" } ], "filters": { "origin": "United States", "destination": [ "Netherlands" ] }, "record": { "id": "AIBD-0301", "title": "A 76-year-old reads a transient negative T-wave on his own Apple Watch ECG after gym exercise; work-up finds silent three-vessel disease and he has quadruple bypass surgery (Amsterdam UMC)", "summary": "Koster et al. (EHJ Case Reports, 30 Jan 2024): a patient-initiated lead-I watch ECG showed a negative T-wave normalizing after 22 min; exercise test, CT and angiography found 3-vessel disease; CABG.", "date_observed": "2024-01-30", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Koster, de Winter, Verberne, Spijkerboer and Chamuleau of Amsterdam UMC reported a case in European Heart Journal Case Reports on 30 January 2024. The manuscript was received 26 June 2023 and the event is undated. The patient was a 76-year-old asymptomatic man with a strong family history of coronary disease. He had 30 years of treated hypercholesterolemia and four years of type 2 diabetes. He exercised weekly on a cross trainer. Very infrequently and “solely out of interest”, he recorded a lead-I electrocardiogram (ECG) with his Apple Watch right afterwards. On the last recording he noticed a negative T-wave that deepened over about seven minutes and normalized after 22 minutes. He had no chest pain, breathlessness, dizziness or palpitations. He consulted a cardiologist, whose resting ECG and examination were normal. A bicycle exercise test two days later reproduced the lead-I T-wave change with ST depression up to 4 mm in V5. Cardiac computed tomography (CT) showed stenoses in all coronary arteries (CAD-RADS 4). Invasive angiography showed three-vessel disease and a perfusion scan reversible ischemia in 16% of the myocardium. The heart team chose surgery and he received four bypass grafts. Recovery was uneventful and a repeat exercise ECG at 11 weeks was normal. The authors note Apple does not recommend the watch for detecting infarction or ischemia. They note lead I is not ideal for ischemia and the finding requires systematic study before clinical use. The paper reports no output from the ECG app's rhythm classifier. The wearer saw the abnormality on the trace and clinicians confirmed it.", "category": "benefit created", "unexpected": true, "purpose": "The ECG app is built to record a single-lead ECG and classify atrial fibrillation. Using the trace to spot exercise-induced ischemia is, as the authors say, not endorsed by Apple.", "system": "Apple Watch ECG app (single lead-I recording; its rhythm classifier is not mentioned in the report)", "model_version": null, "developer": "Apple", "deployer": null, "location": "Amsterdam, the Netherlands", "domain": "Healthcare (cardiology)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Silent, extensive, reversible ischemia from three-vessel disease was found and treated with quadruple bypass in an asymptomatic man. The authors' learning point is that such ischemia can end in exercise-induced sudden death. The counterfactual is not established." } ], "ai_role": { "status": "assessed", "statement": "The watch recorded a 30-second lead-I trace at the wearer's initiative. The report gives no classification result from the app. The negative T-wave was noticed by the patient and interpreted by cardiologists, who ordered the exercise test, imaging and surgery. No machine-learning output contributed to the finding as described. The single source is an independent peer-reviewed case report. No maker material was used." }, "evidence": [ { "kind": "case report", "citation": "Koster RW, de Winter RJ, Verberne HJ, Spijkerboer AM, Chamuleau SA (2024). Severe silent ischaemia detected with an Apple Watch in the home setting: a case report. European Heart Journal - Case Reports 8(2):ytae043 (PMC10872691).", "title": "Severe silent ischaemia detected with an Apple Watch in the home setting: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10872691/", "date": "2024-01-30", "written_by": "independent", "quote": "The use for detecting myocardial ischaemia is not endorsed by Apple but is documented in this case." }, { "kind": "case report", "citation": "Koster et al. (2024), Eur Heart J Case Rep 8(2):ytae043, case presentation.", "title": "Severe silent ischaemia detected with an Apple Watch in the home setting: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10872691/", "date": "2024-01-30", "written_by": "independent", "quote": "he noticed immediately after exercise, a negative T-wave that became more prominent in ∼7 min, then slowly returned back to a normal positive T-wave after 22 min" }, { "kind": "case report", "citation": "Koster et al. (2024), Eur Heart J Case Rep 8(2):ytae043, treatment.", "title": "Severe silent ischaemia detected with an Apple Watch in the home setting: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10872691/", "date": "2024-01-30", "written_by": "independent", "quote": "Coronary bypass surgery was performed with four bypasses on all coronary arteries involved" }, { "kind": "case report", "citation": "Koster et al. (2024), Eur Heart J Case Rep 8(2):ytae043, learning points.", "title": "Severe silent ischaemia detected with an Apple Watch in the home setting: a case report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10872691/", "date": "2024-01-30", "written_by": "independent", "quote": "Extensive exercise-induced reversible myocardial ischaemia may remain asymptomatic and can result in exercise-induced sudden death." } ], "replication": { "produced_by": [ "A wearer who records a watch ECG right after exercise and looks at the waveform, not only the rhythm label", "A cardiologist willing to investigate a watch trace in an asymptomatic patient", "Exercise testing, CT, angiography and perfusion imaging to confirm and grade the disease" ], "must_hold": [ "The lead-I trace reproduces T-wave changes faithfully, which the authors say appeared accurate though frequency characteristics are undocumented", "Ischemia that shows in lead I, which the authors call not ideal for the purpose", "Tolerance of false positives and investigation cascades, which the authors flag as unstudied" ], "echoes": [] }, "paired_incident": null, "headline": "Apple Watch ECG T-wave leads a 76-year-old to quadruple bypass", "metric": "he received four bypass grafts; recovery was uneventful", "comparator": { "against": "the cardiologist's resting 12-lead ECG and physical examination", "before": "A standard ECG was completely normal, as was physical examination.", "after": "he noticed immediately after exercise, a negative T-wave that became more prominent in ∼7 min, then slowly returned back to a normal positive T-wave after 22 min", "basis": "measured", "quote": "He consulted a cardiologist and there was agreement that this Apple Watch observation warranted further investigation. A standard ECG was completely normal, as was physical examination.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10872691/" }, "later": { "status": "unknown", "date": null, "note": "The paper's own 11-week postoperative exercise ECG (normal) is part of the first report; nothing published later about the patient 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 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://pmc.ncbi.nlm.nih.gov/articles/PMC10872691/", "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": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent case report. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0301", "catalog_version": "1.0.0", "source_fingerprint": "7f02698d60bc4f405f2972c173105df69d640a735070abf328fc8c24784f1e95", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0301-source-0001", "AIBD-0301-source-0002", "AIBD-0301-source-0003", "AIBD-0301-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 recorded a 30-second lead-I trace at the wearer's initiative. The report gives no classification result from the app. The negative T-wave was noticed by the patient and interpreted by cardiologists, who ordered the exercise test, imaging and surgery. No machine-learning output contributed to the finding as described. The single source is an independent peer-reviewed case report. No maker material 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": "benefit created", "ai_role": { "status": "assessed", "statement": "The watch recorded a 30-second lead-I trace at the wearer's initiative. The report gives no classification result from the app. The negative T-wave was noticed by the patient and interpreted by cardiologists, who ordered the exercise test, imaging and surgery. No machine-learning output contributed to the finding as described. The single source is an independent peer-reviewed case report. No maker material 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": "Silent, extensive, reversible ischemia from three-vessel disease was found and treated with quadruple bypass in an asymptomatic man. The authors' learning point is that such ischemia can end in exercise-induced sudden death. The counterfactual 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": "high", "evidence_word": "studied", "claim_status": "Independently studied", "basis": "Sources: one independent case report. 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", "depth": 3, "reason": "Silent, extensive, reversible ischemia from three-vessel disease was found and treated with quadruple bypass in an asymptomatic man. The authors' learning point is that such ischemia can end in exercise-induced sudden death. The counterfactual 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": "Silent, extensive, reversible ischemia from three-vessel disease was found and treated with quadruple bypass in an asymptomatic man. The authors' learning point is that such ischemia can end in exercise-induced sudden death. 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He consulted a cardiologist, whose resting ECG and examination were normal. A bicycle exercise test two days later reproduced the lead-I T-wave change with ST depression up to 4 mm in V5. Cardiac computed tomography (CT) showed stenoses in all coronary arteries (CAD-RADS 4). Invasive angiography showed three-vessel disease and a perfusion scan reversible ischemia in 16% of the myocardium. The heart team chose surgery and he received four bypass grafts. Recovery was uneventful and a repeat exercise ECG at 11 weeks was normal. The authors note Apple does not recommend the watch for detecting infarction or ischemia. They note lead I is not ideal for ischemia and the finding requires systematic study before clinical use. The paper reports no output from the ECG app's rhythm classifier. The wearer saw the abnormality on the trace and clinicians 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": "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 ECG app (single lead-I recording; its rhythm classifier is not mentioned in the report)" } }, "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 76-year-old reads a transient negative T-wave on his own Apple Watch ECG after gym exercise; work-up finds silent three-vessel disease and he has quadruple bypass surgery (Amsterdam UMC)", "headline": "Apple Watch ECG T-wave leads a 76-year-old to quadruple bypass" } }, "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": [ "7f02698d60bc4f405f2972c173105df69d640a735070abf328fc8c24784f1e95" ], "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": "Koster, de Winter, Verberne, Spijkerboer and Chamuleau of Amsterdam UMC reported a case in European Heart Journal Case Reports on 30 January 2024. The manuscript was received 26 June 2023 and the event is undated. The patient was a 76-year-old asymptomatic man with a strong family history of coronary disease. He had 30 years of treated hypercholesterolemia and four years of type 2 diabetes. He exercised weekly on a cross trainer. Very infrequently and “solely out of interest”, he recorded a lead-I electrocardiogram (ECG) with his Apple Watch right afterwards. On the last recording he noticed a negative T-wave that deepened over about seven minutes and normalized after 22 minutes. He had no chest pain, breathlessness, dizziness or palpitations. He consulted a cardiologist, whose resting ECG and examination were normal. A bicycle exercise test two days later reproduced the lead-I T-wave change with ST depression up to 4 mm in V5. Cardiac computed tomography (CT) showed stenoses in all coronary arteries (CAD-RADS 4). Invasive angiography showed three-vessel disease and a perfusion scan reversible ischemia in 16% of the myocardium. The heart team chose surgery and he received four bypass grafts. Recovery was uneventful and a repeat exercise ECG at 11 weeks was normal. The authors note Apple does not recommend the watch for detecting infarction or ischemia. They note lead I is not ideal for ischemia and the finding requires systematic study before clinical use. The paper reports no output from the ECG app's rhythm classifier. The wearer saw the abnormality on the trace and clinicians confirmed it.", "replication": { "produced_by": [ "A wearer who records a watch ECG right after exercise and looks at the waveform, not only the rhythm label", "A cardiologist willing to investigate a watch trace in an asymptomatic patient", "Exercise testing, CT, angiography and perfusion imaging to confirm and grade the disease" ], "must_hold": [ "The lead-I trace reproduces T-wave changes faithfully, which the authors say appeared accurate though frequency characteristics are undocumented", "Ischemia that shows in lead I, which the authors call not ideal for the purpose", "Tolerance of false positives and investigation cascades, which the authors flag as unstudied" ], "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-0022", "case_url": "records/AIBD-0022.html", "reason": "The developer is headquartered in United States; the case names Japan.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Japan" }, { "label": "Case location", "value": "Yufu, Oita Prefecture, Japan" } ], "filters": { "origin": "United States", "destination": [ "Japan" ] }, "record": { "id": "AIBD-0022", "title": "Apple Watch Blood Oxygen reading leading to diagnosis and treatment of pulmonary embolism", "summary": "Apple Watch Series 7 SpO2 of 88% prompted CT angiography that found pulmonary embolism; anticoagulated and discharged (Japan).", "date_observed": "2022-09-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A case appeared online on 5 September 2022 in QJM (event date not reported). A 50-year-old woman who recently had COVID-19 was referred to the authors' hospital (Oita University, Yufu, Japan) for assessment of dyspnea. Hospital pulse oximetry read 97%, but her Apple Watch Series 7 had shown 88% oxygen saturation in the early morning. Prompted by the watch reading, clinicians did emergent computed tomography (CT) angiography. It showed filling defects in multiple pulmonary artery branches, confirming pulmonary embolism with bilateral deep venous thrombosis (DVT). She was treated with a direct oral anticoagulant, oxygen was stopped on day 6 and she was discharged on day 15. The authors state the watch's low reading led to the diagnosis.", "category": "benefit created", "unexpected": true, "purpose": "The Blood Oxygen app measures oxygen saturation for general wellness and is not intended for medical diagnosis. Detecting a pulmonary embolism was not what it was built to do.", "system": "Apple Watch Blood Oxygen app", "model_version": "Apple Watch Series 7 (event); Series 6 in echo", "developer": "Apple", "deployer": null, "location": "Yufu, Oita Prefecture, Japan", "domain": "Healthcare (emergency and respiratory medicine)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "A pulmonary embolism with bilateral DVT was diagnosed and anticoagulated after hospital oximetry had been normal. A life-changing benefit, though the source does not establish that death was imminent." } ], "ai_role": { "status": "assessed", "statement": "The watch's pulse-oximetry algorithm produced the 88% reading on the patient's watch. The letter does not say who reported it to the clinicians and describes no automatic alert. Clinicians chose to perform CT angiography because of that reading and state that it led to the diagnosis." }, "evidence": [ { "kind": "case report", "citation": "Shiota S, Horinouchi Y, Doi E, Miyazaki E (2022). Suspicion of pulmonary embolism after COVID-19 infection raised by a smartwatch. QJM, hcac217 (online 5 Sep 2022; issue 2023).", "title": "Suspicion of pulmonary embolism after COVID-19 infection raised by a smartwatch", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9494381/", "date": "2022-09-05", "written_by": "independent", "quote": "The smartwatch correctly recognized a low SpO2, which led to the diagnosis of PE in our case." }, { "kind": "case report", "citation": "Tulisiak LN, Loesing ME, Ljungberg A, Kaczor EE (2025). Smartwatch Detection of Refractory Hypoxemia in Phenazopyridine-Induced Methemoglobinemia: A Case Report. Journal of Medical Toxicology 21(4):430-433.", "title": "Smartwatch Detection of Refractory Hypoxemia in Phenazopyridine-Induced Methemoglobinemia", "url": "https://europepmc.org/article/PMC/PMC12511463", "date": "2025-08-04", "written_by": "independent", "quote": "The day before presentation, she was walking in the mall when she was notified by her Series 6 Apple Watch© (version 9.6.3) that her oxygen level was low." } ], "replication": { "produced_by": [ "The wearer has an Apple Watch with the Blood Oxygen app and reviews its readings", "A hypoxemic condition produces a persistently low reading at home", "The wearer reports the reading and a clinician acts on it despite normal spot oximetry" ], "must_hold": [ "The reading is not dismissed as artifact", "Confirmatory testing (CT angiography, co-oximetry) is available", "Effective treatment exists for the condition found" ], "echoes": [ { "date": "2025-08-04", "setting": "71-year-old woman, emergency department, USA (authors' institution: University at Buffalo); her watch notified her of low oxygen and read 73–86% over the next 24 hours, but she ignored the alert and sought care only when her family saw blue discoloration of her skin; hospital pulse oximetry itself read 84% and venous methaemoglobin was 19.8% from phenazopyridine; methylene blue, discharged after about 24 hours; the watch readings corroborated the hospital finding rather than bringing her to care, so the index mechanism did not repeat (publication date)", "model_version": "Apple Watch Series 6", "outcome": "did not recur", "source": "Tulisiak et al. (2025). J Med Toxicol 21(4):430-433. https://europepmc.org/article/PMC/PMC12511463" } ] }, "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 device, Apple Watch: a sensor-driven alert that was false (Crash Detection dialed 911 for skiers) mirrors a sensor reading that was true (88% SpO2 prompting CT); harmed were Apple Watch users and dispatchers." }, "headline": "Apple Watch SpO2 of 88% prompts CT that finds pulmonary embolism", "metric": "Apple Watch Series 7 had shown an oxygen saturation of 88% in the early morning", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No development after the 2022 case report was found; the 2025 methemoglobinemia report involved a different patient and mechanism.", "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 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Restored “(version 9.6.3)” to the Tulisiak quote, re-recorded the Tulisiak echo as “did not recur” because the patient ignored the watch alert and hospital oximetry itself read 84%, replaced “Buffalo” and “Oita” with the authors' institutions as the sources state them (University at Buffalo; Oita University, Yufu), dropped the unsupported claim that the patient reported the 88% reading, and changed the kind of event from a specific harm prevented to a benefit created or restored because the source shows a changed diagnostic course rather than an averted specified harm, in line with AIBD-0021 and AIBD-0025.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9494381/ ; https://europepmc.org/article/PMC/PMC12511463", "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": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: two independent case reports. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0022", "catalog_version": "1.0.0", "source_fingerprint": "37aad5184fa2ebfaedddf37a1f064ea271a76fecfb119919ae17d8d00138ffef", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0022-source-0001", "AIBD-0022-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": "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 pulse-oximetry algorithm produced the 88% reading on the patient's watch. The letter does not say who reported it to the clinicians and describes no automatic alert. Clinicians chose to perform CT angiography because of that reading and state that it led to the diagnosis." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The watch's pulse-oximetry algorithm produced the 88% reading on the patient's watch. The letter does not say who reported it to the clinicians and describes no automatic alert. Clinicians chose to perform CT angiography because of that reading and state that it led to the diagnosis." } } }, "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 pulmonary embolism with bilateral DVT was diagnosed and anticoagulated after hospital oximetry had been normal. A life-changing benefit, though the source does not establish that death 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" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "high", "evidence_word": "studied", "claim_status": "Independently studied", "basis": "Sources: two independent case reports. 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A life-changing benefit, though the source does not establish that death 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" }, "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 pulmonary embolism with bilateral DVT was diagnosed and anticoagulated after hospital oximetry had been normal. A life-changing benefit, though the source does not establish that death 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" }, "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 device, Apple Watch: a sensor-driven alert that was false (Crash Detection dialed 911 for skiers) mirrors a sensor reading that was true (88% SpO2 prompting CT); harmed were Apple Watch users and dispatchers.", "links": [ { "database": "AI Incident Database", 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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": { "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 device, Apple Watch: a sensor-driven alert that was false (Crash Detection dialed 911 for skiers) mirrors a sensor reading that was true (88% SpO2 prompting CT); harmed were Apple Watch users and 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 after the 2022 case report was found; the 2025 methemoglobinemia report involved a different patient and mechanism." } } }, "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-0022" } }, "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 appeared online on 5 September 2022 in QJM (event date not reported). A 50-year-old woman who recently had COVID-19 was referred to the authors' hospital (Oita University, Yufu, Japan) for assessment of dyspnea. Hospital pulse oximetry read 97%, but her Apple Watch Series 7 had shown 88% oxygen saturation in the early morning. Prompted by the watch reading, clinicians did emergent computed tomography (CT) angiography. It showed filling defects in multiple pulmonary artery branches, confirming pulmonary embolism with bilateral deep venous thrombosis (DVT). She was treated with a direct oral anticoagulant, oxygen was stopped on day 6 and she was discharged on day 15. The authors state the watch's low reading led to the diagnosis.", "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 pulmonary embolism with bilateral DVT was diagnosed and anticoagulated after hospital oximetry had been normal. A life-changing benefit, though the source does not establish that death 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": true, "purpose": "The Blood Oxygen app measures oxygen saturation for general wellness and is not intended for medical diagnosis. Detecting a pulmonary embolism was not 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": { "location": "Yufu, Oita Prefecture, Japan", "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": "The Blood Oxygen app measures oxygen saturation for general wellness and is not intended for medical diagnosis. Detecting a pulmonary embolism was not 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": "2022-09-05", "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 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Restored “(version 9.6.3)” to the Tulisiak quote, re-recorded the Tulisiak echo as “did not recur” because the patient ignored the watch alert and hospital oximetry itself read 84%, replaced “Buffalo” and “Oita” with the authors' institutions as the sources state them (University at Buffalo; Oita University, Yufu), dropped the unsupported claim that the patient reported the 88% reading, and changed the kind of event from a specific harm prevented to a benefit created or restored because the source shows a changed diagnostic course rather than an averted specified harm, in line with AIBD-0021 and AIBD-0025.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9494381/ ; https://europepmc.org/article/PMC/PMC12511463", "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": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "The wearer has an Apple Watch with the Blood Oxygen app and reviews its readings", "A hypoxemic condition produces a persistently low reading at home", "The wearer reports the reading and a clinician acts on it despite normal spot oximetry" ], "must_hold": [ "The reading is not dismissed as artifact", "Confirmatory testing (CT angiography, co-oximetry) is available", "Effective treatment exists for the condition found" ], "echoes": [ { "date": "2025-08-04", "setting": "71-year-old woman, emergency department, USA (authors' institution: University at Buffalo); her watch notified her of low oxygen and read 73–86% over the next 24 hours, but she ignored the alert and sought care only when her family saw blue discoloration of her skin; hospital pulse oximetry itself read 84% and venous methaemoglobin was 19.8% from phenazopyridine; methylene blue, discharged after about 24 hours; the watch readings corroborated the hospital finding rather than bringing her to care, so the index mechanism did not repeat (publication date)", "model_version": "Apple Watch Series 6", "outcome": "did not recur", "source": "Tulisiak et al. (2025). J Med Toxicol 21(4):430-433. https://europepmc.org/article/PMC/PMC12511463" } ] } } }, "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": [ "The wearer has an Apple Watch with the Blood Oxygen app and reviews its readings", "A hypoxemic condition produces a persistently low reading at home", "The wearer reports the reading and a clinician acts on it despite normal spot oximetry" ], "must_hold": [ "The reading is not dismissed as artifact", "Confirmatory testing (CT angiography, co-oximetry) is available", "Effective treatment exists for the condition found" ], "echoes": [ { "date": "2025-08-04", "setting": "71-year-old woman, emergency department, USA (authors' institution: University at Buffalo); her watch notified her of low oxygen and read 73–86% over the next 24 hours, but she ignored the alert and sought care only when her family saw blue discoloration of her skin; hospital pulse oximetry itself read 84% and venous methaemoglobin was 19.8% from phenazopyridine; methylene blue, discharged after about 24 hours; the watch readings corroborated the hospital finding rather than bringing her to care, so the index mechanism did not repeat (publication date)", "model_version": "Apple Watch Series 6", "outcome": "did not recur", "source": "Tulisiak et al. (2025). J Med Toxicol 21(4):430-433. https://europepmc.org/article/PMC/PMC12511463" } ] } } }, "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 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Restored “(version 9.6.3)” to the Tulisiak quote, re-recorded the Tulisiak echo as “did not recur” because the patient ignored the watch alert and hospital oximetry itself read 84%, replaced “Buffalo” and “Oita” with the authors' institutions as the sources state them (University at Buffalo; Oita University, Yufu), dropped the unsupported claim that the patient reported the 88% reading, and changed the kind of event from a specific harm prevented to a benefit created or restored because the source shows a changed diagnostic course rather than an averted specified harm, in line with AIBD-0021 and AIBD-0025.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9494381/ ; https://europepmc.org/article/PMC/PMC12511463", "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": "Healthcare (emergency and respiratory medicine)", "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": "case report", "citation": "Shiota S, Horinouchi Y, Doi E, Miyazaki E (2022). Suspicion of pulmonary embolism after COVID-19 infection raised by a smartwatch. QJM, hcac217 (online 5 Sep 2022; issue 2023).", "title": "Suspicion of pulmonary embolism after COVID-19 infection raised by a smartwatch", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9494381/", "date": "2022-09-05", "written_by": "independent", "quote": "The smartwatch correctly recognized a low SpO2, which led to the diagnosis of PE in our case." }, { "kind": "case report", "citation": "Tulisiak LN, Loesing ME, Ljungberg A, Kaczor EE (2025). Smartwatch Detection of Refractory Hypoxemia in Phenazopyridine-Induced Methemoglobinemia: A Case Report. Journal of Medical Toxicology 21(4):430-433.", "title": "Smartwatch Detection of Refractory Hypoxemia in Phenazopyridine-Induced Methemoglobinemia", "url": "https://europepmc.org/article/PMC/PMC12511463", "date": "2025-08-04", "written_by": "independent", "quote": "The day before presentation, she was walking in the mall when she was notified by her Series 6 Apple Watch© (version 9.6.3) that her oxygen level was low." } ] } }, "provenance": { "kind": "legacy_record", "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-0022-source-0001", "AIBD-0022-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": "case report", "citation": "Shiota S, Horinouchi Y, Doi E, Miyazaki E (2022). Suspicion of pulmonary embolism after COVID-19 infection raised by a smartwatch. QJM, hcac217 (online 5 Sep 2022; issue 2023).", "title": "Suspicion of pulmonary embolism after COVID-19 infection raised by a smartwatch", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9494381/", "date": "2022-09-05", "written_by": "independent", "quote": "The smartwatch correctly recognized a low SpO2, which led to the diagnosis of PE in our case." }, { "kind": "case report", "citation": "Tulisiak LN, Loesing ME, Ljungberg A, Kaczor EE (2025). Smartwatch Detection of Refractory Hypoxemia in Phenazopyridine-Induced Methemoglobinemia: A Case Report. Journal of Medical Toxicology 21(4):430-433.", "title": "Smartwatch Detection of Refractory Hypoxemia in Phenazopyridine-Induced Methemoglobinemia", "url": "https://europepmc.org/article/PMC/PMC12511463", "date": "2025-08-04", "written_by": "independent", "quote": "The day before presentation, she was walking in the mall when she was notified by her Series 6 Apple Watch© (version 9.6.3) that her oxygen level was low." } ] } }, "provenance": { "kind": "legacy_record", "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 Series 7 SpO2 of 88% prompted CT angiography that found pulmonary embolism; anticoagulated and discharged (Japan).", "account": "A case appeared online on 5 September 2022 in QJM (event date not reported). A 50-year-old woman who recently had COVID-19 was referred to the authors' hospital (Oita University, Yufu, Japan) for assessment of dyspnea. Hospital pulse oximetry read 97%, but her Apple Watch Series 7 had shown 88% oxygen saturation in the early morning. Prompted by the watch reading, clinicians did emergent computed tomography (CT) angiography. It showed filling defects in multiple pulmonary artery branches, confirming pulmonary embolism with bilateral deep venous thrombosis (DVT). She was treated with a direct oral anticoagulant, oxygen was stopped on day 6 and she was discharged on day 15. The authors state the watch's low reading led to the diagnosis." } }, "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 estimated blood-oxygen saturation from optical measurements." }, "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 event involved an Apple Watch Series 7. A later report involved a Series 6." }, "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": "Apple Watch Series 7 (event); Series 6 in echo" } }, "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 Blood Oxygen 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": "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 Blood Oxygen reading leading to diagnosis and treatment of pulmonary embolism", "headline": "Apple Watch SpO2 of 88% prompts CT that finds pulmonary embolism" } }, "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": [ "37aad5184fa2ebfaedddf37a1f064ea271a76fecfb119919ae17d8d00138ffef" ], "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 case appeared online on 5 September 2022 in QJM (event date not reported). A 50-year-old woman who recently had COVID-19 was referred to the authors' hospital (Oita University, Yufu, Japan) for assessment of dyspnea. Hospital pulse oximetry read 97%, but her Apple Watch Series 7 had shown 88% oxygen saturation in the early morning. Prompted by the watch reading, clinicians did emergent computed tomography (CT) angiography. It showed filling defects in multiple pulmonary artery branches, confirming pulmonary embolism with bilateral deep venous thrombosis (DVT). She was treated with a direct oral anticoagulant, oxygen was stopped on day 6 and she was discharged on day 15. The authors state the watch's low reading led to the diagnosis.", "replication": { "produced_by": [ "The wearer has an Apple Watch with the Blood Oxygen app and reviews its readings", "A hypoxemic condition produces a persistently low reading at home", "The wearer reports the reading and a clinician acts on it despite normal spot oximetry" ], "must_hold": [ "The reading is not dismissed as artifact", "Confirmatory testing (CT angiography, co-oximetry) is available", "Effective treatment exists for the condition found" ], "echoes": [ { "date": "2025-08-04", "setting": "71-year-old woman, emergency department, USA (authors' institution: University at Buffalo); her watch notified her of low oxygen and read 73–86% over the next 24 hours, but she ignored the alert and sought care only when her family saw blue discoloration of her skin; hospital pulse oximetry itself read 84% and venous methaemoglobin was 19.8% from phenazopyridine; methylene blue, discharged after about 24 hours; the watch readings corroborated the hospital finding rather than bringing her to care, so the index mechanism did not repeat (publication date)", "model_version": "Apple Watch Series 6", "outcome": "did not recur", "source": "Tulisiak et al. (2025). J Med Toxicol 21(4):430-433. https://europepmc.org/article/PMC/PMC12511463" } ] } } }, "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-0026", "case_url": "records/AIBD-0026.html", "reason": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Jamshedpur, India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "record": { "id": "AIBD-0026", "title": "Fitbit Sense ECG detection leading to diagnosis and treatment of ventricular tachycardia", "summary": "Fitbit Sense flagged an irregular 164 bpm rhythm; fascicular VT was diagnosed and controlled with verapamil (India, 2023).", "date_observed": "2023-07-18", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A case report was published in Cureus on 18 July 2023 (event date not reported). A 55-year-old man in Jamshedpur, India, had symptoms at work. His Fitbit Sense watch detected an irregular heart rate of 164 beats per minute with irregular rhythm and bizarre QRS morphology. He sought care at Tata Main Hospital. A 12-lead electrocardiogram (ECG) showed tachycardia at 166 beats per minute with bizarre QRS complexes under 130 ms and right axis deviation. Idiopathic fascicular ventricular tachycardia (VT) was diagnosed. Holter monitoring, echocardiography and coronary angiography excluded structural heart disease. He got oral verapamil and telmisartan. After four cardiology visits he was reported to be doing well with no further complaints.", "category": "benefit created", "unexpected": false, "purpose": "The Fitbit Sense ECG app records a single-lead ECG to assess heart rhythm (its clearance is for atrial fibrillation). Capturing an abnormal rhythm for medical review was its purpose, although VT is outside its validated classification.", "system": "Fitbit Sense ECG app and heart-rate monitoring", "model_version": "Fitbit Sense", "developer": "Fitbit (Google)", "deployer": null, "location": "Jamshedpur, India", "domain": "Healthcare (cardiology)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "One man received a confirmed diagnosis of fascicular ventricular tachycardia and effective medical control. A material benefit to one person." } ], "ai_role": { "status": "assessed", "statement": "The watch's rhythm analysis flagged an irregular 164 bpm rhythm with abnormal QRS morphology, which sent the patient to hospital. The diagnosis and treatment were made by cardiologists from a 12-lead ECG and further tests." }, "evidence": [ { "kind": "case report", "citation": "Kumar S, Banerjee A (2023). Artificial Intelligence-Enabled Smartwatch Used for the Detection of Idiopathic Ventricular Tachycardia: A Case Report. Cureus 15:e42054.", "title": "Artificial Intelligence-Enabled Smartwatch Used for the Detection of Idiopathic Ventricular Tachycardia", "url": "https://europepmc.org/article/PMC/PMC10432923", "date": "2023-07-18", "written_by": "independent", "quote": "detected an irregular heart rate of 164 beats per minute along with irregular rhythm and bizarre QRS wave morphology" } ], "replication": { "produced_by": [ "The wearer has a Fitbit with the ECG app or heart-rate alerts", "A symptomatic tachyarrhythmia occurs while the device is worn and the result prompts care-seeking", "Clinicians confirm the rhythm and start treatment" ], "must_hold": [ "The device result is taken to a clinician promptly", "Confirmatory ECG and structural work-up are available", "Appropriate therapy is accessible" ], "echoes": [] }, "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": "No Fitbit incident exists; the nearest wearable false alarm is Apple Watch Crash Detection misclassifying skiing and dialing 911, harming watch users and dispatchers, the inverse of Fitbit Sense correctly flagging 164 bpm ventricular tachycardia." }, "headline": "Fitbit Sense flags a 164 bpm rhythm, leading to a VT diagnosis", "metric": "detected an irregular heart rate of 164 beats per minute with irregular rhythm", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The only cited source is the case report itself; no later development 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 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "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://support.google.com/product-documentation/answer/14816019", "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": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent case report. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0026", "catalog_version": "1.0.0", "source_fingerprint": "6f5c6804b6d93c5a7e22fac42f65e47f0b37d78df9dd171f32df21f8d5d21c65", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0026-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": "Fitbit (Google)" } }, "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 rhythm analysis flagged an irregular 164 bpm rhythm with abnormal QRS morphology, which sent the patient to hospital. 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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": "detected an irregular heart rate of 164 beats per minute with irregular rhythm", "editor_note": "" } }, 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flagging 164 bpm ventricular tachycardia." } } }, "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": "The only cited source is the case report itself; 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-0026" } }, "provenance": { "kind": 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"value_present", "value": { "kind": "record_fields", "fields": { "account": "A case report was published in Cureus on 18 July 2023 (event date not reported). A 55-year-old man in Jamshedpur, India, had symptoms at work. His Fitbit Sense watch detected an irregular heart rate of 164 beats per minute with irregular rhythm and bizarre QRS morphology. He sought care at Tata Main Hospital. A 12-lead electrocardiogram (ECG) showed tachycardia at 166 beats per minute with bizarre QRS complexes under 130 ms and right axis deviation. Idiopathic fascicular ventricular tachycardia (VT) was diagnosed. Holter monitoring, echocardiography and coronary angiography excluded structural heart disease. He got oral verapamil and telmisartan. After four cardiology visits he was reported to be doing well with no further complaints.", "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": "One man received a confirmed diagnosis of fascicular ventricular tachycardia and effective medical control. A material benefit to one person." } ] } }, "provenance": { "kind": "legacy_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 Fitbit Sense ECG app records a single-lead ECG to assess heart rhythm (its clearance is for atrial fibrillation). Capturing an abnormal rhythm for medical review was its purpose, although VT is outside its validated classification." } }, "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": "Jamshedpur, 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": "The Fitbit Sense ECG app records a single-lead ECG to assess heart rhythm (its clearance is for atrial fibrillation). Capturing an abnormal rhythm for medical review was its purpose, although VT is outside its validated classification." } }, "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": "2023-07-18", "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 463 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "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://support.google.com/product-documentation/answer/14816019", "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": [ "The wearer has a Fitbit with the ECG app or heart-rate alerts", "A symptomatic tachyarrhythmia occurs while the device is worn and the result prompts care-seeking", "Clinicians confirm the rhythm and start treatment" ], "must_hold": [ "The device result is taken to a clinician promptly", "Confirmatory ECG and structural work-up are available", "Appropriate therapy is accessible" ], "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": "Paired with AI Incident Database incident 463 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "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://support.google.com/product-documentation/answer/14816019", "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 (cardiology)", "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": "case report", "citation": "Kumar S, Banerjee A (2023). Artificial Intelligence-Enabled Smartwatch Used for the Detection of Idiopathic Ventricular Tachycardia: A Case Report. Cureus 15:e42054.", "title": "Artificial Intelligence-Enabled Smartwatch Used for the Detection of Idiopathic Ventricular Tachycardia", "url": "https://europepmc.org/article/PMC/PMC10432923", "date": "2023-07-18", "written_by": "independent", "quote": "detected an irregular heart rate of 164 beats per minute along with irregular rhythm and bizarre QRS wave morphology" } ] } }, "provenance": { "kind": "legacy_record", "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-0026-source-0001" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "Kumar S, Banerjee A (2023). Artificial Intelligence-Enabled Smartwatch Used for the Detection of Idiopathic Ventricular Tachycardia: A Case Report. Cureus 15:e42054.", "title": "Artificial Intelligence-Enabled Smartwatch Used for the Detection of Idiopathic Ventricular Tachycardia", "url": "https://europepmc.org/article/PMC/PMC10432923", "date": "2023-07-18", "written_by": "independent", "quote": "detected an irregular heart rate of 164 beats per minute along with irregular rhythm and bizarre QRS wave morphology" } ] } }, "provenance": { "kind": "legacy_record", "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": "Fitbit Sense flagged an irregular 164 bpm rhythm; fascicular VT was diagnosed and controlled with verapamil (India, 2023).", "account": "A case report was published in Cureus on 18 July 2023 (event date not reported). A 55-year-old man in Jamshedpur, India, had symptoms at work. His Fitbit Sense watch detected an irregular heart rate of 164 beats per minute with irregular rhythm and bizarre QRS morphology. He sought care at Tata Main Hospital. A 12-lead electrocardiogram (ECG) showed tachycardia at 166 beats per minute with bizarre QRS complexes under 130 ms and right axis deviation. Idiopathic fascicular ventricular tachycardia (VT) was diagnosed. Holter monitoring, echocardiography and coronary angiography excluded structural heart disease. He got oral verapamil and telmisartan. After four cardiology visits he was reported to be doing well with no further complaints." } }, "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 analyzed cardiac rhythm and flagged an irregular reading." }, "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 a Fitbit Sense." }, "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": "Fitbit Sense" } }, "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": "Fitbit Sense ECG app and heart-rate monitoring" } }, "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": "Fitbit (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": "Fitbit Sense ECG detection leading to diagnosis and treatment of ventricular tachycardia", "headline": "Fitbit Sense flags a 164 bpm rhythm, leading to a VT diagnosis" } }, "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": [ "6f5c6804b6d93c5a7e22fac42f65e47f0b37d78df9dd171f32df21f8d5d21c65" ], "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 case report was published in Cureus on 18 July 2023 (event date not reported). A 55-year-old man in Jamshedpur, India, had symptoms at work. His Fitbit Sense watch detected an irregular heart rate of 164 beats per minute with irregular rhythm and bizarre QRS morphology. He sought care at Tata Main Hospital. A 12-lead electrocardiogram (ECG) showed tachycardia at 166 beats per minute with bizarre QRS complexes under 130 ms and right axis deviation. Idiopathic fascicular ventricular tachycardia (VT) was diagnosed. Holter monitoring, echocardiography and coronary angiography excluded structural heart disease. He got oral verapamil and telmisartan. After four cardiology visits he was reported to be doing well with no further complaints.", "replication": { "produced_by": [ "The wearer has a Fitbit with the ECG app or heart-rate alerts", "A symptomatic tachyarrhythmia occurs while the device is worn and the result prompts care-seeking", "Clinicians confirm the rhythm and start treatment" ], "must_hold": [ "The device result is taken to a clinician promptly", "Confirmatory ECG and structural work-up are available", "Appropriate therapy is accessible" ], "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-0027", "case_url": "records/AIBD-0027.html", "reason": "The developer is headquartered in United States; the case names Switzerland.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Switzerland" }, { "label": "Case location", "value": "Zurich, Switzerland" } ], "filters": { "origin": "United States", "destination": [ "Switzerland" ] }, "record": { "id": "AIBD-0027", "title": "Fitbit Alta HR resting heart rate trend leading to early diagnosis and treatment of large vessel vasculitis", "summary": "Fitbit Alta HR resting-heart-rate rise from 55 to 75 bpm led to early diagnosis and treatment of large vessel vasculitis (Zurich).", "date_observed": "2022-02-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A case report appeared in BMJ Case Reports on 28 February 2022 (event dates not reported). A 57-year-old woman in Zurich, Switzerland, noticed in her Fitbit Alta HR application a persistent rise in resting heart rate. It went from about 55 to about 75 beats per minute over three months, alongside non-specific constitutional symptoms. The elevated resting heart rate led her to seek advice at University Hospital Zurich. FDG-PET with computed tomography (CT) showed inflammation of the aorta and both subclavian and axillary arteries, consistent with large vessel vasculitis. Treatment with prednisone 50 mg daily and tocilizumab produced rapid improvement. Her resting heart rate normalized within 10 days and inflammatory markers within two weeks.", "category": "benefit created", "unexpected": true, "purpose": "The Fitbit Alta HR computes resting heart rate for fitness tracking; it was not built to detect vasculitis, so this benefit was not its purpose.", "system": "Fitbit Alta HR resting heart rate (Fitbit app)", "model_version": "Fitbit Alta HR", "developer": "Fitbit (Google)", "deployer": null, "location": "Zurich, Switzerland", "domain": "Healthcare (rheumatology)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "One woman obtained an earlier diagnosis of giant cell arteritis with large vessel involvement and prompt immunosuppressive treatment that resolved her symptoms. A material benefit to one person." } ], "ai_role": { "status": "assessed", "statement": "The device's resting-heart-rate algorithm produced the trend the patient noticed; there was no alert, and the patient and clinicians drew the inference. The authors credit the elevated resting heart rate with an earlier diagnosis." }, "evidence": [ { "kind": "case report", "citation": "Gkikopoulos N, Wenger M, Distler O, Becker M (2022). Self-monitoring of the resting heart rate using a fitness tracker smartwatch application leads to an early diagnosis of large vessel vasculitis. BMJ Case Reports 15(2):e245021.", "title": "Self-monitoring of the resting heart rate using a fitness tracker smartwatch application leads to an early diagnosis of large vessel vasculitis", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8886383/", "date": "2022-02-28", "written_by": "independent", "quote": "The persistently elevated RHR led the patient to seek medical advice and contributed to an earlier diagnosis of a potentially severe disease." }, { "kind": "case report", "citation": "Gkikopoulos N et al. (2022). BMJ Case Reports 15(2):e245021.", "title": "Self-monitoring of the resting heart rate using a fitness tracker smartwatch application leads to an early diagnosis of large vessel vasculitis", "url": "https://doi.org/10.1136/bcr-2021-245021", "date": "2022-02-28", "written_by": "independent", "quote": "Treatment with glucocorticoids and tocilizumab led to a significant improvement of her symptoms and decrease in inflammatory parameters." } ], "replication": { "produced_by": [ "The wearer tracks resting heart rate over months with a Fitbit", "A systemic inflammatory illness raises resting heart rate persistently", "The wearer brings the trend to a clinician who pursues imaging" ], "must_hold": [ "A stable personal baseline exists", "The clinician treats the trend as a signal rather than noise", "Diagnostic imaging and immunosuppressive therapy are available" ], "echoes": [] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1666", "title": "Oura Smart Ring Purchaser in California Alleged Company Overstated AI Sleep-Tracking Accuracy, Leading to Overpayment", "url": "https://incidentdatabase.ai/cite/1666", "severity": null, "group": "personal", "basis": "Consumer wearable health metric read in its app: a Fitbit resting-heart-rate trend proved true, while Oura's inferred sleep stages were allegedly less accurate than marketed, harming ring purchasers who relied on them." }, "headline": "Fitbit Alta HR heart-rate rise leads to vasculitis diagnosis", "metric": "resting heart rate had risen persistently from about 55 to about 75 beats per minute", "comparator": null, "later": { "status": "unknown", "date": null, "note": "Both cited entries are the same case report; no later development 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 1666 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1666", "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": "Country of the developer added from the organization's own site or a registry.", "source": "https://support.google.com/product-documentation/answer/14816019", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account 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: two independent case reports. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0027", "catalog_version": "1.0.0", "source_fingerprint": "eeda0aac83a83a6d1cf0582ba329fde1920299f343bbb75b4ebf22380d42f449", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0027-source-0001", "AIBD-0027-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": "Fitbit (Google)" } }, "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 device's resting-heart-rate algorithm produced the trend the patient noticed; there was no alert, and the patient and clinicians drew the inference. 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The authors credit the elevated resting heart rate with an earlier diagnosis." } } }, "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": "One woman obtained an earlier diagnosis of giant cell arteritis with large vessel involvement and prompt immunosuppressive treatment that resolved her symptoms. A material benefit to one person." } ] } }, "provenance": { "kind": "legacy_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: two independent case 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": "personal", "depth": 2, "reason": "One woman obtained an earlier diagnosis of giant cell arteritis with large vessel involvement and prompt immunosuppressive treatment that resolved her symptoms. 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No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Fitbit Alta HR" } }, "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": "Fitbit Alta HR resting heart rate (Fitbit 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": "Fitbit (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": "Fitbit Alta HR resting heart rate trend leading to early diagnosis and treatment of large vessel vasculitis", "headline": "Fitbit Alta HR heart-rate rise leads to vasculitis diagnosis" } }, "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": [ "eeda0aac83a83a6d1cf0582ba329fde1920299f343bbb75b4ebf22380d42f449" ], "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 case report appeared in BMJ Case Reports on 28 February 2022 (event dates not reported). A 57-year-old woman in Zurich, Switzerland, noticed in her Fitbit Alta HR application a persistent rise in resting heart rate. It went from about 55 to about 75 beats per minute over three months, alongside non-specific constitutional symptoms. The elevated resting heart rate led her to seek advice at University Hospital Zurich. FDG-PET with computed tomography (CT) showed inflammation of the aorta and both subclavian and axillary arteries, consistent with large vessel vasculitis. Treatment with prednisone 50 mg daily and tocilizumab produced rapid improvement. Her resting heart rate normalized within 10 days and inflammatory markers within two weeks.", "replication": { "produced_by": [ "The wearer tracks resting heart rate over months with a Fitbit", "A systemic inflammatory illness raises resting heart rate persistently", "The wearer brings the trend to a clinician who pursues imaging" ], "must_hold": [ "A stable personal baseline exists", "The clinician treats the trend as a signal rather than noise", "Diagnostic imaging and immunosuppressive therapy are available" ], "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-0021", "case_url": "records/AIBD-0021.html", "reason": "The developer is headquartered in United States; the case names United Kingdom.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "United Kingdom" }, { "label": "Case location", "value": "United Kingdom" } ], "filters": { "origin": "United States", "destination": [ "United Kingdom" ] }, "record": { "id": "AIBD-0021", "title": "Apple Watch heart-rate, irregular-rhythm and ECG alerts leading to arrhythmia diagnosis and treatment", "summary": "Apple Watch heart-rate alarms led to an ECG-confirmed AF diagnosis and anticoagulation (UK, 2019); 16 echoes from 15 published case reports to 2026.", "date_observed": "2019-03-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In a case report of 29 March 2019 (event date not reported), a 59-year-old woman attended a United Kingdom emergency department. The acute medicine service saw her (the report does not name the hospital; the author is at Ipswich Hospital). She attended because her Apple Watch Series 2 had alarmed several times overnight that her pulse exceeded 120 bpm while she was inactive. A transient ischemic attack had occurred two months earlier. An electrocardiogram (ECG) showed atrial fibrillation with a fast ventricular response. She started bisoprolol and a direct oral anticoagulant, and clopidogrel was stopped. Later case reports describe the watch's heart-rate alerts, irregular-rhythm notification or ECG app leading to confirmed atrial flutter, supraventricular and ventricular tachycardia and heart block. Treatment was by cardioversion, ablation, pacemaker or defibrillator.", "category": "benefit created", "unexpected": false, "purpose": "Apple Watch high/low heart-rate notifications, the Irregular Rhythm Notification and the ECG app are built to flag possible arrhythmias so the wearer seeks medical review. This benefit was that purpose.", "system": "Apple Watch heart-rate notifications, Irregular Rhythm Notification and ECG app", "model_version": "Apple Watch Series 2 (event); echoes name Series 3, 4, 5, 6 and Ultra; eight reports do not state the model, and in Ahmed et al. the alerting tracker is unnamed (the Series 5 was the authors' post-diagnosis device)", "developer": "Apple", "deployer": null, "location": "United Kingdom", "domain": "Healthcare (cardiology)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "One woman with a recent TIA received an ECG-confirmed AF diagnosis, rate control and anticoagulation. A material benefit to one person. Several echoes (VT ablation with defibrillator, pacemaker for heart block) are deeper but are not the event." } ], "ai_role": { "status": "assessed", "statement": "The watch's heart-rate algorithm generated the overnight alarms that brought the patient to hospital. Clinicians made the diagnosis on an ECG and chose treatment. In the echoes the notification or the ECG app's single-lead trace likewise triggered evaluation, with clinicians confirming the rhythm." }, "evidence": [ { "kind": "case report", "citation": "Weichert I (2019). 'My watch kept on alarming all night about my heart rate': diagnosis of asymptomatic atrial fibrillation with fast ventricular response in a patient with a recent TIA as the result of a smartwatch alarm. Oxford Medical Case Reports 2019(3):omz014.", "title": "'My watch kept on alarming all night about my heart rate'", "url": "https://europepmc.org/article/PMC/PMC6440268", "date": "2019-03-29", "written_by": "independent", "quote": "Her smartwatch had alarmed several times during the preceding night that her pulse rate exceeded 120 beats per minute (bpm) whilst she was inactive" }, { "kind": "peer-reviewed study", "citation": "Bogár B, Pető D, Sipos D, Füredi G, Keszthelyi A, Betlehem J, Pandur AA (2024). Detection of Arrhythmias Using Smartwatches—A Systematic Literature Review. Healthcare 12(9):892.", "title": "Detection of Arrhythmias Using Smartwatches—A Systematic Literature Review", "url": "https://europepmc.org/article/PMC/PMC11083549", "date": "2024-04-25", "written_by": "independent", "quote": "In most cases (n = 30), the Apple Watch was used, while Samsung Galaxy Fit was used for arrhythmia registration in one patient." }, { "kind": "case report", "citation": "Overbeek DL, Hogikyan EM, Davis M, McGillicuddy DC (2019). A unique case of bradycardia recognized by wearable technology as first presentation of complete heart block. American Journal of Emergency Medicine 37(10):1989.e5-e7.", "title": "A unique case of bradycardia recognized by wearable technology as first presentation of complete heart block", "url": "https://www.sciencedirect.com/science/article/abs/pii/S0735675719304747", "date": "2019-07-21", "written_by": "independent", "quote": "A previously-healthy mid 60s-year-old-woman presented to the Emergency Department (ED) stating that her 'Apple Watch' wearable device was alarming due to a low heart rate." }, { "kind": "case report", "citation": "Yerasi C, O'Donoghue S, Satler LF, Waksman R (2020). Apple Watch detecting high-grade block after transcatheter aortic valve implantation. European Heart Journal 41(10):1096.", "title": "Apple Watch detecting high-grade block after transcatheter aortic valve implantation", "url": "https://academic.oup.com/eurheartj/article/41/10/1096/5651110", "date": "2019-12-03", "written_by": "independent", "quote": "He subsequently underwent urgent biventricular pacemaker implantation" }, { "kind": "case report", "citation": "Goldstein LN, Wells M (2019). Smart watch-detected tachycardia: a case of atrial flutter. Oxford Medical Case Reports 2019(12):omz137.", "title": "Smart watch-detected tachycardia: a case of atrial flutter", "url": "https://academic.oup.com/omcr/article/2019/12/495/5691267", "date": "2019-12-31", "written_by": "independent", "quote": "a consistent rate of 150 bpm for the previous 4 days" }, { "kind": "case report", "citation": "Siddeek H, Fisher K, McMakin S, Bass JL, Cortez D (2020). AVNRT captured by Apple Watch Series 4: Can the Apple watch be used as an event monitor? Annals of Noninvasive Electrocardiology 25:e12742.", "title": "AVNRT captured by Apple Watch Series 4", "url": "https://europepmc.org/article/PMC/PMC7507696", "date": "2020-01-29", "written_by": "independent", "quote": "captured an episode on the Apple watch that demonstrated tachycardia at a rate of about 225 bpm" }, { "kind": "case report", "citation": "Samal S, Singhania N, Bansal S, Sahoo A (2020). New-onset atrial fibrillation in a young patient detected by smartwatch. Clinical Case Reports 8(7).", "title": "New-onset atrial fibrillation in a young patient detected by smartwatch", "url": "https://europepmc.org/article/PMC/PMC7364102", "date": "2020-04-23", "written_by": "independent", "quote": "He was chemically cardioverted using intravenous flecainide per cardiology recommendations. He maintained sinus rhythm afterward and was discharged home on metoprolol and apixaban." }, { "kind": "case report", "citation": "Ahmed AS, Golden KM, Foreman JR, Padanilam BJ (2020). Using a smartwatch to identify the morphology of atrial flutter. HeartRhythm Case Reports 6.", "title": "Using a smartwatch to identify the morphology of atrial flutter", "url": "https://europepmc.org/article/PMC/PMC7573381", "date": "2020-06-28", "written_by": "independent", "quote": "ECG revealed typical atrial flutter with variable atrioventricular conduction" }, { "kind": "case report", "citation": "Burke J, Haigney MCP, Borne R, Krantz MJ (2020). Smartwatch detection of ventricular tachycardia: Case series. HeartRhythm Case Reports 6.", "title": "Smartwatch detection of ventricular tachycardia: Case series", "url": "https://europepmc.org/article/PMC/PMC7573479", "date": "2020-08-18", "written_by": "independent", "quote": "The patient has not experienced further symptoms suggestive of arrhythmia, nor has he reported detection of event recordings on his smartwatch" }, { "kind": "case report", "citation": "Patel D, Tarakji KG (2021). Smartwatch diagnosis of atrial fibrillation in patient with embolic stroke of unknown source: A case report. Cardiovascular Digital Health Journal 2(1):84-87.", "title": "Smartwatch diagnosis of atrial fibrillation in patient with embolic stroke of unknown source", "url": "https://europepmc.org/article/PMC/PMC8890063", "date": "2021-01-13", "written_by": "independent", "quote": "Given her elevated CHA2DS2VASC score of 5 (age, female, previous stroke, and hypertension), the patient was started on a direct oral anticoagulant, and the aspirin was stopped." }, { "kind": "case report", "citation": "Kassam N, Aziz O, Aghan E, Somji S, Mbithe H, Bapumia M, Mvungi R, Surani S (2021). Smart Watch Detection of Supraventricular Tachycardia (SVT): First Case from Tanzania. International Medical Case Reports Journal 14.", "title": "Smart Watch Detection of Supraventricular Tachycardia (SVT): First Case from Tanzania", "url": "https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ", "date": "2021-08-24", "written_by": "independent", "quote": "a high resting heart rate of 210 beats/min" }, { "kind": "case report", "citation": "Itoh A (2022). Apple Watch is useful in demonstrating coffee-triggered atrial fibrillation with a very short duration. European Heart Journal - Case Reports 6:ytac252.", "title": "Apple Watch is useful in demonstrating coffee-triggered atrial fibrillation", "url": "https://europepmc.org/article/PMC/PMC9269674", "date": "2022-06-28", "written_by": "independent", "quote": "After that, the patient stopped drinking coffee in the morning, and AF hardly occurred." }, { "kind": "case report", "citation": "Hawryszko M, Sławiński G, Kozłowski D, Lewicka E (2022). Apple Watch-guided diagnosis of AVNRT in a pregnant woman. Frontiers in Cardiovascular Medicine 9:985421.", "title": "Apple Watch-guided diagnosis of AVNRT in a pregnant woman", "url": "https://europepmc.org/article/PMC/PMC9676225", "date": "2022-11-07", "written_by": "independent", "quote": "narrow QRS complex tachycardia of 216 bpm, and short RP interval" }, { "kind": "case report", "citation": "Heringer GV, Vinson DR (2024). Utility of smart watch in expediting diagnosis of cold drink-triggered atrial fibrillation: a case report. International Journal of Emergency Medicine 17.", "title": "Utility of smart watch in expediting diagnosis of cold drink-triggered atrial fibrillation", "url": "https://europepmc.org/article/PMC/PMC11462712", "date": "2024-10-08", "written_by": "independent", "quote": "Had it not been for his smart watch, the etiology of his transitory palpitations would have remained undiagnosed." }, { "kind": "case report", "citation": "Guarnieri G, Mapelli M, Moltrasio M, Agostoni P, Tondo C (2024). Connected health: Ventricular tachycardia detection with Apple Watch—A case report. Heliyon 10:e40595.", "title": "Connected health: Ventricular tachycardia detection with Apple Watch", "url": "https://europepmc.org/article/PMC/PMC11625252", "date": "2024-11-21", "written_by": "independent", "quote": "An alert message of 'Elevated Heart rate' from his Apple Watch® prompted him to record a single-lead ECG using the smartwatch." }, { "kind": "case report", "citation": "Rushakoff JA, Flores Rosario K, Grover P, Wang A (2025). Smartwatch bradycardia alarms leading to a diagnosis of lamin A/C cardiomyopathy: a case report. European Heart Journal - Case Reports 9(9):ytaf415.", "title": "Smartwatch bradycardia alarms leading to a diagnosis of lamin A/C cardiomyopathy: a case report", "url": "https://europepmc.org/article/PMC/PMC12412439", "date": "2025-08-23", "written_by": "independent", "quote": "An asymptomatic 47-year-old man from Costa Rica presented to cardiology clinic for evaluation of recurrent bradycardia alarms on his smartwatch (Apple Watch)." }, { "kind": "case report", "citation": "Shroddha AD, Mukherjee I, Lambiase PD (2026). Apple Watch Saves an Athlete: Unmasking Early Detection of Arrhythmogenic Cardiomyopathy. Cureus 18:e113337.", "title": "Apple Watch Saves an Athlete: Unmasking Early Detection of Arrhythmogenic Cardiomyopathy", "url": "https://europepmc.org/article/PMC/PMC13501160", "date": "2026-07-25", "written_by": "independent", "quote": "In the early hours of Thursday, at 3:00 am, he awoke with frequent ectopic beats noted on his smartwatch single-lead ECG, which progressed to a broad complex tachycardia." } ], "replication": { "produced_by": [ "The wearer owns an Apple Watch with heart-rate notifications, Irregular Rhythm Notification or the ECG app enabled", "An arrhythmia occurs while the watch is worn and the alert or recording leads the wearer to seek care", "A clinician confirms the rhythm on a 12-lead ECG or monitoring and treats it" ], "must_hold": [ "The alert or trace reaches a clinician rather than being dismissed", "Confirmatory ECG or electrophysiology testing is available", "A treatment pathway (anticoagulation, cardioversion, ablation, device) exists" ], "echoes": [ { "date": "2019-07-21", "setting": "woman in her mid-60s, emergency department, USA; low heart-rate alarms; complete heart block, pacemaker implanted (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Overbeek et al. (2019). Am J Emerg Med 37(10):1989.e5. https://www.sciencedirect.com/science/article/abs/pii/S0735675719304747" }, { "date": "2019-12-03", "setting": "68-year-old man one year after TAVI, USA; ECG app recording after syncope showed high-grade AV block; urgent biventricular pacemaker (publication date)", "model_version": "Apple Watch (ECG app)", "outcome": "recurred", "source": "Yerasi et al. (2020). Eur Heart J 41(10):1096. https://academic.oup.com/eurheartj/article/41/10/1096/5651110" }, { "date": "2019-12-31", "setting": "56-year-old man, emergency department, South Africa; watch showed 150 bpm for four days; atrial flutter, anticoagulation and cardioversion (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Goldstein & Wells (2019). Oxf Med Case Reports 2019(12):omz137. https://academic.oup.com/omcr/article/2019/12/495/5691267" }, { "date": "2020-01-29", "setting": "16-year-old girl, Minneapolis, USA (authors' institution); watch captured 225 bpm and ECG; AVNRT ablated, asymptomatic at four months (publication date)", "model_version": "Apple Watch Series 4", "outcome": "recurred", "source": "Siddeek et al. (2020). Ann Noninvasive Electrocardiol 25:e12742. https://europepmc.org/article/PMC/PMC7507696" }, { "date": "2020-04-23", "setting": "39-year-old man, Milwaukee, USA (authors' institution); watch showed heart rate 60–130 bpm during palpitations; AF cardioverted with flecainide, metoprolol and apixaban (publication date)", "model_version": "Apple Watch Series 3", "outcome": "recurred", "source": "Samal et al. (2020). Clin Case Rep 8(7). https://europepmc.org/article/PMC/PMC7364102" }, { "date": "2020-06-28", "setting": "54-year-old woman with mechanical aortic valve, Pensacola, USA (authors' institution); alerted to elevated heart rates by an unnamed fitness tracker; typical atrial flutter on ECG; outpatient ablation planned, discharged with rate control and continued anticoagulation; the authors then used an Apple Watch Series 5 in a lead II configuration to confirm the flutter morphology in the ambulatory setting (publication date)", "model_version": "Apple Watch Series 5 (authors' post-diagnosis monitoring; alerting device unnamed)", "outcome": "unknown", "source": "Ahmed et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573381" }, { "date": "2020-08-18", "setting": "60-year-old man, Denver, USA (authors' institution); single-lead ECG showed wide complex tachycardia; ARVC with VT, ablation and ICD (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Burke et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573479" }, { "date": "2020-08-18", "setting": "63-year-old man, USA; watch recording labeled ventricular tachycardia; 9% VPC burden with NSVT, papillary muscle ablation (publication date)", "model_version": "smartwatch (brand not stated for this case; case 1 in the same series names an Apple Watch)", "outcome": "recurred", "source": "Burke et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573479" }, { "date": "2021-01-13", "setting": "70-year-old woman after embolic stroke of unknown source, Cleveland, USA (authors' institution); irregular-rhythm notifications and ECG app; AF, DOAC started, loop recorder avoided (publication date)", "model_version": "Apple Watch (Irregular Rhythm Notification and ECG app)", "outcome": "recurred", "source": "Patel & Tarakji (2021). Cardiovasc Digit Health J 2(1):84-87. https://europepmc.org/article/PMC/PMC8890063" }, { "date": "2021-08-24", "setting": "42-year-old man, Dar es Salaam, Tanzania (authors' institution); resting heart rate 210 bpm; SVT (orthodromic AVRT), cardioversion, medication; an ablation attempt had no effect and pathway ablation was declined; managed medically (publication date)", "model_version": "Apple Watch Series 4", "outcome": "recurred", "source": "Kassam et al. (2021). Int Med Case Rep J 14. https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ" }, { "date": "2022-06-28", "setting": "man in his early 60s, Shikokuchuo, Japan (authors' institution); ECG app recordings; coffee-triggered paroxysmal AF, trigger avoidance (publication date)", "model_version": "Apple Watch (ECG app)", "outcome": "recurred", "source": "Itoh (2022). Eur Heart J Case Rep 6:ytac252. https://europepmc.org/article/PMC/PMC9269674" }, { "date": "2022-11-07", "setting": "35-year-old woman at 20 weeks of pregnancy, Gdańsk, Poland (authors' institution); ECG app 216 bpm; AVNRT diagnosed, managed with Valsalva instruction without drugs (publication date)", "model_version": "Apple Watch Series 6", "outcome": "recurred", "source": "Hawryszko et al. (2022). Front Cardiovasc Med 9:985421. https://europepmc.org/article/PMC/PMC9676225" }, { "date": "2024-10-08", "setting": "43-year-old man, urgent care, USA; ECG app reported signs of AFib; paroxysmal AF, trigger avoidance and aspirin, no recurrence (publication date)", "model_version": "Apple Watch Ultra", "outcome": "recurred", "source": "Heringer & Vinson (2024). Int J Emerg Med 17. https://europepmc.org/article/PMC/PMC11462712" }, { "date": "2024-11-21", "setting": "60-year-old man, Milan, Italy (authors' institution); “Elevated Heart rate” alert then ECG; sustained VT at 183 bpm, radiofrequency ablation (publication date)", "model_version": "Apple Watch Series 5", "outcome": "recurred", "source": "Guarnieri et al. (2024). Heliyon 10:e40595. https://europepmc.org/article/PMC/PMC11625252" }, { "date": "2025-08-23", "setting": "47-year-old asymptomatic man from Costa Rica, Duke University Medical Center, USA; recurrent low heart-rate alarms; LMNA cardiomyopathy, CRT-D implanted, ejection fraction improved (publication date)", "model_version": "Apple Watch (low heart-rate notification)", "outcome": "recurred", "source": "Rushakoff et al. (2025). Eur Heart J Case Rep 9(9):ytaf415. https://europepmc.org/article/PMC/PMC12412439" }, { "date": "2026-07-25", "setting": "33-year-old male footballer, London, UK; single-lead ECG showed ectopy progressing to broad complex tachycardia; arrhythmogenic cardiomyopathy, S-ICD, flecainide and nadolol (publication date)", "model_version": "Apple Watch (model not stated)", "outcome": "recurred", "source": "Shroddha et al. (2026). Cureus 18:e113337. https://europepmc.org/article/PMC/PMC13501160" } ] }, "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 device: Apple Watch automatic sensor alerts; Crash Detection misclassified skiing as accidents and placed false 911 calls, burdening Apple Watch users, ski patrols and dispatchers, the inverse of a true heart-rate alarm sending one wearer to hospital." }, "headline": "Apple Watch alarms lead to an AF diagnosis and anticoagulation", "metric": "her pulse exceeded 120 beats per minute while she was inactive", "comparator": null, "later": { "status": "scaled", "date": "2024-04-25", "note": "A 2024 systematic review of smartwatch arrhythmia case reports found 44 patients across 33 articles, with the Apple Watch used in 30 of them.", "quote": "We selected 33 articles from case reports and case series detailing events involving a total of 44 patients. Most of the reports come from the United States (n = 12). In the selected articles, the youngest subject was 10 days old, and the oldest was 72 years old. In most cases (n = 30), the Apple Watch was used, while Samsung Galaxy Fit was used for arrhythmia registration in one patient.", "url": "https://europepmc.org/article/PMC/PMC11083549" }, "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 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Moved the location to “United Kingdom” and marked nine echo cities as the authors' institutions because none of those case texts names the place of care, removed the unsourced Series 9 and 11 from the model version, re-described the Ahmed echo (alert from an unnamed fitness tracker, Series 5 applied by the authors after diagnosis, ablation planned rather than done) with outcome “unknown”, labeled Burke case 2 as an unbranded smartwatch, replaced “partial ablation” in the Kassam echo with the failed attempt and declined pathway ablation, changed “12-lead ECG” to “ECG” for the index case, corrected the echo count to 16 from 15 reports, restored the published Rushakoff title and quoted the whole Patel sentence.", "source": "https://europepmc.org/article/PMC/PMC6440268 ; https://europepmc.org/article/PMC/PMC7573381 ; https://europepmc.org/article/PMC/PMC7573479 ; https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ ; https://europepmc.org/article/PMC/PMC8890063 ; https://europepmc.org/article/PMC/PMC12412439", "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://europepmc.org/article/PMC/PMC11083549", "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" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study and 16 independent case reports. 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This benefit 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": "2019-03-29", "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 463 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Moved the location to “United Kingdom” and marked nine echo cities as the authors' institutions because none of those case texts names the place of care, removed the unsourced Series 9 and 11 from the model version, re-described the Ahmed echo (alert from an unnamed fitness tracker, Series 5 applied by the authors after diagnosis, ablation planned rather than done) with outcome “unknown”, labeled Burke case 2 as an unbranded smartwatch, replaced “partial ablation” in the Kassam echo with the failed attempt and declined pathway ablation, changed “12-lead ECG” to “ECG” for the index case, corrected the echo count to 16 from 15 reports, restored the published Rushakoff title and quoted the whole Patel sentence.", "source": "https://europepmc.org/article/PMC/PMC6440268 ; https://europepmc.org/article/PMC/PMC7573381 ; https://europepmc.org/article/PMC/PMC7573479 ; https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ ; https://europepmc.org/article/PMC/PMC8890063 ; https://europepmc.org/article/PMC/PMC12412439", "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://europepmc.org/article/PMC/PMC11083549", "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" } ] } }, "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": [ "The wearer owns an Apple Watch with heart-rate notifications, Irregular Rhythm Notification or the ECG app enabled", "An arrhythmia occurs while the watch is worn and the alert or recording leads the wearer to seek care", "A clinician confirms the rhythm on a 12-lead ECG or monitoring and treats it" ], "must_hold": [ "The alert or trace reaches a clinician rather than being dismissed", "Confirmatory ECG or electrophysiology testing is available", "A treatment pathway (anticoagulation, cardioversion, ablation, device) exists" ], "echoes": [ { "date": "2019-07-21", "setting": "woman in her mid-60s, emergency department, USA; low heart-rate alarms; complete heart block, pacemaker implanted (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Overbeek et al. (2019). Am J Emerg Med 37(10):1989.e5. https://www.sciencedirect.com/science/article/abs/pii/S0735675719304747" }, { "date": "2019-12-03", "setting": "68-year-old man one year after TAVI, USA; ECG app recording after syncope showed high-grade AV block; urgent biventricular pacemaker (publication date)", "model_version": "Apple Watch (ECG app)", "outcome": "recurred", "source": "Yerasi et al. (2020). Eur Heart J 41(10):1096. https://academic.oup.com/eurheartj/article/41/10/1096/5651110" }, { "date": "2019-12-31", "setting": "56-year-old man, emergency department, South Africa; watch showed 150 bpm for four days; atrial flutter, anticoagulation and cardioversion (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Goldstein & Wells (2019). Oxf Med Case Reports 2019(12):omz137. https://academic.oup.com/omcr/article/2019/12/495/5691267" }, { "date": "2020-01-29", "setting": "16-year-old girl, Minneapolis, USA (authors' institution); watch captured 225 bpm and ECG; AVNRT ablated, asymptomatic at four months (publication date)", "model_version": "Apple Watch Series 4", "outcome": "recurred", "source": "Siddeek et al. (2020). Ann Noninvasive Electrocardiol 25:e12742. https://europepmc.org/article/PMC/PMC7507696" }, { "date": "2020-04-23", "setting": "39-year-old man, Milwaukee, USA (authors' institution); watch showed heart rate 60–130 bpm during palpitations; AF cardioverted with flecainide, metoprolol and apixaban (publication date)", "model_version": "Apple Watch Series 3", "outcome": "recurred", "source": "Samal et al. (2020). Clin Case Rep 8(7). https://europepmc.org/article/PMC/PMC7364102" }, { "date": "2020-06-28", "setting": "54-year-old woman with mechanical aortic valve, Pensacola, USA (authors' institution); alerted to elevated heart rates by an unnamed fitness tracker; typical atrial flutter on ECG; outpatient ablation planned, discharged with rate control and continued anticoagulation; the authors then used an Apple Watch Series 5 in a lead II configuration to confirm the flutter morphology in the ambulatory setting (publication date)", "model_version": "Apple Watch Series 5 (authors' post-diagnosis monitoring; alerting device unnamed)", "outcome": "unknown", "source": "Ahmed et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573381" }, { "date": "2020-08-18", "setting": "60-year-old man, Denver, USA (authors' institution); single-lead ECG showed wide complex tachycardia; ARVC with VT, ablation and ICD (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Burke et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573479" }, { "date": "2020-08-18", "setting": "63-year-old man, USA; watch recording labeled ventricular tachycardia; 9% VPC burden with NSVT, papillary muscle ablation (publication date)", "model_version": "smartwatch (brand not stated for this case; case 1 in the same series names an Apple Watch)", "outcome": "recurred", "source": "Burke et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573479" }, { "date": "2021-01-13", "setting": "70-year-old woman after embolic stroke of unknown source, Cleveland, USA (authors' institution); irregular-rhythm notifications and ECG app; AF, DOAC started, loop recorder avoided (publication date)", "model_version": "Apple Watch (Irregular Rhythm Notification and ECG app)", "outcome": "recurred", "source": "Patel & Tarakji (2021). Cardiovasc Digit Health J 2(1):84-87. https://europepmc.org/article/PMC/PMC8890063" }, { "date": "2021-08-24", "setting": "42-year-old man, Dar es Salaam, Tanzania (authors' institution); resting heart rate 210 bpm; SVT (orthodromic AVRT), cardioversion, medication; an ablation attempt had no effect and pathway ablation was declined; managed medically (publication date)", "model_version": "Apple Watch Series 4", "outcome": "recurred", "source": "Kassam et al. (2021). Int Med Case Rep J 14. https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ" }, { "date": "2022-06-28", "setting": "man in his early 60s, Shikokuchuo, Japan (authors' institution); ECG app recordings; coffee-triggered paroxysmal AF, trigger avoidance (publication date)", "model_version": "Apple Watch (ECG app)", "outcome": "recurred", "source": "Itoh (2022). Eur Heart J Case Rep 6:ytac252. https://europepmc.org/article/PMC/PMC9269674" }, { "date": "2022-11-07", "setting": "35-year-old woman at 20 weeks of pregnancy, Gdańsk, Poland (authors' institution); ECG app 216 bpm; AVNRT diagnosed, managed with Valsalva instruction without drugs (publication date)", "model_version": "Apple Watch Series 6", "outcome": "recurred", "source": "Hawryszko et al. (2022). Front Cardiovasc Med 9:985421. https://europepmc.org/article/PMC/PMC9676225" }, { "date": "2024-10-08", "setting": "43-year-old man, urgent care, USA; ECG app reported signs of AFib; paroxysmal AF, trigger avoidance and aspirin, no recurrence (publication date)", "model_version": "Apple Watch Ultra", "outcome": "recurred", "source": "Heringer & Vinson (2024). Int J Emerg Med 17. https://europepmc.org/article/PMC/PMC11462712" }, { "date": "2024-11-21", "setting": "60-year-old man, Milan, Italy (authors' institution); “Elevated Heart rate” alert then ECG; sustained VT at 183 bpm, radiofrequency ablation (publication date)", "model_version": "Apple Watch Series 5", "outcome": "recurred", "source": "Guarnieri et al. (2024). Heliyon 10:e40595. https://europepmc.org/article/PMC/PMC11625252" }, { "date": "2025-08-23", "setting": "47-year-old asymptomatic man from Costa Rica, Duke University Medical Center, USA; recurrent low heart-rate alarms; LMNA cardiomyopathy, CRT-D implanted, ejection fraction improved (publication date)", "model_version": "Apple Watch (low heart-rate notification)", "outcome": "recurred", "source": "Rushakoff et al. (2025). Eur Heart J Case Rep 9(9):ytaf415. https://europepmc.org/article/PMC/PMC12412439" }, { "date": "2026-07-25", "setting": "33-year-old male footballer, London, UK; single-lead ECG showed ectopy progressing to broad complex tachycardia; arrhythmogenic cardiomyopathy, S-ICD, flecainide and nadolol (publication date)", "model_version": "Apple Watch (model not stated)", "outcome": "recurred", "source": "Shroddha et al. (2026). Cureus 18:e113337. https://europepmc.org/article/PMC/PMC13501160" } ] } } }, "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": [ "The wearer owns an Apple Watch with heart-rate notifications, Irregular Rhythm Notification or the ECG app enabled", "An arrhythmia occurs while the watch is worn and the alert or recording leads the wearer to seek care", "A clinician confirms the rhythm on a 12-lead ECG or monitoring and treats it" ], "must_hold": [ "The alert or trace reaches a clinician rather than being dismissed", "Confirmatory ECG or electrophysiology testing is available", "A treatment pathway (anticoagulation, cardioversion, ablation, device) exists" ], "echoes": [ { "date": "2019-07-21", "setting": "woman in her mid-60s, emergency department, USA; low heart-rate alarms; complete heart block, pacemaker implanted (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Overbeek et al. (2019). Am J Emerg Med 37(10):1989.e5. https://www.sciencedirect.com/science/article/abs/pii/S0735675719304747" }, { "date": "2019-12-03", "setting": "68-year-old man one year after TAVI, USA; ECG app recording after syncope showed high-grade AV block; urgent biventricular pacemaker (publication date)", "model_version": "Apple Watch (ECG app)", "outcome": "recurred", "source": "Yerasi et al. (2020). Eur Heart J 41(10):1096. https://academic.oup.com/eurheartj/article/41/10/1096/5651110" }, { "date": "2019-12-31", "setting": "56-year-old man, emergency department, South Africa; watch showed 150 bpm for four days; atrial flutter, anticoagulation and cardioversion (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Goldstein & Wells (2019). Oxf Med Case Reports 2019(12):omz137. https://academic.oup.com/omcr/article/2019/12/495/5691267" }, { "date": "2020-01-29", "setting": "16-year-old girl, Minneapolis, USA (authors' institution); watch captured 225 bpm and ECG; AVNRT ablated, asymptomatic at four months (publication date)", "model_version": "Apple Watch Series 4", "outcome": "recurred", "source": "Siddeek et al. (2020). Ann Noninvasive Electrocardiol 25:e12742. https://europepmc.org/article/PMC/PMC7507696" }, { "date": "2020-04-23", "setting": "39-year-old man, Milwaukee, USA (authors' institution); watch showed heart rate 60–130 bpm during palpitations; AF cardioverted with flecainide, metoprolol and apixaban (publication date)", "model_version": "Apple Watch Series 3", "outcome": "recurred", "source": "Samal et al. (2020). Clin Case Rep 8(7). https://europepmc.org/article/PMC/PMC7364102" }, { "date": "2020-06-28", "setting": "54-year-old woman with mechanical aortic valve, Pensacola, USA (authors' institution); alerted to elevated heart rates by an unnamed fitness tracker; typical atrial flutter on ECG; outpatient ablation planned, discharged with rate control and continued anticoagulation; the authors then used an Apple Watch Series 5 in a lead II configuration to confirm the flutter morphology in the ambulatory setting (publication date)", "model_version": "Apple Watch Series 5 (authors' post-diagnosis monitoring; alerting device unnamed)", "outcome": "unknown", "source": "Ahmed et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573381" }, { "date": "2020-08-18", "setting": "60-year-old man, Denver, USA (authors' institution); single-lead ECG showed wide complex tachycardia; ARVC with VT, ablation and ICD (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Burke et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573479" }, { "date": "2020-08-18", "setting": "63-year-old man, USA; watch recording labeled ventricular tachycardia; 9% VPC burden with NSVT, papillary muscle ablation (publication date)", "model_version": "smartwatch (brand not stated for this case; case 1 in the same series names an Apple Watch)", "outcome": "recurred", "source": "Burke et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573479" }, { "date": "2021-01-13", "setting": "70-year-old woman after embolic stroke of unknown source, Cleveland, USA (authors' institution); irregular-rhythm notifications and ECG app; AF, DOAC started, loop recorder avoided (publication date)", "model_version": "Apple Watch (Irregular Rhythm Notification and ECG app)", "outcome": "recurred", "source": "Patel & Tarakji (2021). Cardiovasc Digit Health J 2(1):84-87. https://europepmc.org/article/PMC/PMC8890063" }, { "date": "2021-08-24", "setting": "42-year-old man, Dar es Salaam, Tanzania (authors' institution); resting heart rate 210 bpm; SVT (orthodromic AVRT), cardioversion, medication; an ablation attempt had no effect and pathway ablation was declined; managed medically (publication date)", "model_version": "Apple Watch Series 4", "outcome": "recurred", "source": "Kassam et al. (2021). Int Med Case Rep J 14. https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ" }, { "date": "2022-06-28", "setting": "man in his early 60s, Shikokuchuo, Japan (authors' institution); ECG app recordings; coffee-triggered paroxysmal AF, trigger avoidance (publication date)", "model_version": "Apple Watch (ECG app)", "outcome": "recurred", "source": "Itoh (2022). Eur Heart J Case Rep 6:ytac252. https://europepmc.org/article/PMC/PMC9269674" }, { "date": "2022-11-07", "setting": "35-year-old woman at 20 weeks of pregnancy, Gdańsk, Poland (authors' institution); ECG app 216 bpm; AVNRT diagnosed, managed with Valsalva instruction without drugs (publication date)", "model_version": "Apple Watch Series 6", "outcome": "recurred", "source": "Hawryszko et al. (2022). Front Cardiovasc Med 9:985421. https://europepmc.org/article/PMC/PMC9676225" }, { "date": "2024-10-08", "setting": "43-year-old man, urgent care, USA; ECG app reported signs of AFib; paroxysmal AF, trigger avoidance and aspirin, no recurrence (publication date)", "model_version": "Apple Watch Ultra", "outcome": "recurred", "source": "Heringer & Vinson (2024). Int J Emerg Med 17. https://europepmc.org/article/PMC/PMC11462712" }, { "date": "2024-11-21", "setting": "60-year-old man, Milan, Italy (authors' institution); “Elevated Heart rate” alert then ECG; sustained VT at 183 bpm, radiofrequency ablation (publication date)", "model_version": "Apple Watch Series 5", "outcome": "recurred", "source": "Guarnieri et al. (2024). Heliyon 10:e40595. https://europepmc.org/article/PMC/PMC11625252" }, { "date": "2025-08-23", "setting": "47-year-old asymptomatic man from Costa Rica, Duke University Medical Center, USA; recurrent low heart-rate alarms; LMNA cardiomyopathy, CRT-D implanted, ejection fraction improved (publication date)", "model_version": "Apple Watch (low heart-rate notification)", "outcome": "recurred", "source": "Rushakoff et al. (2025). Eur Heart J Case Rep 9(9):ytaf415. https://europepmc.org/article/PMC/PMC12412439" }, { "date": "2026-07-25", "setting": "33-year-old male footballer, London, UK; single-lead ECG showed ectopy progressing to broad complex tachycardia; arrhythmogenic cardiomyopathy, S-ICD, flecainide and nadolol (publication date)", "model_version": "Apple Watch (model not stated)", "outcome": "recurred", "source": "Shroddha et al. (2026). Cureus 18:e113337. https://europepmc.org/article/PMC/PMC13501160" } ] } } }, "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 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Moved the location to “United Kingdom” and marked nine echo cities as the authors' institutions because none of those case texts names the place of care, removed the unsourced Series 9 and 11 from the model version, re-described the Ahmed echo (alert from an unnamed fitness tracker, Series 5 applied by the authors after diagnosis, ablation planned rather than done) with outcome “unknown”, labeled Burke case 2 as an unbranded smartwatch, replaced “partial ablation” in the Kassam echo with the failed attempt and declined pathway ablation, changed “12-lead ECG” to “ECG” for the index case, corrected the echo count to 16 from 15 reports, restored the published Rushakoff title and quoted the whole Patel sentence.", "source": "https://europepmc.org/article/PMC/PMC6440268 ; https://europepmc.org/article/PMC/PMC7573381 ; https://europepmc.org/article/PMC/PMC7573479 ; https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ ; https://europepmc.org/article/PMC/PMC8890063 ; https://europepmc.org/article/PMC/PMC12412439", "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://europepmc.org/article/PMC/PMC11083549", "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" } ] } }, "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 (cardiology)", "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": "case report", "citation": "Weichert I (2019). 'My watch kept on alarming all night about my heart rate': diagnosis of asymptomatic atrial fibrillation with fast ventricular response in a patient with a recent TIA as the result of a smartwatch alarm. Oxford Medical Case Reports 2019(3):omz014.", "title": "'My watch kept on alarming all night about my heart rate'", "url": "https://europepmc.org/article/PMC/PMC6440268", "date": "2019-03-29", "written_by": "independent", "quote": "Her smartwatch had alarmed several times during the preceding night that her pulse rate exceeded 120 beats per minute (bpm) whilst she was inactive" }, { "kind": "peer-reviewed study", "citation": "Bogár B, Pető D, Sipos D, Füredi G, Keszthelyi A, Betlehem J, Pandur AA (2024). Detection of Arrhythmias Using Smartwatches—A Systematic Literature Review. Healthcare 12(9):892.", "title": "Detection of Arrhythmias Using Smartwatches—A Systematic Literature Review", "url": "https://europepmc.org/article/PMC/PMC11083549", "date": "2024-04-25", "written_by": "independent", "quote": "In most cases (n = 30), the Apple Watch was used, while Samsung Galaxy Fit was used for arrhythmia registration in one patient." }, { "kind": "case report", "citation": "Overbeek DL, Hogikyan EM, Davis M, McGillicuddy DC (2019). A unique case of bradycardia recognized by wearable technology as first presentation of complete heart block. American Journal of Emergency Medicine 37(10):1989.e5-e7.", "title": "A unique case of bradycardia recognized by wearable technology as first presentation of complete heart block", "url": "https://www.sciencedirect.com/science/article/abs/pii/S0735675719304747", "date": "2019-07-21", "written_by": "independent", "quote": "A previously-healthy mid 60s-year-old-woman presented to the Emergency Department (ED) stating that her 'Apple Watch' wearable device was alarming due to a low heart rate." }, { "kind": "case report", "citation": "Yerasi C, O'Donoghue S, Satler LF, Waksman R (2020). Apple Watch detecting high-grade block after transcatheter aortic valve implantation. European Heart Journal 41(10):1096.", "title": "Apple Watch detecting high-grade block after transcatheter aortic valve implantation", "url": "https://academic.oup.com/eurheartj/article/41/10/1096/5651110", "date": "2019-12-03", "written_by": "independent", "quote": "He subsequently underwent urgent biventricular pacemaker implantation" }, { "kind": "case report", "citation": "Goldstein LN, Wells M (2019). Smart watch-detected tachycardia: a case of atrial flutter. Oxford Medical Case Reports 2019(12):omz137.", "title": "Smart watch-detected tachycardia: a case of atrial flutter", "url": "https://academic.oup.com/omcr/article/2019/12/495/5691267", "date": "2019-12-31", "written_by": "independent", "quote": "a consistent rate of 150 bpm for the previous 4 days" }, { "kind": "case report", "citation": "Siddeek H, Fisher K, McMakin S, Bass JL, Cortez D (2020). AVNRT captured by Apple Watch Series 4: Can the Apple watch be used as an event monitor? Annals of Noninvasive Electrocardiology 25:e12742.", "title": "AVNRT captured by Apple Watch Series 4", "url": "https://europepmc.org/article/PMC/PMC7507696", "date": "2020-01-29", "written_by": "independent", "quote": "captured an episode on the Apple watch that demonstrated tachycardia at a rate of about 225 bpm" }, { "kind": "case report", "citation": "Samal S, Singhania N, Bansal S, Sahoo A (2020). New-onset atrial fibrillation in a young patient detected by smartwatch. Clinical Case Reports 8(7).", "title": "New-onset atrial fibrillation in a young patient detected by smartwatch", "url": "https://europepmc.org/article/PMC/PMC7364102", "date": "2020-04-23", "written_by": "independent", "quote": "He was chemically cardioverted using intravenous flecainide per cardiology recommendations. He maintained sinus rhythm afterward and was discharged home on metoprolol and apixaban." }, { "kind": "case report", "citation": "Ahmed AS, Golden KM, Foreman JR, Padanilam BJ (2020). Using a smartwatch to identify the morphology of atrial flutter. HeartRhythm Case Reports 6.", "title": "Using a smartwatch to identify the morphology of atrial flutter", "url": "https://europepmc.org/article/PMC/PMC7573381", "date": "2020-06-28", "written_by": "independent", "quote": "ECG revealed typical atrial flutter with variable atrioventricular conduction" }, { "kind": "case report", "citation": "Burke J, Haigney MCP, Borne R, Krantz MJ (2020). Smartwatch detection of ventricular tachycardia: Case series. HeartRhythm Case Reports 6.", "title": "Smartwatch detection of ventricular tachycardia: Case series", "url": "https://europepmc.org/article/PMC/PMC7573479", "date": "2020-08-18", "written_by": "independent", "quote": "The patient has not experienced further symptoms suggestive of arrhythmia, nor has he reported detection of event recordings on his smartwatch" }, { "kind": "case report", "citation": "Patel D, Tarakji KG (2021). Smartwatch diagnosis of atrial fibrillation in patient with embolic stroke of unknown source: A case report. Cardiovascular Digital Health Journal 2(1):84-87.", "title": "Smartwatch diagnosis of atrial fibrillation in patient with embolic stroke of unknown source", "url": "https://europepmc.org/article/PMC/PMC8890063", "date": "2021-01-13", "written_by": "independent", "quote": "Given her elevated CHA2DS2VASC score of 5 (age, female, previous stroke, and hypertension), the patient was started on a direct oral anticoagulant, and the aspirin was stopped." }, { "kind": "case report", "citation": "Kassam N, Aziz O, Aghan E, Somji S, Mbithe H, Bapumia M, Mvungi R, Surani S (2021). Smart Watch Detection of Supraventricular Tachycardia (SVT): First Case from Tanzania. International Medical Case Reports Journal 14.", "title": "Smart Watch Detection of Supraventricular Tachycardia (SVT): First Case from Tanzania", "url": "https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ", "date": "2021-08-24", "written_by": "independent", "quote": "a high resting heart rate of 210 beats/min" }, { "kind": "case report", "citation": "Itoh A (2022). Apple Watch is useful in demonstrating coffee-triggered atrial fibrillation with a very short duration. European Heart Journal - Case Reports 6:ytac252.", "title": "Apple Watch is useful in demonstrating coffee-triggered atrial fibrillation", "url": "https://europepmc.org/article/PMC/PMC9269674", "date": "2022-06-28", "written_by": "independent", "quote": "After that, the patient stopped drinking coffee in the morning, and AF hardly occurred." }, { "kind": "case report", "citation": "Hawryszko M, Sławiński G, Kozłowski D, Lewicka E (2022). Apple Watch-guided diagnosis of AVNRT in a pregnant woman. Frontiers in Cardiovascular Medicine 9:985421.", "title": "Apple Watch-guided diagnosis of AVNRT in a pregnant woman", "url": "https://europepmc.org/article/PMC/PMC9676225", "date": "2022-11-07", "written_by": "independent", "quote": "narrow QRS complex tachycardia of 216 bpm, and short RP interval" }, { "kind": "case report", "citation": "Heringer GV, Vinson DR (2024). Utility of smart watch in expediting diagnosis of cold drink-triggered atrial fibrillation: a case report. International Journal of Emergency Medicine 17.", "title": "Utility of smart watch in expediting diagnosis of cold drink-triggered atrial fibrillation", "url": "https://europepmc.org/article/PMC/PMC11462712", "date": "2024-10-08", "written_by": "independent", "quote": "Had it not been for his smart watch, the etiology of his transitory palpitations would have remained undiagnosed." }, { "kind": "case report", "citation": "Guarnieri G, Mapelli M, Moltrasio M, Agostoni P, Tondo C (2024). Connected health: Ventricular tachycardia detection with Apple Watch—A case report. Heliyon 10:e40595.", "title": "Connected health: Ventricular tachycardia detection with Apple Watch", "url": "https://europepmc.org/article/PMC/PMC11625252", "date": "2024-11-21", "written_by": "independent", "quote": "An alert message of 'Elevated Heart rate' from his Apple Watch® prompted him to record a single-lead ECG using the smartwatch." }, { "kind": "case report", "citation": "Rushakoff JA, Flores Rosario K, Grover P, Wang A (2025). Smartwatch bradycardia alarms leading to a diagnosis of lamin A/C cardiomyopathy: a case report. European Heart Journal - Case Reports 9(9):ytaf415.", "title": "Smartwatch bradycardia alarms leading to a diagnosis of lamin A/C cardiomyopathy: a case report", "url": "https://europepmc.org/article/PMC/PMC12412439", "date": "2025-08-23", "written_by": "independent", "quote": "An asymptomatic 47-year-old man from Costa Rica presented to cardiology clinic for evaluation of recurrent bradycardia alarms on his smartwatch (Apple Watch)." }, { "kind": "case report", "citation": "Shroddha AD, Mukherjee I, Lambiase PD (2026). Apple Watch Saves an Athlete: Unmasking Early Detection of Arrhythmogenic Cardiomyopathy. Cureus 18:e113337.", "title": "Apple Watch Saves an Athlete: Unmasking Early Detection of Arrhythmogenic Cardiomyopathy", "url": "https://europepmc.org/article/PMC/PMC13501160", "date": "2026-07-25", "written_by": "independent", "quote": "In the early hours of Thursday, at 3:00 am, he awoke with frequent ectopic beats noted on his smartwatch single-lead ECG, which progressed to a broad complex tachycardia." } ] } }, "provenance": { "kind": "legacy_record", "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-0021-source-0001", "AIBD-0021-source-0002", "AIBD-0021-source-0003", "AIBD-0021-source-0004", "AIBD-0021-source-0005", "AIBD-0021-source-0006", "AIBD-0021-source-0007", "AIBD-0021-source-0008", "AIBD-0021-source-0009", "AIBD-0021-source-0010", "AIBD-0021-source-0011", "AIBD-0021-source-0012", "AIBD-0021-source-0013", "AIBD-0021-source-0014", "AIBD-0021-source-0015", "AIBD-0021-source-0016", "AIBD-0021-source-0017" ], "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": "Weichert I (2019). 'My watch kept on alarming all night about my heart rate': diagnosis of asymptomatic atrial fibrillation with fast ventricular response in a patient with a recent TIA as the result of a smartwatch alarm. Oxford Medical Case Reports 2019(3):omz014.", "title": "'My watch kept on alarming all night about my heart rate'", "url": "https://europepmc.org/article/PMC/PMC6440268", "date": "2019-03-29", "written_by": "independent", "quote": "Her smartwatch had alarmed several times during the preceding night that her pulse rate exceeded 120 beats per minute (bpm) whilst she was inactive" }, { "kind": "peer-reviewed study", "citation": "Bogár B, Pető D, Sipos D, Füredi G, Keszthelyi A, Betlehem J, Pandur AA (2024). Detection of Arrhythmias Using Smartwatches—A Systematic Literature Review. Healthcare 12(9):892.", "title": "Detection of Arrhythmias Using Smartwatches—A Systematic Literature Review", "url": "https://europepmc.org/article/PMC/PMC11083549", "date": "2024-04-25", "written_by": "independent", "quote": "In most cases (n = 30), the Apple Watch was used, while Samsung Galaxy Fit was used for arrhythmia registration in one patient." }, { "kind": "case report", "citation": "Overbeek DL, Hogikyan EM, Davis M, McGillicuddy DC (2019). A unique case of bradycardia recognized by wearable technology as first presentation of complete heart block. American Journal of Emergency Medicine 37(10):1989.e5-e7.", "title": "A unique case of bradycardia recognized by wearable technology as first presentation of complete heart block", "url": "https://www.sciencedirect.com/science/article/abs/pii/S0735675719304747", "date": "2019-07-21", "written_by": "independent", "quote": "A previously-healthy mid 60s-year-old-woman presented to the Emergency Department (ED) stating that her 'Apple Watch' wearable device was alarming due to a low heart rate." }, { "kind": "case report", "citation": "Yerasi C, O'Donoghue S, Satler LF, Waksman R (2020). Apple Watch detecting high-grade block after transcatheter aortic valve implantation. European Heart Journal 41(10):1096.", "title": "Apple Watch detecting high-grade block after transcatheter aortic valve implantation", "url": "https://academic.oup.com/eurheartj/article/41/10/1096/5651110", "date": "2019-12-03", "written_by": "independent", "quote": "He subsequently underwent urgent biventricular pacemaker implantation" }, { "kind": "case report", "citation": "Goldstein LN, Wells M (2019). Smart watch-detected tachycardia: a case of atrial flutter. Oxford Medical Case Reports 2019(12):omz137.", "title": "Smart watch-detected tachycardia: a case of atrial flutter", "url": "https://academic.oup.com/omcr/article/2019/12/495/5691267", "date": "2019-12-31", "written_by": "independent", "quote": "a consistent rate of 150 bpm for the previous 4 days" }, { "kind": "case report", "citation": "Siddeek H, Fisher K, McMakin S, Bass JL, Cortez D (2020). AVNRT captured by Apple Watch Series 4: Can the Apple watch be used as an event monitor? Annals of Noninvasive Electrocardiology 25:e12742.", "title": "AVNRT captured by Apple Watch Series 4", "url": "https://europepmc.org/article/PMC/PMC7507696", "date": "2020-01-29", "written_by": "independent", "quote": "captured an episode on the Apple watch that demonstrated tachycardia at a rate of about 225 bpm" }, { "kind": "case report", "citation": "Samal S, Singhania N, Bansal S, Sahoo A (2020). New-onset atrial fibrillation in a young patient detected by smartwatch. Clinical Case Reports 8(7).", "title": "New-onset atrial fibrillation in a young patient detected by smartwatch", "url": "https://europepmc.org/article/PMC/PMC7364102", "date": "2020-04-23", "written_by": "independent", "quote": "He was chemically cardioverted using intravenous flecainide per cardiology recommendations. He maintained sinus rhythm afterward and was discharged home on metoprolol and apixaban." }, { "kind": "case report", "citation": "Ahmed AS, Golden KM, Foreman JR, Padanilam BJ (2020). Using a smartwatch to identify the morphology of atrial flutter. HeartRhythm Case Reports 6.", "title": "Using a smartwatch to identify the morphology of atrial flutter", "url": "https://europepmc.org/article/PMC/PMC7573381", "date": "2020-06-28", "written_by": "independent", "quote": "ECG revealed typical atrial flutter with variable atrioventricular conduction" }, { "kind": "case report", "citation": "Burke J, Haigney MCP, Borne R, Krantz MJ (2020). Smartwatch detection of ventricular tachycardia: Case series. HeartRhythm Case Reports 6.", "title": "Smartwatch detection of ventricular tachycardia: Case series", "url": "https://europepmc.org/article/PMC/PMC7573479", "date": "2020-08-18", "written_by": "independent", "quote": "The patient has not experienced further symptoms suggestive of arrhythmia, nor has he reported detection of event recordings on his smartwatch" }, { "kind": "case report", "citation": "Patel D, Tarakji KG (2021). Smartwatch diagnosis of atrial fibrillation in patient with embolic stroke of unknown source: A case report. Cardiovascular Digital Health Journal 2(1):84-87.", "title": "Smartwatch diagnosis of atrial fibrillation in patient with embolic stroke of unknown source", "url": "https://europepmc.org/article/PMC/PMC8890063", "date": "2021-01-13", "written_by": "independent", "quote": "Given her elevated CHA2DS2VASC score of 5 (age, female, previous stroke, and hypertension), the patient was started on a direct oral anticoagulant, and the aspirin was stopped." }, { "kind": "case report", "citation": "Kassam N, Aziz O, Aghan E, Somji S, Mbithe H, Bapumia M, Mvungi R, Surani S (2021). Smart Watch Detection of Supraventricular Tachycardia (SVT): First Case from Tanzania. International Medical Case Reports Journal 14.", "title": "Smart Watch Detection of Supraventricular Tachycardia (SVT): First Case from Tanzania", "url": "https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ", "date": "2021-08-24", "written_by": "independent", "quote": "a high resting heart rate of 210 beats/min" }, { "kind": "case report", "citation": "Itoh A (2022). Apple Watch is useful in demonstrating coffee-triggered atrial fibrillation with a very short duration. European Heart Journal - Case Reports 6:ytac252.", "title": "Apple Watch is useful in demonstrating coffee-triggered atrial fibrillation", "url": "https://europepmc.org/article/PMC/PMC9269674", "date": "2022-06-28", "written_by": "independent", "quote": "After that, the patient stopped drinking coffee in the morning, and AF hardly occurred." }, { "kind": "case report", "citation": "Hawryszko M, Sławiński G, Kozłowski D, Lewicka E (2022). Apple Watch-guided diagnosis of AVNRT in a pregnant woman. Frontiers in Cardiovascular Medicine 9:985421.", "title": "Apple Watch-guided diagnosis of AVNRT in a pregnant woman", "url": "https://europepmc.org/article/PMC/PMC9676225", "date": "2022-11-07", "written_by": "independent", "quote": "narrow QRS complex tachycardia of 216 bpm, and short RP interval" }, { "kind": "case report", "citation": "Heringer GV, Vinson DR (2024). Utility of smart watch in expediting diagnosis of cold drink-triggered atrial fibrillation: a case report. International Journal of Emergency Medicine 17.", "title": "Utility of smart watch in expediting diagnosis of cold drink-triggered atrial fibrillation", "url": "https://europepmc.org/article/PMC/PMC11462712", "date": "2024-10-08", "written_by": "independent", "quote": "Had it not been for his smart watch, the etiology of his transitory palpitations would have remained undiagnosed." }, { "kind": "case report", "citation": "Guarnieri G, Mapelli M, Moltrasio M, Agostoni P, Tondo C (2024). Connected health: Ventricular tachycardia detection with Apple Watch—A case report. Heliyon 10:e40595.", "title": "Connected health: Ventricular tachycardia detection with Apple Watch", "url": "https://europepmc.org/article/PMC/PMC11625252", "date": "2024-11-21", "written_by": "independent", "quote": "An alert message of 'Elevated Heart rate' from his Apple Watch® prompted him to record a single-lead ECG using the smartwatch." }, { "kind": "case report", "citation": "Rushakoff JA, Flores Rosario K, Grover P, Wang A (2025). Smartwatch bradycardia alarms leading to a diagnosis of lamin A/C cardiomyopathy: a case report. European Heart Journal - Case Reports 9(9):ytaf415.", "title": "Smartwatch bradycardia alarms leading to a diagnosis of lamin A/C cardiomyopathy: a case report", "url": "https://europepmc.org/article/PMC/PMC12412439", "date": "2025-08-23", "written_by": "independent", "quote": "An asymptomatic 47-year-old man from Costa Rica presented to cardiology clinic for evaluation of recurrent bradycardia alarms on his smartwatch (Apple Watch)." }, { "kind": "case report", "citation": "Shroddha AD, Mukherjee I, Lambiase PD (2026). Apple Watch Saves an Athlete: Unmasking Early Detection of Arrhythmogenic Cardiomyopathy. Cureus 18:e113337.", "title": "Apple Watch Saves an Athlete: Unmasking Early Detection of Arrhythmogenic Cardiomyopathy", "url": "https://europepmc.org/article/PMC/PMC13501160", "date": "2026-07-25", "written_by": "independent", "quote": "In the early hours of Thursday, at 3:00 am, he awoke with frequent ectopic beats noted on his smartwatch single-lead ECG, which progressed to a broad complex tachycardia." } ] } }, "provenance": { "kind": "legacy_record", "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 heart-rate alarms led to an ECG-confirmed AF diagnosis and anticoagulation (UK, 2019); 16 echoes from 15 published case reports to 2026.", "account": "In a case report of 29 March 2019 (event date not reported), a 59-year-old woman attended a United Kingdom emergency department. The acute medicine service saw her (the report does not name the hospital; the author is at Ipswich Hospital). She attended because her Apple Watch Series 2 had alarmed several times overnight that her pulse exceeded 120 bpm while she was inactive. A transient ischemic attack had occurred two months earlier. An electrocardiogram (ECG) showed atrial fibrillation with a fast ventricular response. She started bisoprolol and a direct oral anticoagulant, and clopidogrel was stopped. Later case reports describe the watch's heart-rate alerts, irregular-rhythm notification or ECG app leading to confirmed atrial flutter, supraventricular and ventricular tachycardia and heart block. Treatment was by cardioversion, ablation, pacemaker or defibrillator." } }, "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 analyzed heart-rate signals and generated notifications about abnormal readings." }, "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 an Apple Watch Series 2. Later reports involved several other watch generations." }, "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": "Apple Watch Series 2 (event); echoes name Series 3, 4, 5, 6 and Ultra; eight reports do not state the model, and in Ahmed et al. the alerting tracker is unnamed (the Series 5 was the authors' post-diagnosis device)" } }, "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 heart-rate notifications, Irregular Rhythm Notification and ECG 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": "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 heart-rate, irregular-rhythm and ECG alerts leading to arrhythmia diagnosis and treatment", "headline": "Apple Watch alarms lead to an AF diagnosis and anticoagulation" } }, "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": [ "905a54793460a36fce999a8d9fc8643ced3de43055171fea943cf8b61e6956bf" ], "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 a case report of 29 March 2019 (event date not reported), a 59-year-old woman attended a United Kingdom emergency department. The acute medicine service saw her (the report does not name the hospital; the author is at Ipswich Hospital). She attended because her Apple Watch Series 2 had alarmed several times overnight that her pulse exceeded 120 bpm while she was inactive. A transient ischemic attack had occurred two months earlier. An electrocardiogram (ECG) showed atrial fibrillation with a fast ventricular response. She started bisoprolol and a direct oral anticoagulant, and clopidogrel was stopped. Later case reports describe the watch's heart-rate alerts, irregular-rhythm notification or ECG app leading to confirmed atrial flutter, supraventricular and ventricular tachycardia and heart block. Treatment was by cardioversion, ablation, pacemaker or defibrillator.", "replication": { "produced_by": [ "The wearer owns an Apple Watch with heart-rate notifications, Irregular Rhythm Notification or the ECG app enabled", "An arrhythmia occurs while the watch is worn and the alert or recording leads the wearer to seek care", "A clinician confirms the rhythm on a 12-lead ECG or monitoring and treats it" ], "must_hold": [ "The alert or trace reaches a clinician rather than being dismissed", "Confirmatory ECG or electrophysiology testing is available", "A treatment pathway (anticoagulation, cardioversion, ablation, device) exists" ], "echoes": [ { "date": "2019-07-21", "setting": "woman in her mid-60s, emergency department, USA; low heart-rate alarms; complete heart block, pacemaker implanted (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Overbeek et al. (2019). Am J Emerg Med 37(10):1989.e5. https://www.sciencedirect.com/science/article/abs/pii/S0735675719304747" }, { "date": "2019-12-03", "setting": "68-year-old man one year after TAVI, USA; ECG app recording after syncope showed high-grade AV block; urgent biventricular pacemaker (publication date)", "model_version": "Apple Watch (ECG app)", "outcome": "recurred", "source": "Yerasi et al. (2020). Eur Heart J 41(10):1096. https://academic.oup.com/eurheartj/article/41/10/1096/5651110" }, { "date": "2019-12-31", "setting": "56-year-old man, emergency department, South Africa; watch showed 150 bpm for four days; atrial flutter, anticoagulation and cardioversion (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Goldstein & Wells (2019). Oxf Med Case Reports 2019(12):omz137. https://academic.oup.com/omcr/article/2019/12/495/5691267" }, { "date": "2020-01-29", "setting": "16-year-old girl, Minneapolis, USA (authors' institution); watch captured 225 bpm and ECG; AVNRT ablated, asymptomatic at four months (publication date)", "model_version": "Apple Watch Series 4", "outcome": "recurred", "source": "Siddeek et al. (2020). Ann Noninvasive Electrocardiol 25:e12742. https://europepmc.org/article/PMC/PMC7507696" }, { "date": "2020-04-23", "setting": "39-year-old man, Milwaukee, USA (authors' institution); watch showed heart rate 60–130 bpm during palpitations; AF cardioverted with flecainide, metoprolol and apixaban (publication date)", "model_version": "Apple Watch Series 3", "outcome": "recurred", "source": "Samal et al. (2020). Clin Case Rep 8(7). https://europepmc.org/article/PMC/PMC7364102" }, { "date": "2020-06-28", "setting": "54-year-old woman with mechanical aortic valve, Pensacola, USA (authors' institution); alerted to elevated heart rates by an unnamed fitness tracker; typical atrial flutter on ECG; outpatient ablation planned, discharged with rate control and continued anticoagulation; the authors then used an Apple Watch Series 5 in a lead II configuration to confirm the flutter morphology in the ambulatory setting (publication date)", "model_version": "Apple Watch Series 5 (authors' post-diagnosis monitoring; alerting device unnamed)", "outcome": "unknown", "source": "Ahmed et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573381" }, { "date": "2020-08-18", "setting": "60-year-old man, Denver, USA (authors' institution); single-lead ECG showed wide complex tachycardia; ARVC with VT, ablation and ICD (publication date)", "model_version": "Apple Watch", "outcome": "recurred", "source": "Burke et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573479" }, { "date": "2020-08-18", "setting": "63-year-old man, USA; watch recording labeled ventricular tachycardia; 9% VPC burden with NSVT, papillary muscle ablation (publication date)", "model_version": "smartwatch (brand not stated for this case; case 1 in the same series names an Apple Watch)", "outcome": "recurred", "source": "Burke et al. (2020). HeartRhythm Case Rep 6. https://europepmc.org/article/PMC/PMC7573479" }, { "date": "2021-01-13", "setting": "70-year-old woman after embolic stroke of unknown source, Cleveland, USA (authors' institution); irregular-rhythm notifications and ECG app; AF, DOAC started, loop recorder avoided (publication date)", "model_version": "Apple Watch (Irregular Rhythm Notification and ECG app)", "outcome": "recurred", "source": "Patel & Tarakji (2021). Cardiovasc Digit Health J 2(1):84-87. https://europepmc.org/article/PMC/PMC8890063" }, { "date": "2021-08-24", "setting": "42-year-old man, Dar es Salaam, Tanzania (authors' institution); resting heart rate 210 bpm; SVT (orthodromic AVRT), cardioversion, medication; an ablation attempt had no effect and pathway ablation was declined; managed medically (publication date)", "model_version": "Apple Watch Series 4", "outcome": "recurred", "source": "Kassam et al. (2021). Int Med Case Rep J 14. https://www.dovepress.com/smart-watch-detection-of-supraventricular-tachycardia-svt-first-case-f-peer-reviewed-fulltext-article-IMCRJ" }, { "date": "2022-06-28", "setting": "man in his early 60s, Shikokuchuo, Japan (authors' institution); ECG app recordings; coffee-triggered paroxysmal AF, trigger avoidance (publication date)", "model_version": "Apple Watch (ECG app)", "outcome": "recurred", "source": "Itoh (2022). Eur Heart J Case Rep 6:ytac252. https://europepmc.org/article/PMC/PMC9269674" }, { "date": "2022-11-07", "setting": "35-year-old woman at 20 weeks of pregnancy, Gdańsk, Poland (authors' institution); ECG app 216 bpm; AVNRT diagnosed, managed with Valsalva instruction without drugs (publication date)", "model_version": "Apple Watch Series 6", "outcome": "recurred", "source": "Hawryszko et al. (2022). Front Cardiovasc Med 9:985421. https://europepmc.org/article/PMC/PMC9676225" }, { "date": "2024-10-08", "setting": "43-year-old man, urgent care, USA; ECG app reported signs of AFib; paroxysmal AF, trigger avoidance and aspirin, no recurrence (publication date)", "model_version": "Apple Watch Ultra", "outcome": "recurred", "source": "Heringer & Vinson (2024). Int J Emerg Med 17. https://europepmc.org/article/PMC/PMC11462712" }, { "date": "2024-11-21", "setting": "60-year-old man, Milan, Italy (authors' institution); “Elevated Heart rate” alert then ECG; sustained VT at 183 bpm, radiofrequency ablation (publication date)", "model_version": "Apple Watch Series 5", "outcome": "recurred", "source": "Guarnieri et al. (2024). Heliyon 10:e40595. https://europepmc.org/article/PMC/PMC11625252" }, { "date": "2025-08-23", "setting": "47-year-old asymptomatic man from Costa Rica, Duke University Medical Center, USA; recurrent low heart-rate alarms; LMNA cardiomyopathy, CRT-D implanted, ejection fraction improved (publication date)", "model_version": "Apple Watch (low heart-rate notification)", "outcome": "recurred", "source": "Rushakoff et al. (2025). Eur Heart J Case Rep 9(9):ytaf415. https://europepmc.org/article/PMC/PMC12412439" }, { "date": "2026-07-25", "setting": "33-year-old male footballer, London, UK; single-lead ECG showed ectopy progressing to broad complex tachycardia; arrhythmogenic cardiomyopathy, S-ICD, flecainide and nadolol (publication date)", "model_version": "Apple Watch (model not stated)", "outcome": "recurred", "source": "Shroddha et al. (2026). Cureus 18:e113337. https://europepmc.org/article/PMC/PMC13501160" } ] } } }, "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-0300", "case_url": "records/AIBD-0300.html", "reason": "The developer is headquartered in United States; the case names Japan.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Japan" }, { "label": "Case location", "value": "Fukuoka, Japan" } ], "filters": { "origin": "United States", "destination": [ "Japan" ] }, "record": { "id": "AIBD-0300", "title": "Apple Watch Series 11 breathing-disturbance notifications send a 79-year-old to his cardiologist; morning watch ECGs show AF after “elevated” nights and CPAP starts without polysomnography", "summary": "Fukuoka case (Cureus, 26 May 2026): sleep-apnoea notifications prompted consultation; watch ECGs showed AF only after “elevated” breathing-disturbance nights; CPAP under way, no polysomnography.", "date_observed": "2026-05-26", "date_basis": "source publication", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Maruyama (Haradoi Hospital, Fukuoka), Hieda and Fukata reported a case in Cureus on 26 May 2026. The consultation is undated. The patient is a 79-year-old Japanese man with hypertension, dyslipidemia, type 2 diabetes and symptomatic paroxysmal atrial fibrillation (AF). A 12-lead electrocardiogram (ECG) had documented it a decade earlier. After retirement no paroxysms were documented by ambulatory or standard ECG, and pulsed-field ablation was deferred. Some months after he began wearing an Apple Watch Series 11 paired with an iPhone 15 Pro, he consulted the authors. He asked about notifications of nocturnal “respiratory disturbance”. He activated the watch ECG every morning and evening. AF paroxysms were detected repeatedly on mornings after nights the watch rated breathing disturbance “elevated”. They were not detected after “not elevated” nights, and they terminated by evening. He is undergoing CPAP therapy, and pulsed-field ablation will be considered depending on its effect on the paroxysms. No polysomnography was performed. The authors state that watch-detected breathing disturbance is not equivalent to the apnea-hypopnea index. They state that the watch ECG cannot fully substitute for conventional ECG, and that the notification algorithm “was reportedly developed using advanced machine learning”. The Food and Drug Administration (FDA) cleared Apple's Sleep Apnea Notification Feature under 510(k) K240929 on 13 September 2024. Apple's 510(k) submission says that it uses a deep learning algorithm on Apple Watch accelerometer data, analyzed in 30-day windows. It was trained on over 11,000 nights with polysomnography or home sleep-test reference. No treatment outcome is reported.", "category": "benefit created", "unexpected": false, "purpose": "The notification feature is built to identify patterns of breathing disturbance suggestive of moderate-to-severe sleep apnea and tell the user. The ECG app is built to record and classify single-lead rhythm. Both were used as designed.", "system": "Apple Watch Sleep Apnea Notification Feature (breathing-disturbance notifications) and the ECG app", "model_version": "Apple Watch Series 11 with iPhone 15 Pro", "developer": "Apple", "deployer": null, "location": "Fukuoka, Japan", "domain": "Healthcare (cardiology; sleep medicine)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 1, "reason": "A man whose AF was thought quiescent learned that paroxysms recur after disturbed-breathing nights, and he started CPAP. No confirmed sleep-apnea diagnosis and no treatment effect are reported." } ], "ai_role": { "status": "assessed", "statement": "The notification feature's deep-learning model, described in Apple's submission to FDA, flagged elevated breathing disturbance and brought the patient to the clinic. His own morning ECG recordings, read by the authors, showed the AF paroxysms. The paper does not state separately what the ECG app's classifier reported. Clinicians chose CPAP and deferred ablation. The treating authors report the clinical episode. Apple supplies the technical description and validation in its 510(k) summary. FDA's clearance is a separate regulatory decision and does not verify this patient's benefit." }, "evidence": [ { "kind": "case report", "citation": "Maruyama T, Hieda M, Fukata M (2026). Smartwatch-Detected Atrial Fibrillation on the Morning After a Nocturnal Sleep-Disordered Breathing Notification: A Case Report. Cureus 18(5):e109701 (PMC13301138). Case Presentation, Figure 2, Discussion, and references 12–14.", "title": "Smartwatch-Detected Atrial Fibrillation on the Morning After a Nocturnal Sleep-Disordered Breathing Notification: A Case Report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "date": "2026-05-26", "written_by": "independent", "quote": "the smartwatch repeatedly detected AF paroxysms the following morning, only after the smartwatch had issued a breathing-disturbance notification during sleep." }, { "kind": "regulatory document", "citation": "Apple Inc. (2024). Sleep Apnea Notification Feature: 510(k) Summary, K240929, device description and performance testing, pp. 2 and 5–7, filed with the FDA. File includes FDA's September 13, 2024 substantial-equivalence letter.", "title": "510(k) Summary K240929: Sleep Apnea Notification Feature (SANF)", "url": "https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240929.pdf", "date": "2024-09-13", "written_by": "makers", "quote": "SANF includes a deep learning algorithm to identify breathing disturbances using accelerometer sensor data from Apple Watch." } ], "replication": { "produced_by": [ "Nightly watch wear with the sleep-apnea notification feature active, plus the owner's habit of recording a watch ECG every morning and evening", "A known history of paroxysmal AF that made the morning traces interpretable to his cardiologists", "Access to a clinic willing to act on watch data (CPAP started without polysomnography)" ], "must_hold": [ "The user completes onboarding and wears the watch during sleep, since the feature evaluates 30-day windows opportunistically", "Clinicians confirm with conventional ECG and polysomnography, which the authors say the watch cannot replace and which was not done here" ], "echoes": [] }, "paired_incident": null, "headline": "Apple Watch breathing alerts send a 79-year-old to CPAP therapy", "metric": null, "comparator": { "against": "ambulatory monitoring and standard 12-lead ECG, which had documented no paroxysms since his retirement", "before": "AF paroxysms were not documented by ambulatory monitoring or standard ECG", "after": "AF paroxysms were detected repeatedly in the morning after notification of nocturnal “elevated” breathing disturbance, but not in the morning after nights when breathing disturbance was “not elevated”", "basis": "measured", "quote": "AF paroxysms were not documented by ambulatory monitoring or standard ECG (Figure 1B), and we deferred pulsed-field ablation for his AF paroxysms.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/" }, "later": { "status": "unknown", "date": null, "note": "The May 2026 report says CPAP therapy is under way and ablation will depend on its effect; no later outcome 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 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://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "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" }, { "date": "2026-10-24", "change": "Consolidated three excerpts of the clinical report and two excerpts of the 510(k) file into two sources. Followed the case's technical references to Apple's original white paper and submission. Corrected the prose to identify that the FDA-hosted technical summary was written by Apple. Preserved that CPAP began but no treatment outcome or polysomnographic confirmation was reported.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent case report and the makers' regulatory document. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0300", "catalog_version": "1.0.0", "source_fingerprint": "3c00ba05fd9e3361ace72306c00b7428276417fec489bdf1bb70cf22ba9853cd", "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" } ], "source_ids": [ "AIBD-0300-source-0001", "AIBD-0300-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": "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 notification feature's deep-learning model, described in Apple's submission to FDA, flagged elevated breathing disturbance and brought the patient to the clinic. 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"value_present", "value": { "kind": "record_fields", "fields": { "account": "Maruyama (Haradoi Hospital, Fukuoka), Hieda and Fukata reported a case in Cureus on 26 May 2026. The consultation is undated. The patient is a 79-year-old Japanese man with hypertension, dyslipidemia, type 2 diabetes and symptomatic paroxysmal atrial fibrillation (AF). A 12-lead electrocardiogram (ECG) had documented it a decade earlier. After retirement no paroxysms were documented by ambulatory or standard ECG, and pulsed-field ablation was deferred. Some months after he began wearing an Apple Watch Series 11 paired with an iPhone 15 Pro, he consulted the authors. He asked about notifications of nocturnal “respiratory disturbance”. He activated the watch ECG every morning and evening. AF paroxysms were detected repeatedly on mornings after nights the watch rated breathing disturbance “elevated”. They were not detected after “not elevated” nights, and they terminated by evening. He is undergoing CPAP therapy, and pulsed-field ablation will be considered depending on its effect on the paroxysms. No polysomnography was performed. The authors state that watch-detected breathing disturbance is not equivalent to the apnea-hypopnea index. They state that the watch ECG cannot fully substitute for conventional ECG, and that the notification algorithm “was reportedly developed using advanced machine learning”. The Food and Drug Administration (FDA) cleared Apple's Sleep Apnea Notification Feature under 510(k) K240929 on 13 September 2024. Apple's 510(k) submission says that it uses a deep learning algorithm on Apple Watch accelerometer data, analyzed in 30-day windows. It was trained on over 11,000 nights with polysomnography or home sleep-test reference. No treatment outcome is reported.", "benefit_type": "Improved Physical Health" } }, "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": [ "account", "benefit_type" ], "source_refs": [ "AIBD-0300-source-0001", "AIBD-0300-source-0002" ], "reviewed_on": "2026-10-24" }, "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": "A man whose AF was thought quiescent learned that paroxysms recur after disturbed-breathing nights, and he started CPAP. No confirmed sleep-apnea diagnosis and no treatment effect are 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": "The notification feature is built to identify patterns of breathing disturbance suggestive of moderate-to-severe sleep apnea and tell the user. The ECG app is built to record and classify single-lead rhythm. Both were used as designed." } }, "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": "Fukuoka, Japan", "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": "The notification feature is built to identify patterns of breathing disturbance suggestive of moderate-to-severe sleep apnea and tell the user. The ECG app is built to record and classify single-lead rhythm. Both were used as designed." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-05-26", "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 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://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "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" }, { "date": "2026-10-24", "change": "Consolidated three excerpts of the clinical report and two excerpts of the 510(k) file into two sources. Followed the case's technical references to Apple's original white paper and submission. Corrected the prose to identify that the FDA-hosted technical summary was written by Apple. Preserved that CPAP began but no treatment outcome or polysomnographic confirmation was reported.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "editor": "AIBD" } ] } }, "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": [ "date_observed", "revisions", "date_basis", "date_precision" ], "source_refs": [ "AIBD-0300-source-0001", "AIBD-0300-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": [ "Nightly watch wear with the sleep-apnea notification feature active, plus the owner's habit of recording a watch ECG every morning and evening", "A known history of paroxysmal AF that made the morning traces interpretable to his cardiologists", "Access to a clinic willing to act on watch data (CPAP started without polysomnography)" ], "must_hold": [ "The user completes onboarding and wears the watch during sleep, since the feature evaluates 30-day windows opportunistically", "Clinicians confirm with conventional ECG and polysomnography, which the authors say the watch cannot replace and which was not done here" ], "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 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://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "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" }, { "date": "2026-10-24", "change": "Consolidated three excerpts of the clinical report and two excerpts of the 510(k) file into two sources. Followed the case's technical references to Apple's original white paper and submission. Corrected the prose to identify that the FDA-hosted technical summary was written by Apple. Preserved that CPAP began but no treatment outcome or polysomnographic confirmation was reported.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "editor": "AIBD" } ] } }, "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": [ "revisions" ], "source_refs": [ "AIBD-0300-source-0001", "AIBD-0300-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Healthcare (cardiology; sleep medicine)", "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://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "access_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13301138/fullTextXML", "status": "Full clinical text, figure descriptions and references read." }, { "source_url": "https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240929.pdf", "status": "FDA letter and manufacturer summary read separately." }, { "source_url": "https://www.apple.com/health/pdf/sleep-apnea/Sleep_Apnea_Notifications_on_Apple_Watch_September_2024.pdf", "status": "September 2024 maker white paper inspected; part of the upstream chain, not another clinical confirmation." } ], "limitations": [ "The article does not show whether the watch ECG classifier, rather than clinical reading, identified each AF episode.", "No polysomnography, controlled counterfactual, or treatment response is reported.", "Nightly breathing-disturbance categories and the feature's 30-day sleep-apnea notification are related but distinct outputs.", "Apple's underlying training and validation records were not publicly available for independent reproduction." ] } }, "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-0300-source-0001", "AIBD-0300-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The clinical report gives no sleep-study confirmation or treatment response and does not identify the ECG classifier’s contribution. Nightly breathing-disturbance ratings differ from the feature’s 30-day notification. Apple’s underlying training and validation records are not public.", "editor_note": "" } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "Maruyama T, Hieda M, Fukata M (2026). Smartwatch-Detected Atrial Fibrillation on the Morning After a Nocturnal Sleep-Disordered Breathing Notification: A Case Report. Cureus 18(5):e109701 (PMC13301138). Case Presentation, Figure 2, Discussion, and references 12–14.", "title": "Smartwatch-Detected Atrial Fibrillation on the Morning After a Nocturnal Sleep-Disordered Breathing Notification: A Case Report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "date": "2026-05-26", "written_by": "independent", "quote": "the smartwatch repeatedly detected AF paroxysms the following morning, only after the smartwatch had issued a breathing-disturbance notification during sleep." }, { "kind": "regulatory document", "citation": "Apple Inc. (2024). Sleep Apnea Notification Feature: 510(k) Summary, K240929, device description and performance testing, pp. 2 and 5–7, filed with the FDA. File includes FDA's September 13, 2024 substantial-equivalence letter.", "title": "510(k) Summary K240929: Sleep Apnea Notification Feature (SANF)", "url": "https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240929.pdf", "date": "2024-09-13", "written_by": "makers", "quote": "SANF includes a deep learning algorithm to identify breathing disturbances using accelerometer sensor data from Apple Watch." } ] } }, "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-0300-source-0001", "AIBD-0300-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0300-source-0001", "AIBD-0300-source-0002" ], "role": "Retained evidence sources in reviewed order" }, "provenance": { "kind": "curatorial", "basis": "Updated identifiers after consolidating duplicate excerpts and reviewing original sources.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0300-source-0001", "AIBD-0300-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": "case report", "citation": "Maruyama T, Hieda M, Fukata M (2026). Smartwatch-Detected Atrial Fibrillation on the Morning After a Nocturnal Sleep-Disordered Breathing Notification: A Case Report. Cureus 18(5):e109701 (PMC13301138). Case Presentation, Figure 2, Discussion, and references 12–14.", "title": "Smartwatch-Detected Atrial Fibrillation on the Morning After a Nocturnal Sleep-Disordered Breathing Notification: A Case Report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "date": "2026-05-26", "written_by": "independent", "quote": "the smartwatch repeatedly detected AF paroxysms the following morning, only after the smartwatch had issued a breathing-disturbance notification during sleep." }, { "kind": "regulatory document", "citation": "Apple Inc. (2024). Sleep Apnea Notification Feature: 510(k) Summary, K240929, device description and performance testing, pp. 2 and 5–7, filed with the FDA. File includes FDA's September 13, 2024 substantial-equivalence letter.", "title": "510(k) Summary K240929: Sleep Apnea Notification Feature (SANF)", "url": "https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240929.pdf", "date": "2024-09-13", "written_by": "makers", "quote": "SANF includes a deep learning algorithm to identify breathing disturbances using accelerometer sensor data from Apple Watch." } ] } }, "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-0300-source-0001", "AIBD-0300-source-0002" ], "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-0300-source-0001", "retained_source_order": [ "AIBD-0300-source-0001", "AIBD-0300-source-0002" ], "origins": [ { "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "contribution": "Original clinical narrative, ECGs and breathing-disturbance chart document consultation and management.", "locator": "Case Presentation; Figure 2; Discussion; references 12–14.", "relationship": "Treating clinical authors report the episode and declare no relevant support or financial relationships.", "source_refs": [ "AIBD-0300-source-0001" ] }, { "url": "https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240929.pdf", "contribution": "Manufacturer specifies the deep-learning model, training and 30-day notification rule; FDA letter establishes clearance.", "locator": "FDA letter pp. 1–3; Apple summary pp. 2 and 5–7.", "relationship": "Mixed file: FDA's decision plus Apple's technical submission. The technical claims are maker-authored.", "source_refs": [ "AIBD-0300-source-0002" ] } ], "claim_chains": [ { "claim": "Patient episode and management", "path": [ "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "Case Presentation and Figure 2" ], "finding": "The clinical authors interpreted the ECGs and chose CPAP. No CPAP response is reported, and no polysomnography confirmed sleep apnea." }, { "claim": "Machine learning and detection method", "path": [ "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "References 12–14", "https://www.apple.com/health/pdf/sleep-apnea/Sleep_Apnea_Notifications_on_Apple_Watch_September_2024.pdf", "https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240929.pdf" ], "finding": "The original white paper and 510(k) summary describe Apple's own development and validation. The paper's references 12 and 13 resolve to the same Apple white paper, not two independent sources." }, { "claim": "Regulatory status", "path": [ "https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240929.pdf", "September 13, 2024 FDA substantial-equivalence letter" ], "finding": "Clearance concerns the product and intended use; it is not confirmation of this clinical outcome." } ], "limitations": [ "The article does not show whether the watch ECG classifier, rather than clinical reading, identified each AF episode.", "No polysomnography, controlled counterfactual, or treatment response is reported.", "Nightly breathing-disturbance categories and the feature's 30-day sleep-apnea notification are related but distinct outputs.", "Apple's underlying training and validation records were not publicly available for independent reproduction." ], "excluded_or_consolidated_sources": [ { "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "reason": "Duplicate excerpt of the retained clinical report or technical submission." }, { "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13301138/", "reason": "Duplicate excerpt of the retained clinical report or technical submission." }, { "url": "https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240929.pdf", "reason": "Duplicate excerpt of the retained clinical report or technical submission." } ], "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-0300-source-0001", "AIBD-0300-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Treating clinicians report the consultation and ECG findings. Apple’s FDA submission describes the notification algorithm and its validation. FDA’s clearance is a regulatory decision, not confirmation of this patient’s benefit.", "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": "Fukuoka case (Cureus, 26 May 2026): sleep-apnoea notifications prompted consultation; watch ECGs showed AF only after “elevated” breathing-disturbance nights; CPAP under way, no polysomnography.", "account": "Maruyama (Haradoi Hospital, Fukuoka), Hieda and Fukata reported a case in Cureus on 26 May 2026. The consultation is undated. The patient is a 79-year-old Japanese man with hypertension, dyslipidemia, type 2 diabetes and symptomatic paroxysmal atrial fibrillation (AF). A 12-lead electrocardiogram (ECG) had documented it a decade earlier. After retirement no paroxysms were documented by ambulatory or standard ECG, and pulsed-field ablation was deferred. Some months after he began wearing an Apple Watch Series 11 paired with an iPhone 15 Pro, he consulted the authors. He asked about notifications of nocturnal “respiratory disturbance”. He activated the watch ECG every morning and evening. AF paroxysms were detected repeatedly on mornings after nights the watch rated breathing disturbance “elevated”. They were not detected after “not elevated” nights, and they terminated by evening. He is undergoing CPAP therapy, and pulsed-field ablation will be considered depending on its effect on the paroxysms. No polysomnography was performed. The authors state that watch-detected breathing disturbance is not equivalent to the apnea-hypopnea index. They state that the watch ECG cannot fully substitute for conventional ECG, and that the notification algorithm “was reportedly developed using advanced machine learning”. The Food and Drug Administration (FDA) cleared Apple's Sleep Apnea Notification Feature under 510(k) K240929 on 13 September 2024. Apple's 510(k) submission says that it uses a deep learning algorithm on Apple Watch accelerometer data, analyzed in 30-day windows. It was trained on over 11,000 nights with polysomnography or home sleep-test reference. No treatment outcome is reported." } }, "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": [ "summary", "account" ], "source_refs": [ "AIBD-0300-source-0001", "AIBD-0300-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Apple Watch sleep-apnea notifications prompted a consultation. Clinicians found atrial fibrillation in morning ECG recordings after nights with elevated breathing disturbances and began CPAP treatment, without a confirmatory sleep study.", "editor_note": "" } }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The model detected elevated breathing disturbance and generated 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 participant used an Apple Watch Series 11 with an iPhone 15 Pro." }, "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": "Apple Watch Series 11 with iPhone 15 Pro" } }, "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 Sleep Apnea Notification Feature (breathing-disturbance notifications) and the ECG 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": "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 Series 11 breathing-disturbance notifications send a 79-year-old to his cardiologist; morning watch ECGs show AF after “elevated” nights and CPAP starts without polysomnography", "headline": "Apple Watch breathing alerts send a 79-year-old to CPAP therapy" } }, "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": [ "3c00ba05fd9e3361ace72306c00b7428276417fec489bdf1bb70cf22ba9853cd" ], "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": "Maruyama (Haradoi Hospital, Fukuoka), Hieda and Fukata reported a case in Cureus on 26 May 2026. The consultation is undated. The patient is a 79-year-old Japanese man with hypertension, dyslipidemia, type 2 diabetes and symptomatic paroxysmal atrial fibrillation (AF). A 12-lead electrocardiogram (ECG) had documented it a decade earlier. After retirement no paroxysms were documented by ambulatory or standard ECG, and pulsed-field ablation was deferred. Some months after he began wearing an Apple Watch Series 11 paired with an iPhone 15 Pro, he consulted the authors. He asked about notifications of nocturnal “respiratory disturbance”. He activated the watch ECG every morning and evening. AF paroxysms were detected repeatedly on mornings after nights the watch rated breathing disturbance “elevated”. They were not detected after “not elevated” nights, and they terminated by evening. He is undergoing CPAP therapy, and pulsed-field ablation will be considered depending on its effect on the paroxysms. No polysomnography was performed. The authors state that watch-detected breathing disturbance is not equivalent to the apnea-hypopnea index. They state that the watch ECG cannot fully substitute for conventional ECG, and that the notification algorithm “was reportedly developed using advanced machine learning”. The Food and Drug Administration (FDA) cleared Apple's Sleep Apnea Notification Feature under 510(k) K240929 on 13 September 2024. Apple's 510(k) submission says that it uses a deep learning algorithm on Apple Watch accelerometer data, analyzed in 30-day windows. It was trained on over 11,000 nights with polysomnography or home sleep-test reference. No treatment outcome is reported.", "replication": { "produced_by": [ "Nightly watch wear with the sleep-apnea notification feature active, plus the owner's habit of recording a watch ECG every morning and evening", "A known history of paroxysmal AF that made the morning traces interpretable to his cardiologists", "Access to a clinic willing to act on watch data (CPAP started without polysomnography)" ], "must_hold": [ "The user completes onboarding and wears the watch during sleep, since the feature evaluates 30-day windows opportunistically", "Clinicians confirm with conventional ECG and polysomnography, which the authors say the watch cannot replace and which was not done here" ], "echoes": [] } } }, "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": [ "account", "replication" ], "source_refs": [ "AIBD-0300-source-0001", "AIBD-0300-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 33, "reviewed": 11, "record_mapped": 22, "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-0284", "case_url": "records/AIBD-0284.html", "reason": "The developer is headquartered in United States; the case names Australia.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Australia" }, { "label": "Case location", "value": "Melbourne, Australia" } ], "filters": { "origin": "United States", "destination": [ "Australia" ] }, "record": { "id": "AIBD-0284", "title": "Apple Watch Series 3 heart-rate drop from 99 to 65 bpm marks an 80-year-old woman's spontaneous reversion from atrial fibrillation, confirmed on a concurrent Holter", "summary": "Melbourne cardiologists matched 199 Apple Watch heart-rate readings to a Holter: the watch showed a sudden 99-to-65 bpm drop at 19:29 that coincided with reversion from AF to sinus rhythm.", "date_observed": "2020-02-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Toner, Al-Kaisey, Koshy and Lim of the Department of Cardiology, Austin Hospital, Melbourne, reported a case in BMJ Case Reports (published 4 February 2020). The event date is not given. An 80-year-old woman with known paroxysmal atrial fibrillation wore a 24-hour Holter monitor to assess rate control. On returning the monitor she reported feeling her heart “doing a flip” at 19:29. She noted her heart rate had suddenly dropped from 99 bpm to 65 bpm on her Apple Watch Series 3. The Holter showed she had reverted from AF to sinus rhythm at that time. The authors exported 199 watch readings over 22 hours and matched them to the Holter. The watch's average heart rate was 94 bpm in AF and 66 bpm after reversion. No notification, irregular-rhythm alert or electrocardiogram (ECG) app is described: the patient read the heart-rate display herself. The report documents no change in her treatment. The full text was not available from BMJ. The PMC copy is among the sources and names the device.", "category": "benefit created", "unexpected": false, "purpose": "The watch's optical heart-rate estimate is built to show the wearer their pulse. Its use here to time a rhythm change was that function, read by the patient rather than flagged by the device.", "system": "Apple Watch Series 3 heart-rate readings (optical photoplethysmography)", "model_version": "Apple Watch Series 3", "developer": "Apple", "deployer": null, "location": "Melbourne, Australia", "domain": "Healthcare (cardiology)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 1, "reason": "The patient learned from her watch, and had confirmed by Holter, that her rhythm had reverted at 19:29. The report records no change in management, so the benefit is informational only." } ], "ai_role": { "status": "assessed", "statement": "No alert, notification or rhythm classifier was involved. The watch displayed a photoplethysmographic heart-rate estimate, the patient noticed the drop, and the authors compared the readings with the Holter afterwards. The paper does not describe the heart-rate estimation as machine learning. So the AI character of the contribution is not established." }, "evidence": [ { "kind": "case report", "citation": "Toner L, Al-Kaisey AM, Koshy A, Lim HS (2020). Spontaneous reversion of paroxysmal atrial fibrillation detected by smart watch. BMJ Case Reports 13(2):e233431. doi:10.1136/bcr-2019-233431 (PMC7035811).", "title": "Spontaneous reversion of paroxysmal atrial fibrillation detected by smart watch", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7035811/", "date": "2020-02-04", "written_by": "independent", "quote": "she noted her heart rate suddenly drop from 99 bpm to 65 bpm as measured by her Apple Watch Series 3 (Cupertino, USA)" }, { "kind": "case report", "citation": "Toner L et al. (2020), BMJ Case Reports 13(2):e233431, Holter confirmation.", "title": "Spontaneous reversion of paroxysmal atrial fibrillation detected by smart watch", "url": "https://europepmc.org/article/MED/32024718", "date": "2020-02-04", "written_by": "independent", "quote": "Review of the Holter monitor revealed she had reverted from AF to SR at that time" }, { "kind": "case report", "citation": "Toner L et al. (2020), BMJ Case Reports 13(2):e233431, learning points.", "title": "Spontaneous reversion of paroxysmal atrial fibrillation detected by smart watch", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7035811/", "date": "2020-02-04", "written_by": "independent", "quote": "Sudden and persistent changes in heart rate as detected by smart watches should alert clinicians to the possibility of cardiac rhythm changes." } ], "replication": { "produced_by": [ "A patient with known paroxysmal AF wearing a heart-rate-displaying smartwatch while a Holter monitor was also fitted", "The patient noticing and reporting a sudden, persistent heart-rate drop with its time", "Clinicians willing to export the watch readings (Apple Health app to Excel) and align them with the Holter" ], "must_hold": [ "The rhythm change produces a heart-rate change large enough to be visible on the watch's optical estimate", "A reference recording (Holter) exists to confirm what the watch suggested" ], "echoes": [] }, "paired_incident": null, "headline": "Apple Watch Series 3 drop from 99 to 65 bpm marks AF reversion", "metric": "sudden 99-to-65 bpm drop at 19:29 that coincided with reversion from AF to sinus rhythm", "comparator": { "against": "a concurrent 24-hour Holter monitor", "before": null, "after": null, "basis": "measured", "quote": "A total of 199 heart rate readings were recorded from the Apple Watch and matched to 199 readings of the same time intervals on Holter monitoring over a 22-hour period as shown in figure 2 (median interval 6 min and IQR of 5.75 min). Moreover, the average HR as measured by smart watch in AF prior to reversion was 94 bpm compared with 66 bpm after reversion to SR.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7035811/" }, "later": { "status": "unknown", "date": null, "note": "The report says only that outcomes of smartwatch-detected arrhythmias are the subject of ongoing research; nothing later 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 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": "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": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: two independent case reports. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0284", "catalog_version": "1.0.0", "source_fingerprint": "21ad16cb86c219fb89e5514f46842e2eef6316bcd0595a6f644bce5798f809ad", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0284-source-0001", "AIBD-0284-source-0002", "AIBD-0284-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": "No alert, notification or rhythm classifier was involved. The watch displayed a photoplethysmographic heart-rate estimate, the patient noticed the drop, and the authors compared the readings with the Holter afterwards. The paper does not describe the heart-rate estimation as machine learning. So the AI character of the contribution is not established." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "No alert, notification or rhythm classifier was involved. The watch displayed a photoplethysmographic heart-rate estimate, the patient noticed the drop, and the authors compared the readings with the Holter afterwards. The paper does not describe the heart-rate estimation as machine learning. So the AI character of the 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": 1, "reason": "The patient learned from her watch, and had confirmed by Holter, that her rhythm had reverted at 19:29. 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The PMC copy is among the sources and names the device.", "replication": { "produced_by": [ "A patient with known paroxysmal AF wearing a heart-rate-displaying smartwatch while a Holter monitor was also fitted", "The patient noticing and reporting a sudden, persistent heart-rate drop with its time", "Clinicians willing to export the watch readings (Apple Health app to Excel) and align them with the Holter" ], "must_hold": [ "The rhythm change produces a heart-rate change large enough to be visible on the watch's optical estimate", "A reference recording (Holter) exists to confirm what the watch suggested" ], "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-0261", "case_url": "records/AIBD-0261.html", "reason": "The developer is headquartered in United Kingdom; the case names United States.", "details": [ { "label": "Developer headquarters", "value": "United Kingdom" }, { "label": "Case jurisdictions", "value": "United States" }, { "label": "Case location", "value": "Mountain View, California, USA" } ], "filters": { "origin": "United Kingdom", "destination": [ "United States" ] }, "record": { "id": "AIBD-0261", "title": "GNoME predicts 2.2 million crystal structures, of which few may be useful new compounds", "summary": "Google DeepMind's GNoME proposed 2.2 million crystal structures (380,000 stable); independent reviews (Cheetham & Seshadri 2024, Kurlin 2026) find duplicates and scant evidence of useful compounds.", "date_observed": "2023-11-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 29 November 2023 Google DeepMind published GNoME in Nature. 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Mater. critique itself was not available. Its abstract on Europe PMC (PMC11044265) is among the sources.", "category": "general benefit", "unexpected": false, "purpose": "Predict stable inorganic crystal structures at scale to accelerate materials discovery", "system": "GNoME (Graph Networks for Materials Exploration)", "model_version": null, "developer": "DeepMind", "deployer": "DeepMind (dataset released openly; stable structures contributed to the Materials Project)", "location": "Mountain View, California, USA", "domain": "Materials science", "segment": "Materials researchers", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "Independent reviewers dispute the novelty and utility of the predicted structures.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary. A dataset release." }, { "group": "organizational", "depth": null, "reason": "Use by the Materials Project and A-Lab is reported only by the vendor. Independent reviewers find scant utility." }, { "group": "societal", "depth": null, "reason": "No demonstrated downstream technology. Novelty of the predictions is disputed." } ], "ai_role": { "status": "unassessed", "statement": "The graph-network model is the vendor's. Its predictions are documented in a vendor-authored Nature paper and blog. Independent reviewers (Cheetham & Seshadri 2024; Kurlin 2026 as reported by C&EN) report near-duplicates, unrealistic compounds and little evidence of useful novel materials. Benefit therefore rests on the vendor's account and is contested." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. 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Human Researchers Say...", "url": "https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/", "date": "2024-04-11", "written_by": "independent", "quote": "the Google paper falls way short in terms of it being a useful, practical contribution to the experimental materials scientists." }, { "kind": "news report", "citation": "Chemical & Engineering News, 15 Oct 2026", "title": "Google DeepMind AI crystallography database includes near duplicate structures and unrealistic compounds", "url": "https://cen.acs.org/research-integrity/Google-DeepMind-structures-near-duplicates-questionable/104/web/2026/10", "date": "2026-10-15", "written_by": "independent", "quote": "My biggest problem with GNoME is that there are materials in there that are completely unrealistic." } ] } }, "provenance": { "kind": "legacy_record", "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-0261-source-0001", "AIBD-0261-source-0002", "AIBD-0261-source-0003", "AIBD-0261-source-0004", "AIBD-0261-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": "peer-reviewed study", "citation": "Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. 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It reported 2.2 million structures below the convex hull, calling it “an order-of-magnitude expansion in stable materials known to humanity”. It stated that 736 of the stable structures had already been independently realized. The company blog adds that the Berkeley Lab A-Lab synthesized more than 41 materials from the predictions. Cheetham and Seshadri (UC Santa Barbara, Chem. Mater., 8 April 2024) examined the listings. They reported “scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility”. 404 Media (11 April 2024) quoted Cheetham that the paper “falls way short” as a practical contribution and DeepMind that it stands by all claims. C&EN (15 October 2026) reported an arXiv analysis by Vitaliy Kurlin (Liverpool). It found more than 15% near-duplicates within GNoME, about 24% duplicating Materials Project entries and about 17% close to ICSD entries. Leslie Schoop (Princeton) called some entries “completely unrealistic”. The Chem. Mater. critique itself was not available. Its abstract on Europe PMC (PMC11044265) 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": "GNoME (Graph Networks for Materials Exploration)" } }, "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": "GNoME predicts 2.2 million crystal structures, of which few may be useful new compounds", "headline": "GNoME's 2.2 million predicted crystals may include useful compounds" } }, "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": [ "4b33996fd343295677781a9abca4c40244fce51cc5014c72c542405c1573fc90" ], "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 November 2023 Google DeepMind published GNoME in Nature. It reported 2.2 million structures below the convex hull, calling it “an order-of-magnitude expansion in stable materials known to humanity”. It stated that 736 of the stable structures had already been independently realized. The company blog adds that the Berkeley Lab A-Lab synthesized more than 41 materials from the predictions. Cheetham and Seshadri (UC Santa Barbara, Chem. Mater., 8 April 2024) examined the listings. They reported “scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility”. 404 Media (11 April 2024) quoted Cheetham that the paper “falls way short” as a practical contribution and DeepMind that it stands by all claims. C&EN (15 October 2026) reported an arXiv analysis by Vitaliy Kurlin (Liverpool). It found more than 15% near-duplicates within GNoME, about 24% duplicating Materials Project entries and about 17% close to ICSD entries. Leslie Schoop (Princeton) called some entries “completely unrealistic”. The Chem. Mater. critique itself was not available. Its abstract on Europe PMC (PMC11044265) is among the sources.", "replication": { "produced_by": [ "Graph neural network trained on existing crystal databases with iterative active learning", "High-throughput DFT stability checks of candidates", "Open release of the predicted structures" ], "must_hold": [ "Predicted structures are genuinely novel rather than near-duplicates of known entries", "Predicted compounds are synthesizable and have useful properties", "Independent experimental confirmation of utility" ], "echoes": [ { "date": "2024-04-08", "setting": "Independent review of GNoME listings by Cheetham and Seshadri (UC Santa Barbara)", "outcome": "did not recur", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11044265/" }, { "date": "2026-10-09", "setting": "Kurlin et al. arXiv analysis of the November 2024 GNoME update, reported by C&EN", "model_version": "GNoME dataset update, Nov 2024", "outcome": "did not recur", "source": "https://cen.acs.org/research-integrity/Google-DeepMind-structures-near-duplicates-questionable/104/web/2026/10" } ] } } }, "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-0341", "case_url": "records/AIBD-0341.html", "reason": "The developer is headquartered in United Kingdom; the case names Canada.", "details": [ { "label": "Developer headquarters", "value": "United Kingdom" }, { "label": "Case jurisdictions", "value": "Canada" }, { "label": "Case location", "value": "Long Island, southwestern Nova Scotia, Canada (landfall)" } ], "filters": { "origin": "United Kingdom", "destination": [ "Canada" ] }, "record": { "id": "AIBD-0341", "title": "A GraphCast live run on ECMWF charts may have forecast Hurricane Lee's Nova Scotia landfall about nine days ahead", "summary": "DeepMind says a live GraphCast run on ECMWF's site forecast Lee's Nova Scotia landfall about nine days ahead; NHC records landfall 16 September 2023; no forecaster use of the run is documented.", "date_observed": "2023-09-16", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Google DeepMind's blog post of 14 November 2023 describes a live version of its publicly available GraphCast model, deployed on the ECMWF website. In September 2023 it predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. MIT Technology Review (14 November 2023) relayed the claim from DeepMind staff research scientist Rémi Lam. It added that traditional forecasting models pinpointed Nova Scotia only six days in advance. The article quotes no ECMWF staff on Lee. ECMWF's Newsletter 177 editorial (Autumn 2023) confirms that GraphCast, Pangu-Weather and FourCastNet were made available on ECMWF's public charts pages from ECMWF's own initial conditions. The NHC tropical cyclone report on Lee (21 March 2024) records the landfall. Lee, by then post-tropical, made landfall on Long Island in southwestern Nova Scotia around 2000 UTC on 16 September 2023. Maximum sustained winds were about 55 kt. NHC official track errors were exceptionally low through 72 h and below five-year means through 120 h. The report mentions no AI model. No ECMWF document recording the Lee forecast is on record, and the NHC 2023 verification report contains no AI-model entry. It found only an intensity-structure mention of Lee in the independent CIRA preprint. The forecast itself is dated only as “September” by the vendor. No cited source documents a forecaster or authority acting on the GraphCast Lee run.", "category": "general benefit", "unexpected": false, "purpose": "Machine-learning global medium-range weather forecasting (10 days, 0.25 degree), including severe-event prediction such as tropical cyclone tracks. This forecast was that purpose.", "system": "GraphCast (Google DeepMind graph neural network weather model), experimental live run on ECMWF public charts", "model_version": "GraphCast (live version deployed on the ECMWF website)", "developer": "DeepMind", "deployer": "ECMWF (experimental data-driven forecasts on its public charts pages)", "location": "Long Island, southwestern Nova Scotia, Canada (landfall)", "domain": "Weather forecasting / tropical cyclones", "segment": "Public-sector meteorological service; AI research laboratory", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The nine-day claim is vendor-sourced and no forecaster use of the run is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual outcome is documented." }, { "group": "organizational", "depth": null, "reason": "General benefit. No cited source documents a forecast office using the GraphCast Lee run in its products." }, { "group": "societal", "depth": null, "reason": "General benefit. No public-safety decision is linked to the run in any cited source." } ], "ai_role": { "status": "assessed", "statement": "The only sources for the nine-day Lee forecast are the maker's own blog and a news report quoting the maker's scientist. ECMWF's editorial confirms only that GraphCast ran experimentally on its charts. NHC's Lee report records exceptionally low official track errors without mentioning AI guidance. The AI output is therefore documented as a forecast, not as an input to any decision." }, "evidence": [ { "kind": "press release", "citation": "Google DeepMind (2023). GraphCast: AI model for faster and more accurate global weather forecasting. DeepMind blog, 14 Nov 2023.", "title": "GraphCast: AI model for faster and more accurate global weather forecasting", "url": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/", "date": "2023-11-14", "written_by": "makers", "quote": "a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia" }, { "kind": "news report", "citation": "Heikkilä, M. (2023). Google DeepMind's weather AI can forecast extreme weather faster and more accurately. MIT Technology Review, 14 Nov 2023.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately", "url": "https://web.archive.org/web/20231114164801/https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "In September, GraphCast accurately predicted that Hurricane Lee would make landfall in Nova Scotia nine days in advance, says Rémi Lam, a staff research scientist at Google DeepMind." }, { "kind": "news report", "citation": "Heikkilä, M. (2023). Same article, comparison with conventional models.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately (conventional-model comparison)", "url": "https://web.archive.org/web/20231114164801/https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "Traditional weather forecasting models pinpointed the hurricane to Nova Scotia only six days in advance." }, { "kind": "report", "citation": "Blake, E. and Nepaul, H. (2024). National Hurricane Center Tropical Cyclone Report: Hurricane Lee (AL132023), 5-16 September 2023. NOAA/NWS, 21 Mar 2024.", "title": "Tropical Cyclone Report: Hurricane Lee (AL132023)", "url": "https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf", "date": "2024-03-21", "written_by": "independent", "quote": "making landfall on Long Island, in southwestern Nova Scotia, later that day around 2000 UTC with maximum sustained winds of about 55 kt well east of the center" }, { "kind": "report", "citation": "Blake, E. and Nepaul, H. (2024). Same report, forecast verification section (no AI model is mentioned anywhere in the report).", "title": "Tropical Cyclone Report: Hurricane Lee (track verification)", "url": "https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf", "date": "2024-03-21", "written_by": "independent", "quote": "Official track forecast errors were exceptionally low through 72 h, and below the previous 5-year period means through 120 h." }, { "kind": "press release", "citation": "Rabier, F. (2023). The AIFS is launched (editorial). ECMWF Newsletter No. 177, Autumn 2023.", "title": "The AIFS is launched", "url": "https://www.ecmwf.int/en/newsletter/177/editorial/aifs-launched", "date": null, "written_by": "deployer", "quote": "We have made these systems available on ECMWF's public charts pages, based on our own initial conditions." }, { "kind": "preprint", "citation": "Lam, R., Sanchez-Gonzalez, A., Willson, M., et al. (2022/2023). GraphCast: Learning skillful medium-range global weather forecasting. arXiv:2212.12794 (published in Science, Nov 2023).", "title": "GraphCast: Learning skillful medium-range global weather forecasting", "url": "https://arxiv.org/abs/2212.12794", "date": "2022-12-24", "written_by": "makers", "quote": "its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme temperatures" }, { "kind": "peer-reviewed study", "citation": "Olivetti L, Messori G (2024). Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast. Geoscientific Model Development 17:7915-7962, doi:10.5194/gmd-17-7915-2024.", "title": "Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast", "url": "https://gmd.copernicus.org/articles/17/7915/2024/", "date": "2024-11-07", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "GraphCast trained on decades of ERA5 reanalysis and run live from ECMWF initial conditions on ECMWF's experimental charts", "A long-lived Cabo Verde hurricane whose track was, per NHC, easier than normal to forecast (low climatology-persistence errors)", "Vendor retrospective comparison of its own run against conventional models" ], "must_hold": [ "Independent verification of the specific nine-day run against the official track record (not found in any ECMWF or NHC document among the sources)", "Access to ECMWF initial conditions for the live run", "A forecaster or authority actually consulting the AI run for it to become an attributable benefit" ], "echoes": [ { "date": null, "setting": "2023 season verification by CIRA of GraphCast and peer AI models against operational cyclone track guidance (existing record AIBD-0090)", "model_version": null, "outcome": "recurred", "source": "AIBD-0090 (DeMaria et al. 2024 preprint cited there)" } ] }, "paired_incident": null, "headline": "GraphCast may have forecast Lee's landfall nine days ahead", "metric": "forecast Lee's Nova Scotia landfall about nine days ahead", "comparator": { "against": "traditional physics-based forecasts of Hurricane Lee's landfall (DeepMind's own account)", "before": "only locked in on Nova Scotia about six days in advance", "after": "about nine days in advance", "basis": "assumed", "quote": "In September, a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. By contrast, traditional forecasts had greater variability in where and when landfall would occur, and only locked in on Nova Scotia about six days in advance.", "url": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/" }, "later": { "status": "persisted", "date": "2024-11-07", "note": "An independent Uppsala University evaluation published 7 November 2024 confirmed GraphCast's general advantage over ECMWF's IFS HRES, including on near-surface temperature and wind extremes; it does not examine the Hurricane Lee forecast, and the NHC's March 2024 report on Lee names no AI model.", "quote": "Both data-driven models perform significantly better than ECMWF's IFS HRES globally and in most regions. Most impressively, GraphCast significantly outperforms IFS HRES across all regions and lead times.", "url": "https://gmd.copernicus.org/articles/17/7915/2024/" }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/; https://gmd.copernicus.org/articles/17/7915/2024/; https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://en.wikipedia.org/wiki/European_Centre_for_Medium-Range_Weather_Forecasts", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study, one independent report, one independent news report, the makers' preprint, the makers' press release and the deployer's press release. 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model.", "quote": "Both data-driven models perform significantly better than ECMWF's IFS HRES globally and in most regions. 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No ECMWF document recording the Lee forecast is on record, and the NHC 2023 verification report contains no AI-model entry. It found only an intensity-structure mention of Lee in the independent CIRA preprint. The forecast itself is dated only as “September” by the vendor. No cited source documents a forecaster or authority acting on the GraphCast Lee run.", "benefit_type": "Improved Task Accuracy" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual outcome is documented." }, { "group": "organizational", "reason": "General benefit. 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A comparison of IFS HRES, Pangu-Weather, and GraphCast", "url": "https://gmd.copernicus.org/articles/17/7915/2024/", "date": "2024-11-07", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "DeepMind says a live GraphCast run on ECMWF's site forecast Lee's Nova Scotia landfall about nine days ahead; NHC records landfall 16 September 2023; no forecaster use of the run is documented.", "account": "Google DeepMind's blog post of 14 November 2023 describes a live version of its publicly available GraphCast model, deployed on the ECMWF website. In September 2023 it predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. MIT Technology Review (14 November 2023) relayed the claim from DeepMind staff research scientist Rémi Lam. It added that traditional forecasting models pinpointed Nova Scotia only six days in advance. The article quotes no ECMWF staff on Lee. ECMWF's Newsletter 177 editorial (Autumn 2023) confirms that GraphCast, Pangu-Weather and FourCastNet were made available on ECMWF's public charts pages from ECMWF's own initial conditions. The NHC tropical cyclone report on Lee (21 March 2024) records the landfall. Lee, by then post-tropical, made landfall on Long Island in southwestern Nova Scotia around 2000 UTC on 16 September 2023. Maximum sustained winds were about 55 kt. NHC official track errors were exceptionally low through 72 h and below five-year means through 120 h. The report mentions no AI model. No ECMWF document recording the Lee forecast is on record, and the NHC 2023 verification report contains no AI-model entry. It found only an intensity-structure mention of Lee in the independent CIRA preprint. The forecast itself is dated only as “September” by the vendor. No cited source documents a forecaster or authority acting on the GraphCast Lee run." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "GraphCast generated weather forecasts, including the projected path of Hurricane Lee." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "Researchers used the live GraphCast forecasts available through ECMWF." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. 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In September 2023 it predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. MIT Technology Review (14 November 2023) relayed the claim from DeepMind staff research scientist Rémi Lam. It added that traditional forecasting models pinpointed Nova Scotia only six days in advance. The article quotes no ECMWF staff on Lee. ECMWF's Newsletter 177 editorial (Autumn 2023) confirms that GraphCast, Pangu-Weather and FourCastNet were made available on ECMWF's public charts pages from ECMWF's own initial conditions. The NHC tropical cyclone report on Lee (21 March 2024) records the landfall. Lee, by then post-tropical, made landfall on Long Island in southwestern Nova Scotia around 2000 UTC on 16 September 2023. Maximum sustained winds were about 55 kt. NHC official track errors were exceptionally low through 72 h and below five-year means through 120 h. The report mentions no AI model. No ECMWF document recording the Lee forecast is on record, and the NHC 2023 verification report contains no AI-model entry. It found only an intensity-structure mention of Lee in the independent CIRA preprint. The forecast itself is dated only as “September” by the vendor. No cited source documents a forecaster or authority acting on the GraphCast Lee run.", "replication": { "produced_by": [ "GraphCast trained on decades of ERA5 reanalysis and run live from ECMWF initial conditions on ECMWF's experimental charts", "A long-lived Cabo Verde hurricane whose track was, per NHC, easier than normal to forecast (low climatology-persistence errors)", "Vendor retrospective comparison of its own run against conventional models" ], "must_hold": [ "Independent verification of the specific nine-day run against the official track record (not found in any ECMWF or NHC document among the sources)", "Access to ECMWF initial conditions for the live run", "A forecaster or authority actually consulting the AI run for it to become an attributable benefit" ], "echoes": [ { "setting": "2023 season verification by CIRA of GraphCast and peer AI models against operational cyclone track guidance (existing record AIBD-0090)", "outcome": "recurred", "source": "AIBD-0090 (DeMaria et al. 2024 preprint cited there)" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 0, "record_mapped": 32, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0031", "case_url": "records/AIBD-0031.html", "reason": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Bihar (twelve flood-prone districts), India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "record": { "id": "AIBD-0031", "title": "Randomized evaluation of Google AI flood alerts with community dissemination in Bihar, India (2022–2023 monsoons)", "summary": "Bihar RCT, 319 panchayats: households relayed Google flood alerts reported 16.6% fewer illness symptoms and about 30% lower medical costs.", "date_observed": "2022-06-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Google's AI flood-forecasting system, run with India's Central Water Commission (CWC), issues riverine flood alerts as Android push notifications. In Bihar, India, economists Maulik Jagnani (Tufts) and Rohini Pande (Yale) ran a randomized evaluation across 319 panchayats in 12 flood-prone districts. It covered the 2022 and 2023 monsoon seasons (June to October). The non-governmental organization Yuganter trained community volunteers to relay the alerts. Treatment households got 2.41 more alerts in 2022 and were 46.5% more likely to get an alert before water reached their area. They reported 16.56% fewer illness symptoms and about 30% lower medical spending than controls. No specific evacuation tied to an alert is documented.", "category": "general benefit", "unexpected": false, "purpose": "The forecasting system was built to warn people of riverine floods before water arrives so they can prepare or evacuate. The measured preparedness and health effects are that intended benefit, but only at cohort level.", "system": "Google flood forecasting with Android push alerts (Google Public Alerts), plus community dissemination by Yuganter volunteers", "model_version": null, "developer": "Google", "deployer": "Central Water Commission of India with Google; last-mile dissemination by Yuganter (NGO) under the Jagnani-Pande evaluation", "location": "Bihar (twelve flood-prone districts), India", "domain": "Disaster risk reduction / flood early warning", "segment": "Rural households in riverine flood zones", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Effects are cohort averages across roughly 3.6 million people's communities (fewer illness symptoms, lower medical spending). No identifiable household with an attributable changed outcome is documented." }, { "group": "societal", "depth": null, "reason": "The study estimates population-level preparedness and health effects from a randomized design. It reports no discrete event such as a named evacuation." } ], "ai_role": { "status": "assessed", "statement": "The AI forecast generated the alerts. The randomized contrast, however, is between community dissemination of those alerts and alerts alone. So the measured effect is attributable to the dissemination of AI forecasts rather than to forecast accuracy itself. A 2019 pilot in which the only added treatment was raising alert awareness with a single local leader found no treatment impact." }, "evidence": [ { "kind": "preprint", "citation": "Jagnani M, Pande R (2024). Forecasting Fate: Experimental Evaluation of a Flood Early Warning System. Working paper, Tufts University and Yale University (Economic Growth Center), 3 April 2024.", "title": "Forecasting Fate: Experimental Evaluation of a Flood Early Warning System", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "date": "2024-04-03", "written_by": "independent", "quote": "treatment households exhibiting higher adaptation and health scores and a 30% reduction in medical costs compared to control communities" }, { "kind": "report", "citation": "Abdul Latif Jameel Poverty Action Lab (J-PAL) (n.d.). Disseminating Flood Warnings to Increase Disaster Preparedness in India. Evaluation summary (PDF).", "title": "Disseminating Flood Warnings to Increase Disaster Preparedness in India", "url": "https://www.povertyactionlab.org/print/pdf/node/8026750", "date": null, "written_by": "independent", "quote": "households in treatment communities received 2.37 more alerts (a 290 percent increase) on average during the 2022 flood season (June-October)" } ], "replication": { "produced_by": [ "Google/CWC flood alerts available to all 319 study panchayats (both arms); the randomized element was volunteer dissemination", "Trained community volunteers relaying smartphone alerts in villages with low literacy and limited location-services use", "Repeated flood seasons (2022 was more intense than 2023) allowing effects to be measured" ], "must_hold": [ "Alerts reach households before water arrives (46.5% more likely in treatment communities in 2022)", "Households trust alerts despite more false positives (8.09% vs 3.64%)", "Dissemination capacity is maintained. From 2023 two volunteers per panchayat replaced three, supplemented by direct WhatsApp/SMS messages to treatment households enrolled in the 2022 endline survey." ], "echoes": [ { "date": "2023-06-01", "setting": "2023 monsoon season in the same 319 Bihar panchayats: treatment households received 4.35 more alerts and were 88% more likely to receive an alert before water reached their panchayat", "model_version": null, "outcome": "recurred", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "severity": null, "group": "societal", "basis": "Same developer and channel: Google's Android hazard alerts; flood alerts cut illness in Bihar, while the Android Earthquake Alerts system underestimated the Kahramanmaraş quake and failed to warn millions of Android users in Turkey." }, "headline": "Google flood alerts in Bihar cut reported illness by 16.6%", "metric": "16.56% fewer illness symptoms and about 30% lower medical expenditure", "comparator": { "against": "control communities, where households had access to the smartphone alerts but no community volunteers relaying them", "before": "control households (reference group)", "after": "0.36 fewer illness symptoms (a 16.56% decrease); roughly INR 6,500 less on illness treatment (a 32.87% decrease)", "basis": "measured", "quote": "Overall, treatment households observed 0.36 fewer illness symptoms (a 16.56% decrease) compared to control households (Table 4). As a result, households in treatment areas incurred roughly INR 6,500 less on illness treatment (a 32.87% decrease).", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" }, "later": { "status": "unknown", "date": null, "note": "Neither cited source reports a development after the 2022-2023 evaluation seasons.", "quote": null, "url": null }, "value": { "amount": "INR 6,500", "what": "less spent on illness treatment per household over the flood season", "who": "treatment households in the Bihar study panchayats", "estimate": false, "quote": "As a result, households in treatment areas incurred roughly INR 6,500 less on illness treatment (a 32.87% decrease). In fact, based on the estimated reduction in medical expenditures alone, we estimate that for every $1 spent on the program, the intervention would generate a minimum benefit of $30 during a severe flooding season.", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" }, "developer_country": "United States", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Removed “Flood Hub”, “Google Research” and the Kosi-river claim because neither cited source states them, and reworded the 2023 volunteer change and the 2019 pilot to match the working paper, which adds direct WhatsApp/SMS messaging in 2023 and describes the pilot's treatment as amplifying alert awareness to a single local leader.", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the value as stated from the cited sources.", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf; https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Central_Water_Commission", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent report and one independent preprint. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0031", "catalog_version": "1.0.0", "source_fingerprint": "1bfab83fdd45a0a55bd8861c4524bc8554fc9c682ab1f94bcf4c48375513c3f8", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "fructus", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0031-source-0001", "AIBD-0031-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0031-legacy-value", "DIV-0010" ], "role": "Existing monetary observations and Fructus statements with matched AIBD case links; each statement retains its own review and eligibility status" }, "provenance": { "kind": "curatorial", "basis": "Linked to the current Fructus dataset by its validated case identifier. 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PlantVillage (Penn State) developed it with FAO, IITA, CIMMYT and others. IITA researchers compared Nuru with cassava experts, agricultural extension officers and farmers. The targets were cassava mosaic disease (CMD), cassava brown streak disease (CBSD) and cassava green mite damage. They inspected plants in the field and scored a 170-image cassava symptom recognition assessment tool (CaSRAT) built from a farm in Mkuranga, Tanzania. CaSRAT was also used with 30 extension agents and 50 farmers from Busia County, western Kenya, before and after they were introduced to the app. Nuru diagnosed cassava disease symptoms at 65% accuracy in 2020 versus 40–58% for extension agents and 18–31% for farmers. It rose to 74–88% with six leaves per plant assessed. Mild symptoms were recognized at only 13–37%. Two weeks of practical use gave extension workers only a slight gain. No farm-level outcome is reported.", "category": "general benefit", "unexpected": false, "purpose": "Diagnose cassava mosaic disease, cassava brown streak disease and cassava green mite damage from leaf images on an offline Android phone.", "system": "PlantVillage Nuru cassava model (deep-learning object detection)", "model_version": "PlantVillage Nuru V2.6.0-39", "developer": "PlantVillage, Pennsylvania State University, with FAO, IITA, CIMMYT and others", "deployer": "IITA field evaluation with extension officers and farmers in Busia County, Kenya, and in Tanzania", "location": "Busia County, Kenya; Mkuranga, Tanzania", "domain": "Agriculture / crop disease diagnosis", "segment": "Smallholder cassava", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The paper also frames Nuru as an e-extension tool for transferring diagnostic knowledge, but the measured result is diagnostic accuracy.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Farmers scored 18–31% on diagnosis. The paper reports only a “slight increase” in extension workers' skill after two weeks and no yield or income outcome." }, { "group": "organizational", "depth": null, "reason": "No deployment outcome for extension services is documented beyond the accuracy comparison." } ], "ai_role": { "status": "assessed", "statement": "The study measures the model's diagnostic accuracy against humans. It does not document any alert that changed a management decision. Authors include the model's developers at IITA and Penn State, so the source is peer-reviewed rather than independent of the makers." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Mrisho LM, Mbilinyi NA, Ndalahwa M, Ramcharan AM, Kehs AK, McCloskey PC, Murithi H, Hughes DP, Legg JP. Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD. Frontiers in Plant Science 11:590889 (2020). doi:10.3389/fpls.2020.590889", "title": "Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD", "url": "https://doi.org/10.3389/fpls.2020.590889", "date": "2020-12-18", "written_by": "makers", "quote": "Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40-58%) and farmers (18-31%)." }, { "kind": "peer-reviewed study", "citation": "Adjéi EA, Traoré K, Sawadogo/Compaoré EMFW, et al. Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d'Ivoire: a case study in the departments of Dabou, Bouaké, and Man. Frontiers in Agronomy 6 (2024). doi:10.3389/fagro.2024.1433204", "title": "Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d'Ivoire: a case study in the departments of Dabou, Bouaké, and Man", "url": "https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full", "date": "2024-09-13", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A model trained on 2,756 cassava leaf images", "Field plants and a 170-image CaSRAT set scored by consensus of cassava experts", "Comparison groups of 30 extension agents and 50 farmers from Busia County" ], "must_hold": [ "Symptoms expressed on the assessed leaves (mild symptoms were recognized at only 13–37%)", "Six leaves per plant assessed to reach 74–88%" ], "echoes": [ { "date": "2018-06-25", "setting": "Fall armyworm model in the same app, launched by FAO and Penn State", "model_version": "Nuru fall armyworm", "outcome": "unknown", "source": "FAO news release, 25 June 2018" } ] }, "paired_incident": null, "headline": "PlantVillage Nuru out-diagnoses extension agents, 65% vs 40–58%", "metric": "65% accuracy in 2020 versus 40–58% for extension agents and 18–31% for farmers", "comparator": { "against": "agricultural extension agents and farmers diagnosing the same cassava symptoms", "before": "40–58% accuracy (agricultural extension agents); 18–31% (farmers)", "after": "65% accuracy in 2020 (Nuru)", "basis": "measured", "quote": "Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40–58%) and farmers (18–31%).", "url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.590889/full" }, "later": { "status": "scaled", "date": "2024-09-13", "note": "A September 2024 study reports that the WAVE center has disseminated Nuru to cassava farmers in Côte d'Ivoire since 2020, with a 45% adoption rate among 152 respondents in three departments.", "quote": "In this regard, WAVE has been disseminating a plant disease detection smart application, “PlantVillage Nuru” to farmers since 2020.", "url": "https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full" }, "value": null, "developer_country": "United States", "deployer_country": "Nigeria", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Learning among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.590889/full; https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full; https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.590889/full", "editor": "AIBD" }, { "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://en.wikipedia.org/wiki/International_Institute_of_Tropical_Agriculture", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study and the makers' peer-reviewed study. 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it was Improved Learning among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.590889/full; https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full; https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.590889/full", "editor": "AIBD" }, { "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://en.wikipedia.org/wiki/International_Institute_of_Tropical_Agriculture", "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": "Agriculture / crop disease diagnosis", "segment": "Smallholder cassava" } }, "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": "Mrisho LM, Mbilinyi NA, Ndalahwa M, Ramcharan AM, Kehs AK, McCloskey PC, Murithi H, Hughes DP, Legg JP. Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD. Frontiers in Plant Science 11:590889 (2020). doi:10.3389/fpls.2020.590889", "title": "Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD", "url": "https://doi.org/10.3389/fpls.2020.590889", "date": "2020-12-18", "written_by": "makers", "quote": "Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40-58%) and farmers (18-31%)." }, { "kind": "peer-reviewed study", "citation": "Adjéi EA, Traoré K, Sawadogo/Compaoré EMFW, et al. Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d'Ivoire: a case study in the departments of Dabou, Bouaké, and Man. Frontiers in Agronomy 6 (2024). doi:10.3389/fagro.2024.1433204", "title": "Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d'Ivoire: a case study in the departments of Dabou, Bouaké, and Man", "url": "https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full", "date": "2024-09-13", "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-0191-source-0001", "AIBD-0191-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": "Mrisho LM, Mbilinyi NA, Ndalahwa M, Ramcharan AM, Kehs AK, McCloskey PC, Murithi H, Hughes DP, Legg JP. Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD. Frontiers in Plant Science 11:590889 (2020). doi:10.3389/fpls.2020.590889", "title": "Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD", "url": "https://doi.org/10.3389/fpls.2020.590889", "date": "2020-12-18", "written_by": "makers", "quote": "Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40-58%) and farmers (18-31%)." }, { "kind": "peer-reviewed study", "citation": "Adjéi EA, Traoré K, Sawadogo/Compaoré EMFW, et al. Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d'Ivoire: a case study in the departments of Dabou, Bouaké, and Man. Frontiers in Agronomy 6 (2024). doi:10.3389/fagro.2024.1433204", "title": "Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d'Ivoire: a case study in the departments of Dabou, Bouaké, and Man", "url": "https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full", "date": "2024-09-13", "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": "IITA-led evaluation in Kenya and Tanzania: the offline deep-learning app out-diagnosed extension officers (40–58%) and farmers (18–31%); six-leaf assessment raised accuracy to 74–88%.", "account": "Nuru is a deep-learning object-detection model inside the free PlantVillage Android app, trained on 2,756 cassava leaf images and able to run offline. PlantVillage (Penn State) developed it with FAO, IITA, CIMMYT and others. IITA researchers compared Nuru with cassava experts, agricultural extension officers and farmers. The targets were cassava mosaic disease (CMD), cassava brown streak disease (CBSD) and cassava green mite damage. They inspected plants in the field and scored a 170-image cassava symptom recognition assessment tool (CaSRAT) built from a farm in Mkuranga, Tanzania. CaSRAT was also used with 30 extension agents and 50 farmers from Busia County, western Kenya, before and after they were introduced to the app. Nuru diagnosed cassava disease symptoms at 65% accuracy in 2020 versus 40–58% for extension agents and 18–31% for farmers. It rose to 74–88% with six leaves per plant assessed. Mild symptoms were recognized at only 13–37%. Two weeks of practical use gave extension workers only a slight gain. No farm-level outcome 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": "Nuru detected signs of cassava disease in photographs." }, "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 PlantVillage Nuru V2.6.0-39." }, "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": "PlantVillage Nuru V2.6.0-39" } }, "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": "PlantVillage Nuru 2.6.0-39.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "PlantVillage Nuru cassava model (deep-learning object 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": "PlantVillage, Pennsylvania State University, with FAO, IITA, CIMMYT and others" } }, "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": "PlantVillage Nuru identifies cassava disease symptoms at 65% accuracy against 40–58% for extension agents", "headline": "PlantVillage Nuru out-diagnoses extension agents, 65% vs 40–58%" } }, "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": [ "f6256232ccb15115e087f28114b76bd5cde4d3a11a241a11093c09cbe1ffa2cb" ], "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": "Nuru is a deep-learning object-detection model inside the free PlantVillage Android app, trained on 2,756 cassava leaf images and able to run offline. PlantVillage (Penn State) developed it with FAO, IITA, CIMMYT and others. IITA researchers compared Nuru with cassava experts, agricultural extension officers and farmers. The targets were cassava mosaic disease (CMD), cassava brown streak disease (CBSD) and cassava green mite damage. They inspected plants in the field and scored a 170-image cassava symptom recognition assessment tool (CaSRAT) built from a farm in Mkuranga, Tanzania. CaSRAT was also used with 30 extension agents and 50 farmers from Busia County, western Kenya, before and after they were introduced to the app. Nuru diagnosed cassava disease symptoms at 65% accuracy in 2020 versus 40–58% for extension agents and 18–31% for farmers. It rose to 74–88% with six leaves per plant assessed. Mild symptoms were recognized at only 13–37%. Two weeks of practical use gave extension workers only a slight gain. No farm-level outcome is reported.", "replication": { "produced_by": [ "A model trained on 2,756 cassava leaf images", "Field plants and a 170-image CaSRAT set scored by consensus of cassava experts", "Comparison groups of 30 extension agents and 50 farmers from Busia County" ], "must_hold": [ "Symptoms expressed on the assessed leaves (mild symptoms were recognized at only 13–37%)", "Six leaves per plant assessed to reach 74–88%" ], "echoes": [ { "date": "2018-06-25", "setting": "Fall armyworm model in the same app, launched by FAO and Penn State", "model_version": "Nuru fall armyworm", "outcome": "unknown", "source": "FAO news release, 25 June 2018" } ] } } }, "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-0295", "case_url": "records/AIBD-0295.html", "reason": "The developer is headquartered in United States; the case names United Kingdom.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "United States, United Kingdom" }, { "label": "Case location", "value": "United States (vendor analyzes); London, United Kingdom (independent trials)" } ], "filters": { "origin": "United States", "destination": [ "United States", "United Kingdom" ] }, "record": { "id": "AIBD-0295", "title": "Dexcom G6's “Urgent Low Soon” predictive alert may cut time below 54 mg/dL, by less in controlled trials", "summary": "Dexcom-authored analyzes (1,424 and 15,000 users) link the 20-minute predictive low alert to less hypoglycemia; an independent 24-person crossover RCT found a reduction only below 2.8 mmol/L.", "date_observed": "2019-03-22", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Dexcom G6 continuous glucose monitor was classified by the Food and Drug Administration (FDA) under De Novo DEN170088 on 27 March 2018. Its “Urgent Low Soon” (ULS) alert fires when an algorithm predicts an estimated glucose value of 55 mg/dL or below within 20 minutes. Dexcom employees Puhr, Derdzinski, Welsh, Parker, Walker and Price compared 1,424 users before and after moving from the G5 (no predictive alert) to the G6. Their anonymized comparison was in Diabetes Technology & Therapeutics (online 22 March 2019). With a 70 mg/dL low-threshold setting (n=658) time below 54 mg/dL fell from 1.0% to 0.6%. With an 80 mg/dL setting (n=766) it fell 0.6% to 0.4% (both P<0.001). ULS fired 0.9 and 0.6 times a day. A second Dexcom analysis (Puhr et al., Journal of Diabetes Science and Technology, online 3 April 2019; abstract only) had 15,000 G6 app users. More than 93% had ULS enabled. Its use was associated with less time below 55 and 70 mg/dL regardless of how often users viewed the app. The FDA decision summary describes the threshold and predictive alerts as intended to warn patients to act before a hypoglycemic event. Independent evidence is thinner. The PACE crossover randomized trial at Imperial College Healthcare NHS Trust had 24 adults with type 1 diabetes. Each had ULS on or off for 40 days. It found no difference in hypoglycemia below 3.0 or 3.9 mmol/L in the 24 hours after exercise. Time below 2.8 mmol/L was lower with ULS on (P=0.04). Preechasuk et al. (Imperial College London) had 29 adults who switched from FreeStyle Libre 2 to a Dexcom G7 with ULS. They spent less time below 70 mg/dL (3.0% to 2.0%, P=0.006). That changes device and alert together. A Prague case report records a patient with impaired hypoglycemia awareness whose CGM with ULS on did not eliminate prolonged hypoglycemia. He sometimes muted the alerts. None of the sources calls the predictive algorithm machine learning.", "category": "general benefit", "unexpected": false, "purpose": "Built to warn users 20 minutes before predicted serious hypoglycemia so they can act. The reported reductions in time below 54–55 mg/dL are that purpose.", "system": "Dexcom G6 real-time continuous glucose monitoring system, 'Urgent Low Soon' predictive low glucose alert (also present on the G7 in one independent study)", "model_version": "Dexcom G6 (vendor analyses; PACE trial); Dexcom G7 (Preechasuk et al.)", "developer": "Dexcom, Inc.", "deployer": null, "location": "United States (vendor analyzes); London, United Kingdom (independent trials)", "domain": "Healthcare (diabetes technology)", "segment": "Diabetes technology", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Reductions in time below 54–55 mg/dL are reported for user populations, mostly in Dexcom's own analyses. No individual event with a changed outcome is documented. The one independent case report found ULS alone insufficient." }, { "group": "organizational", "depth": null, "reason": "No payer, clinic or health-system outcome is reported in the sources." } ], "ai_role": { "status": "assessed", "statement": "The alert is generated when the G6 algorithm forecasts glucose at or below 55 mg/dL within 20 minutes. Users still have to act on it. The principal outcome evidence is the maker's own real-world analyses (Puhr et al. 2019, 2020). They compare the same users before and after the G6 transition without a control arm. The independent Imperial College PACE randomized trial found no difference at 3.0 or 3.9 mmol/L after exercise and a difference only below 2.8 mmol/L. The Imperial switch study cannot separate the alert from the change of device. None of the sources describes the prediction method as machine learning." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Puhr S, Derdzinski M, Welsh JB, Parker AS, Walker T, Price DA (2019). Real-World Hypoglycemia Avoidance with a Continuous Glucose Monitoring System's Predictive Low Glucose Alert. Diabetes Technology & Therapeutics 21(4):155-158 (PMC6477579). All authors at Dexcom, Inc.", "title": "Real-World Hypoglycemia Avoidance with a Continuous Glucose Monitoring System's Predictive Low Glucose Alert", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "date": "2019-03-22", "written_by": "makers", "quote": "includes a new alert that is triggered when an algorithm predicts that an estimated glucose value ≤55 mg/dL will occur within 20 min" }, { "kind": "peer-reviewed study", "citation": "Puhr et al. (2019), Diabetes Technol Ther 21(4):155-158, results.", "title": "Real-World Hypoglycemia Avoidance with a Continuous Glucose Monitoring System's Predictive Low Glucose Alert", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "date": "2019-03-22", "written_by": "makers", "quote": "Percent time <54, ≤55, <70, and >250 mg/dL fell significantly after the transition to G6, independent of low threshold alert setting." }, { "kind": "peer-reviewed study", "citation": "Puhr S, Derdzinski M, Parker AS, Welsh JB, Price DA (2020). Real-World Hypoglycemia Avoidance With a Predictive Low Glucose Alert Does Not Depend on Frequent Screen Views. Journal of Diabetes Science and Technology 14(1) (online 3 April 2019; PMC7189147). All authors at Dexcom, Inc.", "title": "Real-World Hypoglycemia Avoidance With a Predictive Low Glucose Alert Does Not Depend on Frequent Screen Views", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7189147/", "date": "2019-04-03", "written_by": "makers", "quote": "Over 93% of users enabled the ULS alert; its use was associated with significantly reduced hypoglycemia <55 and <70 mg/dL, independent of screen view frequency." }, { "kind": "regulatory document", "citation": "U.S. Food and Drug Administration (2018). Evaluation of Automatic Class III Designation for Dexcom G6 Continuous Glucose Monitoring System, Decision Summary, DEN170088 (decision date 27 March 2018).", "title": "De Novo DEN170088 Decision Summary: Dexcom G6 Continuous Glucose Monitoring System", "url": "https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN170088.pdf", "date": "2018-03-27", "written_by": "independent", "quote": "An adjustable threshold and predictive glucose alert at 55 mg/dL are intended to warn patients that they need to take action", "supports_benefit": false }, { "kind": "peer-reviewed study", "citation": "Rilstone S, Oliver N, Godsland I, Tanushi B, Thomas M, Hill N (2024). A Randomized Controlled Trial Assessing the Impact of Continuous Glucose Monitoring with a Predictive Hypoglycemia Alert Function on Hypoglycemia in Physical Activity for People with Type 1 Diabetes (PACE). Diabetes Technology & Therapeutics 26(2); doi:10.1089/dia.2023.0376.", "title": "A Randomized Controlled Trial Assessing the Impact of Continuous Glucose Monitoring with a Predictive Hypoglycemia Alert Function on Hypoglycemia in Physical Activity for People with Type 1 Diabetes (PACE)", "url": "https://doi.org/10.1089/dia.2023.0376", "date": "2023-11-27", "written_by": "independent", "quote": "There was no difference in change from baseline of hypoglycemia <3.0 and <3.9 mmol/L with the ULS on or off during the 24 h after exercise." }, { "kind": "peer-reviewed study", "citation": "Preechasuk L, Avari P, Oliver N, Reddy M (2024). Switching from Intermittently Scanned Continuous Glucose Monitoring to Real-Time Continuous Glucose Monitoring with a Predictive Urgent Low Soon Alert Reduces Exposure to Hypoglycemia. Diabetes Technology & Therapeutics 26(7); doi:10.1089/dia.2023.0434.", "title": "Switching from Intermittently Scanned Continuous Glucose Monitoring to Real-Time Continuous Glucose Monitoring with a Predictive Urgent Low Soon Alert Reduces Exposure to Hypoglycemia", "url": "https://doi.org/10.1089/dia.2023.0434", "date": "2024-04-09", "written_by": "independent", "quote": "After switching to rtCGM, participants spent less time in hypoglycemia below 3.9 mmol/L (70 mg/dL) (3.0% [1.0%, 5.0%] vs. 2.0% [1.0%, 3.0%], P = 0.006)" } ], "replication": { "produced_by": [ "Real-time CGM with the ULS alert enabled (over 93% of G6 app users in Dexcom's sample)", "Users who respond to the alert by treating or adjusting insulin, which the sources assume rather than measure", "A low-threshold alert also set (70 or 80 mg/dL in the Dexcom analysis)" ], "must_hold": [ "Users keep the alert on rather than muting it for alarm fatigue, as the Prague patient sometimes did", "Sensor accuracy sufficient to avoid nuisance alerts, which Puhr et al. cite as a cause of CGM discontinuation", "Benefit beyond a plain threshold alert, which the PACE trial found only below 2.8 mmol/L" ], "echoes": [ { "date": "2023-11-27", "setting": "PACE crossover randomized trial, Imperial College Healthcare NHS Trust, 24 adults with type 1 diabetes, ULS on versus off for 40 days each around regular exercise", "model_version": "Dexcom G6", "outcome": "recurred", "source": "Rilstone et al. (2024) Diabetes Technol Ther, doi:10.1089/dia.2023.0376 (significant only for time below 2.8 mmol/L)" }, { "date": "2024-04-09", "setting": "Imperial College London, 29 adults with type 1 diabetes switched from FreeStyle Libre 2 to Dexcom G7 with ULS for 12 weeks", "model_version": "Dexcom G7", "outcome": "recurred", "source": "Preechasuk et al. (2024) Diabetes Technol Ther, doi:10.1089/dia.2023.0434 (device and alert changed together)" }, { "date": "2025-02-21", "setting": "Case report, General University Hospital Prague: a 45-year-old man with impaired hypoglycemia awareness used CGM with ULS on during 2020", "model_version": null, "outcome": "did not recur", "source": "Zadáková E, Horová E (2025) Front Clin Diabetes Healthc 6:1536038, https://pmc.ncbi.nlm.nih.gov/articles/PMC11886589/ (quote: 'prolonged hypoglycemia could not be eliminated during 2020, especially after physical activity. The patient admitted that he occasionally had to turn off or mute the alerts')" } ] }, "paired_incident": null, "headline": "Dexcom G6's Urgent Low Soon alert may cut time below 54 mg/dL", "metric": "time below 54 mg/dL fell from 1.0% to 0.6%", "comparator": { "against": "the same users on the preceding Dexcom G5 system, which had no predictive alert (before-and-after)", "before": "1.0 (1.5) percent of time <54 mg/dL on G5 (70 mg/dL low-alert setting, Table 1)", "after": "0.6 (1.0) percent of time <54 mg/dL on G6 (70 mg/dL low-alert setting, Table 1)", "basis": "measured", "quote": "Percent time <54, ≤55, <70, and >250 mg/dL fell significantly after the transition to G6, independent of low threshold alert setting. Time in range improved for G6 users with a low threshold alert setting of 70 mg/dL.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/" }, "later": 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": "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 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://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "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/Dexcom", "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 specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent regulatory document, two independent peer-reviewed studies and two peer-reviewed studies from the makers. For the benefit itself, the cited sources include no finding by a regulator or court.", "profile": { "case_id": "AIBD-0295", "catalog_version": "1.0.0", "source_fingerprint": "a22b940e01e867889b7fa5921506d0d0cd267ddfe3653c464b81e87b76a1a6f2", "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" } ], "source_ids": [ "AIBD-0295-source-0001", "AIBD-0295-source-0002", "AIBD-0295-source-0003", "AIBD-0295-source-0004", "AIBD-0295-source-0005", "AIBD-0295-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": "Dexcom, Inc." } }, "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 alert is generated when the G6 algorithm forecasts glucose at or below 55 mg/dL within 20 minutes. Users still have to act on it. The principal outcome evidence is the maker's own real-world analyses (Puhr et al. 2019, 2020). They compare the same users before and after the G6 transition without a control arm. The independent Imperial College PACE randomized trial found no difference at 3.0 or 3.9 mmol/L after exercise and a difference only below 2.8 mmol/L. The Imperial switch study cannot separate the alert from the change of device. None of the sources describes the prediction method as machine learning." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "The alert is generated when the G6 algorithm forecasts glucose at or below 55 mg/dL within 20 minutes. Users still have to act on it. The principal outcome evidence is the maker's own real-world analyses (Puhr et al. 2019, 2020). They compare the same users before and after the G6 transition without a control arm. The independent Imperial College PACE randomized trial found no difference at 3.0 or 3.9 mmol/L after exercise and a difference only below 2.8 mmol/L. 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"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 Dexcom G6 continuous glucose monitor was classified by the Food and Drug Administration (FDA) under De Novo DEN170088 on 27 March 2018. Its “Urgent Low Soon” (ULS) alert fires when an algorithm predicts an estimated glucose value of 55 mg/dL or below within 20 minutes. Dexcom employees Puhr, Derdzinski, Welsh, Parker, Walker and Price compared 1,424 users before and after moving from the G5 (no predictive alert) to the G6. Their anonymized comparison was in Diabetes Technology & Therapeutics (online 22 March 2019). With a 70 mg/dL low-threshold setting (n=658) time below 54 mg/dL fell from 1.0% to 0.6%. With an 80 mg/dL setting (n=766) it fell 0.6% to 0.4% (both P<0.001). ULS fired 0.9 and 0.6 times a day. A second Dexcom analysis (Puhr et al., Journal of Diabetes Science and Technology, online 3 April 2019; abstract only) had 15,000 G6 app users. More than 93% had ULS enabled. Its use was associated with less time below 55 and 70 mg/dL regardless of how often users viewed the app. The FDA decision summary describes the threshold and predictive alerts as intended to warn patients to act before a hypoglycemic event. Independent evidence is thinner. The PACE crossover randomized trial at Imperial College Healthcare NHS Trust had 24 adults with type 1 diabetes. Each had ULS on or off for 40 days. It found no difference in hypoglycemia below 3.0 or 3.9 mmol/L in the 24 hours after exercise. Time below 2.8 mmol/L was lower with ULS on (P=0.04). Preechasuk et al. (Imperial College London) had 29 adults who switched from FreeStyle Libre 2 to a Dexcom G7 with ULS. They spent less time below 70 mg/dL (3.0% to 2.0%, P=0.006). That changes device and alert together. A Prague case report records a patient with impaired hypoglycemia awareness whose CGM with ULS on did not eliminate prolonged hypoglycemia. He sometimes muted the alerts. None of the sources calls the predictive algorithm machine learning.", "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": "Reductions in time below 54–55 mg/dL are reported for user populations, mostly in Dexcom's own analyses. No individual event with a changed outcome is documented. The one independent case report found ULS alone insufficient." }, { "group": "organizational", "reason": "No payer, clinic or health-system outcome is reported 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 warn users 20 minutes before predicted serious hypoglycemia so they can act. The reported reductions in time below 54–55 mg/dL are that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "United States (vendor analyzes); London, United Kingdom (independent trials)", "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 warn users 20 minutes before predicted serious hypoglycemia so they can act. The reported reductions in time below 54–55 mg/dL are that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2019-03-22", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "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://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "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/Dexcom", "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": "Reviewed the specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "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": [ "Real-time CGM with the ULS alert enabled (over 93% of G6 app users in Dexcom's sample)", "Users who respond to the alert by treating or adjusting insulin, which the sources assume rather than measure", "A low-threshold alert also set (70 or 80 mg/dL in the Dexcom analysis)" ], "must_hold": [ "Users keep the alert on rather than muting it for alarm fatigue, as the Prague patient sometimes did", "Sensor accuracy sufficient to avoid nuisance alerts, which Puhr et al. cite as a cause of CGM discontinuation", "Benefit beyond a plain threshold alert, which the PACE trial found only below 2.8 mmol/L" ], "echoes": [ { "date": "2023-11-27", "setting": "PACE crossover randomized trial, Imperial College Healthcare NHS Trust, 24 adults with type 1 diabetes, ULS on versus off for 40 days each around regular exercise", "model_version": "Dexcom G6", "outcome": "recurred", "source": "Rilstone et al. (2024) Diabetes Technol Ther, doi:10.1089/dia.2023.0376 (significant only for time below 2.8 mmol/L)" }, { "date": "2024-04-09", "setting": "Imperial College London, 29 adults with type 1 diabetes switched from FreeStyle Libre 2 to Dexcom G7 with ULS for 12 weeks", "model_version": "Dexcom G7", "outcome": "recurred", "source": "Preechasuk et al. (2024) Diabetes Technol Ther, doi:10.1089/dia.2023.0434 (device and alert changed together)" }, { "date": "2025-02-21", "setting": "Case report, General University Hospital Prague: a 45-year-old man with impaired hypoglycemia awareness used CGM with ULS on during 2020", "outcome": "did not recur", "source": "Zadáková E, Horová E (2025) Front Clin Diabetes Healthc 6:1536038, https://pmc.ncbi.nlm.nih.gov/articles/PMC11886589/ (quote: 'prolonged hypoglycemia could not be eliminated during 2020, especially after physical activity. The patient admitted that he occasionally had to turn off or mute the alerts')" } ] } } }, "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": [ "Real-time CGM with the ULS alert enabled (over 93% of G6 app users in Dexcom's sample)", "Users who respond to the alert by treating or adjusting insulin, which the sources assume rather than measure", "A low-threshold alert also set (70 or 80 mg/dL in the Dexcom analysis)" ], "must_hold": [ "Users keep the alert on rather than muting it for alarm fatigue, as the Prague patient sometimes did", "Sensor accuracy sufficient to avoid nuisance alerts, which Puhr et al. cite as a cause of CGM discontinuation", "Benefit beyond a plain threshold alert, which the PACE trial found only below 2.8 mmol/L" ], "echoes": [ { "date": "2023-11-27", "setting": "PACE crossover randomized trial, Imperial College Healthcare NHS Trust, 24 adults with type 1 diabetes, ULS on versus off for 40 days each around regular exercise", "model_version": "Dexcom G6", "outcome": "recurred", "source": "Rilstone et al. (2024) Diabetes Technol Ther, doi:10.1089/dia.2023.0376 (significant only for time below 2.8 mmol/L)" }, { "date": "2024-04-09", "setting": "Imperial College London, 29 adults with type 1 diabetes switched from FreeStyle Libre 2 to Dexcom G7 with ULS for 12 weeks", "model_version": "Dexcom G7", "outcome": "recurred", "source": "Preechasuk et al. (2024) Diabetes Technol Ther, doi:10.1089/dia.2023.0434 (device and alert changed together)" }, { "date": "2025-02-21", "setting": "Case report, General University Hospital Prague: a 45-year-old man with impaired hypoglycemia awareness used CGM with ULS on during 2020", "outcome": "did not recur", "source": "Zadáková E, Horová E (2025) Front Clin Diabetes Healthc 6:1536038, https://pmc.ncbi.nlm.nih.gov/articles/PMC11886589/ (quote: 'prolonged hypoglycemia could not be eliminated during 2020, especially after physical activity. The patient admitted that he occasionally had to turn off or mute the alerts')" } ] } } }, "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 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://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "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/Dexcom", "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": "Reviewed the specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "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 (diabetes technology)", "segment": "Diabetes 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": "peer-reviewed study", "citation": "Puhr S, Derdzinski M, Welsh JB, Parker AS, Walker T, Price DA (2019). Real-World Hypoglycemia Avoidance with a Continuous Glucose Monitoring System's Predictive Low Glucose Alert. Diabetes Technology & Therapeutics 21(4):155-158 (PMC6477579). All authors at Dexcom, Inc.", "title": "Real-World Hypoglycemia Avoidance with a Continuous Glucose Monitoring System's Predictive Low Glucose Alert", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "date": "2019-03-22", "written_by": "makers", "quote": "includes a new alert that is triggered when an algorithm predicts that an estimated glucose value ≤55 mg/dL will occur within 20 min" }, { "kind": "peer-reviewed study", "citation": "Puhr et al. (2019), Diabetes Technol Ther 21(4):155-158, results.", "title": "Real-World Hypoglycemia Avoidance with a Continuous Glucose Monitoring System's Predictive Low Glucose Alert", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6477579/", "date": "2019-03-22", "written_by": "makers", "quote": "Percent time <54, ≤55, <70, and >250 mg/dL fell significantly after the transition to G6, independent of low threshold alert setting." }, { "kind": "peer-reviewed study", "citation": "Puhr S, Derdzinski M, Parker AS, Welsh JB, Price DA (2020). Real-World Hypoglycemia Avoidance With a Predictive Low Glucose Alert Does Not Depend on Frequent Screen Views. Journal of Diabetes Science and Technology 14(1) (online 3 April 2019; PMC7189147). All authors at Dexcom, Inc.", "title": "Real-World Hypoglycemia Avoidance With a Predictive Low Glucose Alert Does Not Depend on Frequent Screen Views", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7189147/", "date": "2019-04-03", "written_by": "makers", "quote": "Over 93% of users enabled the ULS alert; its use was associated with significantly reduced hypoglycemia <55 and <70 mg/dL, independent of screen view frequency." }, { "kind": "regulatory document", "citation": "U.S. Food and Drug Administration (2018). Evaluation of Automatic Class III Designation for Dexcom G6 Continuous Glucose Monitoring System, Decision Summary, DEN170088 (decision date 27 March 2018).", "title": "De Novo DEN170088 Decision Summary: Dexcom G6 Continuous Glucose Monitoring System", "url": "https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN170088.pdf", "date": "2018-03-27", "written_by": "independent", "quote": "An adjustable threshold and predictive glucose alert at 55 mg/dL are intended to warn patients that they need to take action", "supports_benefit": false }, { "kind": "peer-reviewed study", "citation": "Rilstone S, Oliver N, Godsland I, Tanushi B, Thomas M, Hill N (2024). A Randomized Controlled Trial Assessing the Impact of Continuous Glucose Monitoring with a Predictive Hypoglycemia Alert Function on Hypoglycemia in Physical Activity for People with Type 1 Diabetes (PACE). Diabetes Technology & Therapeutics 26(2); doi:10.1089/dia.2023.0376.", "title": "A Randomized Controlled Trial Assessing the Impact of Continuous Glucose Monitoring with a Predictive Hypoglycemia Alert Function on Hypoglycemia in Physical Activity for People with Type 1 Diabetes (PACE)", "url": "https://doi.org/10.1089/dia.2023.0376", "date": "2023-11-27", "written_by": "independent", "quote": "There was no difference in change from baseline of hypoglycemia <3.0 and <3.9 mmol/L with the ULS on or off during the 24 h after exercise." }, { "kind": "peer-reviewed study", "citation": "Preechasuk L, Avari P, Oliver N, Reddy M (2024). Switching from Intermittently Scanned Continuous Glucose Monitoring to Real-Time Continuous Glucose Monitoring with a Predictive Urgent Low Soon Alert Reduces Exposure to Hypoglycemia. Diabetes Technology & Therapeutics 26(7); doi:10.1089/dia.2023.0434.", "title": "Switching from Intermittently Scanned Continuous Glucose Monitoring to Real-Time Continuous Glucose Monitoring with a Predictive Urgent Low Soon Alert Reduces Exposure to Hypoglycemia", "url": "https://doi.org/10.1089/dia.2023.0434", "date": "2024-04-09", "written_by": "independent", "quote": "After switching to rtCGM, participants spent less time in hypoglycemia below 3.9 mmol/L (70 mg/dL) (3.0% [1.0%, 5.0%] vs. 2.0% [1.0%, 3.0%], P = 0.006)" } ] } }, "provenance": { "kind": "legacy_record", "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-0295-source-0001", "AIBD-0295-source-0002", "AIBD-0295-source-0003", "AIBD-0295-source-0004", "AIBD-0295-source-0005", "AIBD-0295-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": "text", "text": "FDA cleared the G6 after reviewing its performance. The later studies assess hypoglycemia. The controlled PACE trial found no difference on its primary comparison and a benefit on a secondary measure." }, "provenance": { "kind": "source_review", "basis": "The identified originals and their locators are listed in the source assessment.", "record_fields": [], "source_refs": [ "AIBD-0295-source-0004", "AIBD-0295-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "FDA cleared the G6 after reviewing its performance. The later studies assess hypoglycemia. The controlled PACE trial found no difference on its primary comparison and a benefit on a secondary measure." } }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "lead_source_ref": "AIBD-0295-source-0001", "retained_source_order": [ "AIBD-0295-source-0001", "AIBD-0295-source-0002", "AIBD-0295-source-0003", "AIBD-0295-source-0004", "AIBD-0295-source-0005", "AIBD-0295-source-0006" ], "origins": [ { "url": "https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN170088.pdf", "locator": "§ L(3), pp. 13–14; §§ S–T, pp. 38–39.", "contribution": "Sensor-performance evidence, the alert’s intended function, and the clearance decision.", "relationship": "FDA’s regulatory review predates the outcome studies.", "source_refs": [ "AIBD-0295-source-0004" ] }, { "url": "https://journals.sagepub.com/doi/10.1089/dia.2023.0376", "locator": "Study design, Results, and Discussion.", "contribution": "A controlled comparison of alerts on and off; the primary hypoglycemia comparison was null, with a favorable secondary result.", "relationship": "Original randomized study; distinct from clearance.", "source_refs": [ "AIBD-0295-source-0005" ] } ], "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-0295-source-0004", "AIBD-0295-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "FDA cleared the G6 after reviewing its performance. The later studies assess hypoglycemia. The controlled PACE trial found no difference on its primary comparison and a benefit on a secondary measure." } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Dexcom-authored analyzes (1,424 and 15,000 users) link the 20-minute predictive low alert to less hypoglycemia; an independent 24-person crossover RCT found a reduction only below 2.8 mmol/L.", "account": "The Dexcom G6 continuous glucose monitor was classified by the Food and Drug Administration (FDA) under De Novo DEN170088 on 27 March 2018. Its “Urgent Low Soon” (ULS) alert fires when an algorithm predicts an estimated glucose value of 55 mg/dL or below within 20 minutes. Dexcom employees Puhr, Derdzinski, Welsh, Parker, Walker and Price compared 1,424 users before and after moving from the G5 (no predictive alert) to the G6. Their anonymized comparison was in Diabetes Technology & Therapeutics (online 22 March 2019). With a 70 mg/dL low-threshold setting (n=658) time below 54 mg/dL fell from 1.0% to 0.6%. With an 80 mg/dL setting (n=766) it fell 0.6% to 0.4% (both P<0.001). ULS fired 0.9 and 0.6 times a day. A second Dexcom analysis (Puhr et al., Journal of Diabetes Science and Technology, online 3 April 2019; abstract only) had 15,000 G6 app users. More than 93% had ULS enabled. Its use was associated with less time below 55 and 70 mg/dL regardless of how often users viewed the app. The FDA decision summary describes the threshold and predictive alerts as intended to warn patients to act before a hypoglycemic event. Independent evidence is thinner. The PACE crossover randomized trial at Imperial College Healthcare NHS Trust had 24 adults with type 1 diabetes. Each had ULS on or off for 40 days. It found no difference in hypoglycemia below 3.0 or 3.9 mmol/L in the 24 hours after exercise. Time below 2.8 mmol/L was lower with ULS on (P=0.04). Preechasuk et al. (Imperial College London) had 29 adults who switched from FreeStyle Libre 2 to a Dexcom G7 with ULS. They spent less time below 70 mg/dL (3.0% to 2.0%, P=0.006). That changes device and alert together. A Prague case report records a patient with impaired hypoglycemia awareness whose CGM with ULS on did not eliminate prolonged hypoglycemia. He sometimes muted the alerts. None of the sources calls the predictive algorithm machine learning." } }, "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 vendor analyses and PACE trial used Dexcom G6 devices. The Preechasuk study used Dexcom G7 devices." }, "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": "Dexcom G6 (vendor analyses; PACE trial); Dexcom G7 (Preechasuk et al.)" } }, "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": "Dexcom G6 real-time continuous glucose monitoring system, 'Urgent Low Soon' predictive low glucose alert (also present on the G7 in one independent study)" } }, "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": "Dexcom, 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": "Dexcom G6's “Urgent Low Soon” predictive alert may cut time below 54 mg/dL, by less in controlled trials", "headline": "Dexcom G6's Urgent Low Soon alert may cut time below 54 mg/dL" } }, "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": [ "a22b940e01e867889b7fa5921506d0d0cd267ddfe3653c464b81e87b76a1a6f2" ], "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 Dexcom G6 continuous glucose monitor was classified by the Food and Drug Administration (FDA) under De Novo DEN170088 on 27 March 2018. Its “Urgent Low Soon” (ULS) alert fires when an algorithm predicts an estimated glucose value of 55 mg/dL or below within 20 minutes. Dexcom employees Puhr, Derdzinski, Welsh, Parker, Walker and Price compared 1,424 users before and after moving from the G5 (no predictive alert) to the G6. Their anonymized comparison was in Diabetes Technology & Therapeutics (online 22 March 2019). With a 70 mg/dL low-threshold setting (n=658) time below 54 mg/dL fell from 1.0% to 0.6%. With an 80 mg/dL setting (n=766) it fell 0.6% to 0.4% (both P<0.001). ULS fired 0.9 and 0.6 times a day. A second Dexcom analysis (Puhr et al., Journal of Diabetes Science and Technology, online 3 April 2019; abstract only) had 15,000 G6 app users. More than 93% had ULS enabled. Its use was associated with less time below 55 and 70 mg/dL regardless of how often users viewed the app. The FDA decision summary describes the threshold and predictive alerts as intended to warn patients to act before a hypoglycemic event. Independent evidence is thinner. The PACE crossover randomized trial at Imperial College Healthcare NHS Trust had 24 adults with type 1 diabetes. Each had ULS on or off for 40 days. It found no difference in hypoglycemia below 3.0 or 3.9 mmol/L in the 24 hours after exercise. Time below 2.8 mmol/L was lower with ULS on (P=0.04). Preechasuk et al. (Imperial College London) had 29 adults who switched from FreeStyle Libre 2 to a Dexcom G7 with ULS. They spent less time below 70 mg/dL (3.0% to 2.0%, P=0.006). That changes device and alert together. A Prague case report records a patient with impaired hypoglycemia awareness whose CGM with ULS on did not eliminate prolonged hypoglycemia. He sometimes muted the alerts. None of the sources calls the predictive algorithm machine learning.", "replication": { "produced_by": [ "Real-time CGM with the ULS alert enabled (over 93% of G6 app users in Dexcom's sample)", "Users who respond to the alert by treating or adjusting insulin, which the sources assume rather than measure", "A low-threshold alert also set (70 or 80 mg/dL in the Dexcom analysis)" ], "must_hold": [ "Users keep the alert on rather than muting it for alarm fatigue, as the Prague patient sometimes did", "Sensor accuracy sufficient to avoid nuisance alerts, which Puhr et al. cite as a cause of CGM discontinuation", "Benefit beyond a plain threshold alert, which the PACE trial found only below 2.8 mmol/L" ], "echoes": [ { "date": "2023-11-27", "setting": "PACE crossover randomized trial, Imperial College Healthcare NHS Trust, 24 adults with type 1 diabetes, ULS on versus off for 40 days each around regular exercise", "model_version": "Dexcom G6", "outcome": "recurred", "source": "Rilstone et al. (2024) Diabetes Technol Ther, doi:10.1089/dia.2023.0376 (significant only for time below 2.8 mmol/L)" }, { "date": "2024-04-09", "setting": "Imperial College London, 29 adults with type 1 diabetes switched from FreeStyle Libre 2 to Dexcom G7 with ULS for 12 weeks", "model_version": "Dexcom G7", "outcome": "recurred", "source": "Preechasuk et al. (2024) Diabetes Technol Ther, doi:10.1089/dia.2023.0434 (device and alert changed together)" }, { "date": "2025-02-21", "setting": "Case report, General University Hospital Prague: a 45-year-old man with impaired hypoglycemia awareness used CGM with ULS on during 2020", "outcome": "did not recur", "source": "Zadáková E, Horová E (2025) Front Clin Diabetes Healthc 6:1536038, https://pmc.ncbi.nlm.nih.gov/articles/PMC11886589/ (quote: 'prolonged hypoglycemia could not be eliminated during 2020, especially after physical activity. The patient admitted that he occasionally had to turn off or mute the alerts')" } ] } } }, "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-0328", "case_url": "records/AIBD-0328.html", "reason": "The developer is headquartered in United States; the case names Iran.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Iran" }, { "label": "Case location", "value": "Mountains of southern Iran" } ], "filters": { "origin": "United States", "destination": [ "Iran" ] }, "record": { "id": "AIBD-0328", "title": "A CIA tool called “Ghost Murmur”, pairing quantum magnetometry with AI, may have located downed F-15E officer “Dude 44 Bravo” in Iran before his 4 April 2026 rescue", "summary": "The rescue is documented. The claim that Ghost Murmur located the airman rests on two anonymous sources and a subsequent presidential remark; its technical capability remains unverified.", "date_observed": "2026-04-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The April 6 White House briefing documents the rescue of the downed F-15E weapons systems officer in Iran. The official transcript also describes watching his suspected position with a camera from 40 miles away. That distance concerns the camera, not an established heartbeat-detection range. On April 7, the New York Post reported that two anonymous sources attributed the officer's location to a CIA tool called Ghost Murmur. They described quantum magnetic sensing combined with AI that separates a heartbeat signal from background noise, and attributed its development to Lockheed Martin's Skunk Works. Lockheed Martin declined to comment. In an April 8 telephone interview with the Post, President Trump called the tool important to the rescue, but supplied no technical explanation. The Post and Scientific American separately interviewed physicists who questioned the described capability. The official briefing does not name Ghost Murmur or establish an AI contribution. No public measurements or inspectable technical account establish that the claimed system located the officer.", "category": "harm prevented", "unexpected": false, "purpose": "Per the Post's sources, it is a tool built to find a human heartbeat at distance. The uses are intelligence collection and personnel recovery, and it was tested on Black Hawk helicopters. Its stated purpose is exactly the use claimed.", "system": "'Ghost Murmur': long-range quantum (nitrogen-vacancy diamond) magnetometry paired with AI software that isolates heartbeat signatures from background noise, as described by the NY Post's anonymous sources", "model_version": null, "developer": "Lockheed Martin Skunk Works, per two NY Post sources; the company declined to comment", "deployer": "US Central Intelligence Agency, per the NY Post; the CIA and White House named no tool", "location": "Mountains of southern Iran", "domain": "Defense and intelligence: combat search and rescue", "segment": "Remote personnel-location sensing", "benefit_type": "Improved Physical Health", "taxonomy_note": "A combat rescue is read as a health outcome because no rescue or defense type exists.", "registry_ids": { "oecd_aim": "2026-04-08-d905" }, "ratings": [ { "group": "personal", "depth": 5, "reason": "The weapons systems officer was found alive on 4 April 2026 and brought out with no US casualties, per Ratcliffe and Trump. Whether Ghost Murmur produced the find rests on the Post's anonymous sources and Trump's “very important”. Physicists dispute the described mechanism, and a beacon and a long-range camera were also in play." }, { "group": "organizational", "depth": null, "reason": "No source quantifies the tool's contribution to the search beyond Trump's remark. The mission also used human assets, deception, a camera at 40 miles and the airman's beacon." } ], "ai_role": { "status": "unassessed", "statement": "The Post's anonymous sources describe AI that isolates a heartbeat signal from sensor noise. No public technical evidence establishes that this system located the airman. The official account confirms the rescue and camera observation, while the later presidential remark does not establish the sensing method." }, "evidence": [ { "kind": "news report", "citation": "Steven Nelson, New York Post. 'Ghost Murmur,' a never-used secret tool, deployed to find lost airman in Iran in daring mission. 2026-04-07. Original reporting based on two anonymous sources.", "title": "'Ghost Murmur,' a never-used secret tool, deployed to find lost airman in Iran in daring mission", "url": "https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/", "date": "2026-04-07", "written_by": "independent", "quote": null }, { "kind": "report", "citation": "White House; Daily Compilation of Presidential Documents. Remarks on the Rescue of United States Military Personnel in Iran and an Exchange With Reporters. 2026-04-06. DCPD-202600233, pp. 1–2 and 7. Official account of the rescue and long-range camera observation.", "title": "Remarks on the Rescue of United States Military Personnel in Iran and an Exchange With Reporters", "url": "https://www.govinfo.gov/content/pkg/DCPD-202600233/pdf/DCPD-202600233.pdf", "date": "2026-04-06", "written_by": "deployer", "quote": null }, { "kind": "news report", "citation": "Steven Nelson, New York Post. Trump confirms CIA 'Ghost Murmur' tool was ‘very important’ to find airman in Iran — as experts debate how it works. 2026-04-08. Adds a direct telephone interview with President Trump and interviews with physicists.", "title": "Trump confirms CIA 'Ghost Murmur' tool was ‘very important’ to find airman in Iran — as experts debate how it works", "url": "https://nypost.com/2026/04/08/us-news/trump-confirms-cia-ghost-murmur-tool-was-very-important-to-find-airman-in-iran-as-experts-debate-how-it-works/", "date": "2026-04-08", "written_by": "independent", "quote": null }, { "kind": "news report", "citation": "Deni Ellis Béchard, Scientific American. Is the ‘Ghost Murmur’ quantum device possible? Scientists are skeptical. 2026-04-08. Adds original expert interviews challenging the publicly described sensing mechanism.", "title": "Is the ‘Ghost Murmur’ quantum device possible? Scientists are skeptical", "url": "https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/", "date": "2026-04-08", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A rescue operation that involved personnel, aircraft, intelligence, and camera observation", "An alleged AI-assisted sensing contribution whose specifications and performance are not public" ], "must_hold": [ "Public evidence of the sensing method and its role in locating the officer would be needed before identifying reproducible conditions for an AI benefit" ], "echoes": [] }, "paired_incident": null, "headline": "*Ghost Murmur* may have found downed F-15E officer", "metric": "one rescued airman; the claimed AI contribution remains unverified", "comparator": null, "later": { "status": "challenged", "date": "2026-04-08", "note": "Physicists interviewed by the Post and Scientific American disputed the public technical description. Their criticism does not establish that the rescue claim was formally reversed or withdrawn.", "quote": null, "url": "https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": [ "White House, April 6, 2026, DCPD-202600233, pp. 1–2 and 7. https://www.govinfo.gov/content/pkg/DCPD-202600233/pdf/DCPD-202600233.pdf", "Steven Nelson, New York Post, April 7 and 8, 2026. https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/ https://nypost.com/2026/04/08/us-news/trump-confirms-cia-ghost-murmur-tool-was-very-important-to-find-airman-in-iran-as-experts-debate-how-it-works/", "Deni Ellis Béchard, Scientific American, April 8, 2026, original expert interviews. https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/" ], "brief": "The April 6 White House briefing documents the rescue of a downed American airman in Iran.[1] The claim that an AI-assisted quantum sensor located him comes from two anonymous sources in the New York Post. President Trump subsequently told the Post that the tool was important, without explaining how it worked.[2] Physicists interviewed by the Post and Scientific American questioned the described capability.[3] The rescue is established; the contribution attributed to Ghost Murmur remains unverified.", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Brief from Peter added.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Brief from Peter rewritten to say why the case matters now.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Sidenotes added to the brief.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.military.com/feature/2026/04/18/ghost-murmur-heartbeat-tracking-tech-has-experts-questioning-laws-of-physics.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Skunk_Works; https://www.cia.gov/legacy/headquarters/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Replaced the incident-monitor summary with original reporting, separated camera distance from heartbeat claims, and corrected the unsupported reversal label.", "source": "https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and the deployer's report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0328", "catalog_version": "1.0.0", "source_fingerprint": "4678006907444b0cd74c0e1b28a900499156578134b32a7632876f04ae852846", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Lockheed Martin Skunk Works, per two NY Post sources; the company declined to comment", "deployer": "US Central Intelligence Agency, per the NY Post; the CIA and White House named no tool" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "unassessed", "statement": "The Post's anonymous sources describe AI that isolates a heartbeat signal from sensor noise. No public technical evidence establishes that this system located the airman. The official account confirms the rescue and camera observation, while the later presidential remark does not establish the sensing method." } } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "The weapons systems officer was found alive on 4 April 2026 and brought out with no US casualties, per Ratcliffe and Trump. Whether Ghost Murmur produced the find rests on the Post's anonymous sources and Trump's “very important”. 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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": 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”. 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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." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "brief" ], "source_refs": [] }, "review_state": "record_mapped", "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 rescued airman; 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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. 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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": "The developer is headquartered in Israel; the case names Australia.", "details": [ { "label": "Developer headquarters", "value": "Israel" }, { "label": "Case jurisdictions", "value": "Australia" }, { "label": "Case location", "value": "Stirling Leisure - Inglewood, Perth, Western Australia, Australia" } ], "filters": { "origin": "Israel", "destination": [ "Australia" ] }, "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. 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", "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": 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" }, "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 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-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003" ], "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": "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" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "value_present", "value": { "kind": "text", "text": "Reported benefit" }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "benefit_status" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 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" }, "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-0010", "case_url": "records/AIBD-0010.html", "reason": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Meerut, India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "record": { "id": "AIBD-0010", "title": "Meta self-harm alert routed to Uttar Pradesh Police leads to hospital treatment of a 25-year-old in Meerut within eight minutes", "summary": "7 June 2026: Meta alert at 6:53 pm; Meerut police reached a 25-year-old in 8 minutes, found him critical; hospital treatment, improved", "date_observed": "2026-06-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 7 June 2026 a 25-year-old man from the Sardhana area of Meerut, Uttar Pradesh, posted an Instagram video. In it he appeared to drink a white liquid. Police said Meta sent a suicide alert about the post to the Social Media Center at Uttar Pradesh Police headquarters at 6:53 pm. The center alerted Meerut Police, and a Sardhana team reached his home within eight minutes. They found him in a critical condition with a bottle of suspected liquid nearby and took him to hospital, where his condition improved after treatment. He told police he was under stress following the end of a relationship. Police data cited by PTI credit such alerts with 3,011 lives saved statewide from 1 January 2023 to 31 May 2026.", "category": "harm prevented", "unexpected": false, "purpose": "Built to flag posts and live videos indicating suicide or self-harm risk for review and, in imminent cases, referral to emergency services. This benefit is that purpose.", "system": "Meta proactive self-harm detection on Instagram with alert routing to the Uttar Pradesh Police Social Media Center", "model_version": null, "developer": "Meta Platforms", "deployer": "Meta Platforms with the Uttar Pradesh Police Social Media Center (agreement signed March 2022)", "location": "Meerut, India", "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police", "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health.", "registry_ids": { "oecd_aim": "2026-06-08-7d81" }, "ratings": [ { "group": "personal", "depth": 4, "reason": "Police found him in a critical condition after apparent ingestion, and he was treated in hospital within minutes of the alert. Police describe the outcome as a life saved." } ], "ai_role": { "status": "assessed", "statement": "Police sources say Meta sent the alert after the post. They do not state whether Meta's classifier or a user report flagged it. Meta's documentation and NPR describe the process as machine-learning detection with human review before contacting authorities. The record relies on that description and should be read with that caveat." }, "evidence": [ { "kind": "news report", "citation": "PTI (2026). When every minute counts: Uttar Pradesh Police save youth attempting suicide in 8 minutes after Meta alert over Instagram post. Deccan Herald.", "title": "When every minute counts: Uttar Pradesh Police save youth attempting suicide in 8 minutes after Meta alert over Instagram post", "url": "https://www.deccanherald.com/india/uttar-pradesh/when-every-minute-counts-uttar-pradesh-police-save-youth-attempting-suicide-in-8-minutes-after-meta-alert-over-instagram-post-4032393", "date": "2026-06-09", "written_by": "independent", "quote": "flags posts indicating possible self-harm and shares alerts with authorities" }, { "kind": "news report", "citation": "The Hans India (2026). Cops save man in 8 mins after suicide alert on Insta.", "title": "Cops save man in 8 mins after suicide alert on Insta", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "date": "2026-06-10", "written_by": "independent", "quote": "Police said Meta sent a suicide alert regarding the post to the Social Media Centre at the police headquarters at 6.53 pm." }, { "kind": "news report", "citation": "Kaste, M. (2018). Facebook Increasingly Reliant On A.I. To Predict Suicide Risk. NPR (transcript republished by North Country Public Radio).", "title": "Facebook Increasingly Reliant On A.I. To Predict Suicide Risk", "url": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk", "date": "2018-11-17", "written_by": "independent", "quote": "We also are able to use AI to coordinate a bunch of information on location to try to identify the location of that individual" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom.", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "In serious cases, when it's determined that there may be imminent danger of self harm, Facebook may contact local authorities." }, { "kind": "news report", "citation": "PTI (2023). Facebook helps UP Police avert two suicides in a week in state. The Tribune.", "title": "Facebook helps UP Police avert two suicides in a week in state", "url": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp", "date": "2023-02-05", "written_by": "independent", "quote": "Meta Hqs USA immediately sends an alert to the social media centre of PHQ UP Police through phone and email" }, { "kind": "news report", "citation": "PTI (2021). Man saved by Delhi Police after Facebook alerts on self-harm video. The Tribune.", "title": "Man saved by Delhi Police after Facebook alerts on self-harm video", "url": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp", "date": "2021-06-05", "written_by": "independent", "quote": "received a call alert from Facebook's US office about a suspicious self-harm live video being posted by a male Facebook user located in Delhi" } ], "replication": { "produced_by": [ "Platform detection and review that flags self-harm posts and contacts a police nodal center with account phone and location details", "Police nodal center with a standing arrangement to dispatch local officers immediately" ], "must_hold": [ "Account details resolve to a reachable address within minutes", "Local officers are available to respond immediately" ], "echoes": [ { "date": null, "setting": "Dwarka, Delhi (reported 5 June 2021): Delhi Police CyPAD received a Facebook alert at about 12:50 am on a self-harm live video; the man was found with heavy blood loss, taken to hospital and AIIMS Trauma Center, condition stable (PTI/The Tribune)", "model_version": null, "outcome": "recurred", "source": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp" }, { "date": null, "setting": "Ambedkarnagar, Uttar Pradesh (late January 2023): Meta alert at 11:37 pm on a student's Instagram photos of pills; police arrived within 15 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "model_version": null, "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "date": "2023-01-31", "setting": "Ghaziabad, Uttar Pradesh: Instagram live of a man preparing to hang himself; Inspector Anita Chauhan arrived within 13 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "model_version": null, "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "date": null, "setting": "Upstate New York, from a Facebook promotional video described by NPR (17 November 2018): officers found a woman who had taken medication and got her to a local hospital", "model_version": null, "outcome": "recurred", "source": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "382", "title": "Instagram's Exposure of Harmful Content Allegedly Contributed to Teenage Girl's Suicide", "url": "https://incidentdatabase.ai/cite/382", "severity": null, "group": "personal", "basis": "Same developer and platform, Meta's Instagram, and the same at-risk user group: Instagram's recommendation of suicide and self-harm content was ruled to have contributed to Molly Russell's death, where the Meerut alert reached a user in time." }, "headline": "Meta self-harm alert gets Meerut police to a man in 8 minutes", "metric": "3,011 lives saved statewide from 1 January 2023 to 31 May 2026", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No development dated after the 7 June 2026 case was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 382 (high confidence); 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This benefit is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-06-07", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 382 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/382", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Linked the OECD AI Incidents entry, which describes this event.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Platform detection and review that flags self-harm posts and contacts a police nodal center with account phone and location details", "Police nodal center with a standing arrangement to dispatch local officers immediately" ], "must_hold": [ "Account details resolve to a reachable address within minutes", "Local officers are available to respond immediately" ], "echoes": [ { "setting": "Dwarka, Delhi (reported 5 June 2021): Delhi Police CyPAD received a Facebook alert at about 12:50 am on a self-harm live video; the man was found with heavy blood loss, taken to hospital and AIIMS Trauma Center, condition stable (PTI/The Tribune)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp" }, { "setting": "Ambedkarnagar, Uttar Pradesh (late January 2023): Meta alert at 11:37 pm on a student's Instagram photos of pills; police arrived within 15 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "date": "2023-01-31", "setting": "Ghaziabad, Uttar Pradesh: Instagram live of a man preparing to hang himself; Inspector Anita Chauhan arrived within 13 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "setting": "Upstate New York, from a Facebook promotional video described by NPR (17 November 2018): officers found a woman who had taken medication and got her to a local hospital", "outcome": "recurred", "source": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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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": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Raebareli, India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "record": { "id": "AIBD-0159", "title": "Meta flags a Raebareli 18-year-old's Instagram suicide post at 8:58 pm; Uttar Pradesh Police reach him within 12 minutes after he took rat poison, and he is treated and stable", "summary": "29 March 2026: Meta alert at 8:58 pm; Raebareli police reached an 18-year-old in 12 minutes after rat poison; hospital treatment, stable", "date_observed": "2026-03-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 29 March 2026 an 18-year-old class 12 student from Raebareli, Uttar Pradesh, texted his girlfriend “Alvida” after a breakup. He lived within the Mill Area police station limits. He also posted a suicide video on Instagram with rat poison and messages of intent. At about 8:58 pm Meta flagged the content and emailed an alert to the Social Media Center at Uttar Pradesh Police headquarters. The center traced his mobile number and location and alerted Raebareli police. A police team reached his home within 12 minutes and found him in severe distress, with an empty rat-poison packet nearby. Police and family took him to a nearby hospital, where he was treated. On 1 April 2026 police said he was stable.", "category": "harm prevented", "unexpected": false, "purpose": "Meta's detection and review system is built to find posts and live videos that indicate suicide or self-injury risk. It routes them to trained reviewers and, in imminent cases, contacts local authorities. The Uttar Pradesh Police Social Media Center exists to act on those alerts. This rescue is that purpose.", "system": "Meta's suicide and self-injury detection and review pipeline on Instagram, with email alerts to the Uttar Pradesh Police Social Media Center at DGP headquarters", "model_version": null, "developer": "Meta Platforms", "deployer": "Meta Platforms with the Uttar Pradesh Police Social Media Center (Decode: Uttar Pradesh has been part of Meta's suicide-prevention program since 2022)", "location": "Raebareli, India", "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police", "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health.", "registry_ids": { "oecd_aim": "2026-04-01-6e56" }, "ratings": [ { "group": "personal", "depth": 4, "reason": "An 18-year-old who had taken rat poison was reached within 12 minutes of Meta's 8:58 pm alert. He was treated in hospital and reported stable. Officials said even a slight delay could have been fatal. No clinician is quoted and the rodenticide is not identified. The life-saving reading rests on the police account." } ], "ai_role": { "status": "assessed", "statement": "Police say Meta flagged the Instagram post at about 8:58 pm on 29 March and emailed the Uttar Pradesh Police Social Media Center. No source says whether Meta's classifier or a user report started the review. Decode reports that UP alerts arise from “artificial intelligence systems or user reports” reviewed by Meta staff. Everything after the email was human work. The center traced the phone number and location. Raebareli police reached the home in 12 minutes on the DGP's instructions, and a hospital treated him." }, "evidence": [ { "kind": "news report", "citation": "TNN (2026). Cops save 18-yr-old from suicide after Meta alert. The Times of India, Lucknow.", "title": "Cops save 18-yr-old from suicide after Meta alert", "url": "https://timesofindia.indiatimes.com/city/lucknow/cops-save-18-yr-old-from-suicide-after-meta-alert/articleshow/129935943.cms", "date": "2026-04-01", "written_by": "independent", "quote": "At around 8:58pm, Meta flagged the content and sent an alert via email to the social media centre at the UP police headquarters." }, { "kind": "news report", "citation": "IANS (2026). Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert. Telangana Today.", "title": "Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert", "url": "https://telanganatoday.com/teens-life-saved-in-12-minutes-up-police-act-swiftly-after-social-media-suicide-alert", "date": "2026-04-01", "written_by": "independent", "quote": "The information was received by the police through Meta. Action was taken within minutes, and he was saved." }, { "kind": "news report", "citation": "IANS (2026). Teen's life saved in 12 minutes: Raebareli police act swiftly after social media suicide alert. IANS Live.", "title": "Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert", "url": "https://ianslive.in/teens-life-saved-in-12-minutes-raebareli-police-act-swiftly-after-social-media-suicide-alert--20260401091324", "date": "2026-04-01", "written_by": "independent", "quote": "a video related to suicide was posted on Instagram, triggering an alert through the Meta Alert System at the police headquarters" }, { "kind": "news report", "citation": "Mathrubhumi English (2026). 'Alvida' text, poison post, and a 12-min race against death: How UP police saved a teen.", "title": "'Alvida' text, poison post, and a 12-min race against death: How UP police saved a teen", "url": "https://english.mathrubhumi.com/news/good-news/up-police-rescue-teen-social-media-alert-ar7x14gi", "date": "2026-04-01", "written_by": "independent", "quote": "police in Raebareli saved an 18-year-old youth after a distressing Instagram post triggered an automated alert through Meta's monitoring system" }, { "kind": "news report", "citation": "Srivastava, S. (2026). Meta Flagged A Suicide Post. Police Had Minutes To Save The Man. Decode (BOOM).", "title": "Meta Flagged A Suicide Post. Police Had Minutes To Save The Man", "url": "https://www.decodeinternet.in/decode/meta-sent-suicide-alert-uttar-pradesh-police-had-minutes-to-save-30394", "date": "2026-01-06", "written_by": "independent", "quote": "flagged—by artificial intelligence systems or user reports—as potentially indicating self-harm are reviewed by Meta and, in high-risk cases, shared with law enforcement partners" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom.", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "In serious cases, when it’s determined that there may be imminent danger of self harm, Facebook may contact local authorities." }, { "kind": "press release", "citation": "Rosen, G. (2017). Getting Our Community Help in Real Time. Meta Newsroom.", "title": "Getting Our Community Help in Real Time", "url": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/", "date": "2017-11-27", "written_by": "makers", "quote": "Using pattern recognition to detect posts or live videos where someone might be expressing thoughts of suicide" }, { "kind": "news report", "citation": "The Hans India (2026). Cops save man in 8 mins after suicide alert on Insta. The Hans India, 10 June 2026.", "title": "Cops save man in 8 mins after suicide alert on Insta", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "date": "2026-06-10", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "An Instagram post with rat poison and messages of intent, after a breakup. Meta's detection and review system flagged it as a suicide risk at about 8:58 pm on 29 March 2026", "A standing arrangement, in place since 2022 per Decode, under which Meta emails such alerts to the Uttar Pradesh Police Social Media Center. The center traces the account's phone number and location and forwards them to district police", "A Raebareli police team able to reach the Mill Area address within 12 minutes and take him to a nearby hospital with his family" ], "must_hold": [ "The post is flagged and reviewed while the person can still be treated. Here the alert came at about 8:58 pm and police arrived within 12 minutes", "The account leads to a phone number and a reachable address. Decode reports that UP police usually receive a phone number and trace the SIM and live location", "Officers and a hospital are available at once. Officials said even a slight delay could have been fatal" ], "echoes": [] }, "paired_incident": null, "headline": "Meta flags a suicide post; Raebareli police arrive in 12 minutes", "metric": "A police team reached his home within 12 minutes", "comparator": { "against": "reports escalated without Meta's pattern-recognition acceleration", "before": "escalation speed of other reports", "after": "escalated to local authorities twice as quickly as other reports", "basis": "measured", "quote": "We also use pattern recognition to help accelerate the most concerning reports. We’ve found these accelerated reports— that we have signaled require immediate attention—are escalated to local authorities twice as quickly as other reports.", "url": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/" }, "later": { "status": "persisted", "date": "2026-06-10", "note": "A further Meta alert on 7 June 2026 led Meerut police to reach a 25-year-old within eight minutes of a suicide post and get him to hospital.", "quote": "A 25-year-old man’s life was saved after Meerut Police acted on a suicide alert received from Meta following an Instagram post, reaching him within eight minutes and getting him admitted to a hospital, officials have said.", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-01-6e56", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/; https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0159", "catalog_version": "1.0.0", "source_fingerprint": "24892167a8185c5d54ff449ed7c0697ebac643babb4fe7e28ef0fa4aa89df921", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0159-source-0001", "AIBD-0159-source-0002", "AIBD-0159-source-0003", "AIBD-0159-source-0004", "AIBD-0159-source-0005", "AIBD-0159-source-0006", "AIBD-0159-source-0007", "AIBD-0159-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Meta Platforms", "deployer": "Meta Platforms with the Uttar Pradesh Police Social Media Center (Decode: Uttar Pradesh has been part of Meta's suicide-prevention program since 2022)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Police say Meta flagged the Instagram post at about 8:58 pm on 29 March and emailed the Uttar Pradesh Police Social Media Center. 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" ], "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": "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." } } }, "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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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" }, "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": "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. 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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": "A police team reached his home within 12 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": "2026-04-01-6e56" } } }, "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-0239", "case_url": "records/AIBD-0239.html", "reason": "The developer is headquartered in United States; the case names Brazil.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Brazil" }, { "label": "Case location", "value": "São Gabriel da Palha, Brazil" } ], "filters": { "origin": "United States", "destination": [ "Brazil" ] }, "record": { "id": "AIBD-0239", "title": "OpenAI alert relayed through the FBI leads Espírito Santo police to detain a father a day before his stated date to kill his son", "summary": "OpenAI flagged a user's plan to kill his 8-year-old son; relayed via the FBI, Brazilian police held him a day before his stated date.", "date_observed": "2026-06-19", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In 2026 a 36-year-old farmer in São Gabriel da Palha, Espírito Santo, Brazil, told ChatGPT about a plan to kill his 8-year-old son. He said he had offered R$50,000 to a hitman and set 20 June as the date. OpenAI's safety systems flagged the account, and the company referred it to the FBI. The FBI passed it to the Ministry of Justice's CyberLab. The Polícia Civil received it on 16 June and, under warrants, arrested him on 19 June. Officers seized his phone, four bottles of an unidentified substance and a knotted rope. He admitted the searches but denied intent. He was freed on habeas corpus on 13 August 2026 after 54 days, under an ankle monitor and a no-contact order. The inquiry continues.", "category": "protective response", "unexpected": false, "purpose": "ChatGPT is a general-purpose conversational assistant. OpenAI's moderation layer is built to route conversations that indicate plans to harm others to human reviewers. Those reviewers may refer an imminent threat to law enforcement, under a policy published 26 August 2025. The referral that triggered the arrest was that layer doing what it was built to do, not an emergent use of the assistant.", "system": "ChatGPT with OpenAI's safety-monitoring pipeline (automated classifiers plus human review and law-enforcement referral)", "model_version": null, "developer": "OpenAI", "deployer": "OpenAI", "location": "São Gabriel da Palha, Brazil", "domain": "Public safety", "segment": "Law enforcement and child protection", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-09-16-2a33" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "The 8-year-old, and his mother who was placed under a no-contact order, were shielded from a plan the father had dated to 20 June. Police say the platform's information was corroborated when they arrived. He denies intent and no charge has been filed, so a life-saving effect is not established." }, { "group": "organizational", "depth": 2, "reason": "The Polícia Civil do Espírito Santo obtained a lead through the FBI and the Ministry of Justice CyberLab. It says it had no other way to get it. Its cybercrime chief called the alert another path for police work. The Ministry rated it the gravest of three such alerts in Brazil." } ], "ai_role": { "status": "assessed", "statement": "OpenAI's automated classifiers and human reviewers flagged the user's messages, and the company chose to refer the account to the FBI. The system did not predict a crime or act on its own. The warrants, the arrest and the evidence-gathering were police and court actions. OpenAI sent only the user's prompts, not ChatGPT's replies. What the assistant said to the user, and whether it helped or hindered him, is unknown." }, "evidence": [ { "kind": "report", "citation": "Polícia Civil do Espírito Santo (2026). Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial. pc.es.gov.br.", "title": "Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial", "url": "https://pc.es.gov.br/Not%C3%ADcia/alerta-do-fbi-e-do-ministerio-da-justica-auxiliam-pces-na-prisao-de-investigado-por-ameacas-contra-o-proprio-filho-em-inteligencia-artificial", "date": "2026-06-26", "written_by": "beneficiary", "quote": "Recebemos a denúncia no dia 16 de junho. Nas conversas analisadas, ele mencionava que os atos ocorreriam no dia 20." }, { "kind": "news report", "citation": "g1 Espírito Santo (2026). Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT. g1.globo.com.", "title": "Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT", "url": "https://g1.globo.com/es/espirito-santo/norte-noroeste-es/noticia/2026/06/25/pai-e-preso-por-planejar-a-morte-do-filho-para-nao-pagar-pensao-e-confessar-crime-ao-chatgpt.ghtml", "date": "2026-06-25", "written_by": "independent", "quote": "no último dia 19, um dia antes da data em que o crime aconteceria, segundo o delegado adjunto" }, { "kind": "news report", "citation": "Freitas, C. / A Gazeta (2026). Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES. agazeta.com.br.", "title": "Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES", "url": "https://www.agazeta.com.br/es/policia/conversa-com-ia-ajuda-policia-a-prender-pai-por-planejar-morte-de-filho-no-es-0626", "date": "2026-06-25", "written_by": "independent", "quote": "Os elementos foram fornecidos pela própria empresa responsável por essa inteligência artificial." }, { "kind": "news report", "citation": "Folha de S.Paulo (2026). Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta. folha.uol.com.br.", "title": "Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta", "url": "https://www1.folha.uol.com.br/cotidiano/2026/06/homem-e-preso-sob-suspeita-de-usar-ia-para-planejar-morte-de-filho-de-8-anos-plataforma-emitiu-alerta.shtml", "date": "2026-06-26", "written_by": "independent", "quote": "a OpenAI, desenvolvedora do ChatGPT, foi quem reportou ao FBI que um usuário realizava pesquisas frequentes relacionadas à intenção de matar o próprio filho" }, { "kind": "news report", "citation": "g1 Tecnologia (2026). Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades. g1.globo.com.", "title": "Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades", "url": "https://g1.globo.com/tecnologia/noticia/2026/06/27/como-chatgpt-identificou-plano-de-pai-para-matar-o-filho-e-levou-alerta-para-autoridades.ghtml", "date": "2026-06-27", "written_by": "independent", "quote": "Quando identificamos conversas que indicam um risco iminente e crível de dano a outras pessoas, podemos notificar as autoridades competentes" }, { "kind": "news report", "citation": "BBC News Brasil (2026). Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil? bbc.com/portuguese.", "title": "Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil?", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "date": "2026-08-14", "written_by": "independent", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa" }, { "kind": "press release", "citation": "OpenAI (2025). Helping people when they need it most. openai.com.", "title": "Helping people when they need it most", "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "date": "2025-08-26", "written_by": "makers", "quote": "If human reviewers determine that a case involves an imminent threat of serious physical harm to others, we may refer it to law enforcement." } ], "replication": { "produced_by": [ "The user typed explicit, dated threats against an identifiable child into ChatGPT over about two months, naming means and a hired killer", "OpenAI's moderation process, automated classifiers plus human review, flagged the account. The company chose to refer it to the FBI under its policy on imminent threats to others", "A standing FBI-to-Brazil channel through the Ministry of Justice CyberLab relayed the file to the state police within days", "The Polícia Civil do Espírito Santo prioritized the case, identified the user, confirmed the child existed and obtained warrants before the stated date" ], "must_hold": [ "The user discloses the plan to the assistant rather than keeping it offline", "The vendor's reviewers judge the threat credible and imminent and choose referral rather than only an account ban", "A cross-border law-enforcement channel exists and acts within days", "Local police can identify the user from the vendor's data and secure judicial warrants before the stated date" ], "echoes": [] }, "paired_incident": null, "headline": "OpenAI flag leads Brazilian police to hold a father a day early", "metric": "held him a day before his stated date", "comparator": null, "later": { "status": "litigated", "date": "2026-08-13", "note": "He was released on 13 August 2026 after a court granted habeas corpus, under an ankle monitor and a no-contact order, with the inquiry continuing.", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-09-16-2a33", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/OpenAI; https://en.wikipedia.org/wiki/OpenAI", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports, the makers' press release and the beneficiary's report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0239", "catalog_version": "1.0.0", "source_fingerprint": "40a6b91f6da11e7b61e17b845b9c4d3689ceaeaf7a17db97c70ddc003eb24a85", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0239-source-0001", "AIBD-0239-source-0002", "AIBD-0239-source-0003", "AIBD-0239-source-0004", "AIBD-0239-source-0005", "AIBD-0239-source-0006", "AIBD-0239-source-0007" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "OpenAI", "deployer": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "OpenAI's automated classifiers and human reviewers flagged the user's messages, and the company chose to refer the account to the FBI. 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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": "held him a day before his stated date", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": 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granted habeas corpus, under an ankle monitor and a no-contact order, with the inquiry continuing.", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0239" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2026 a 36-year-old farmer in São Gabriel da Palha, Espírito Santo, Brazil, told ChatGPT about a plan to kill his 8-year-old son. He said he had offered R$50,000 to a hitman and set 20 June as the date. OpenAI's safety systems flagged the account, and the company referred it to the FBI. The FBI passed it to the Ministry of Justice's CyberLab. The Polícia Civil received it on 16 June and, under warrants, arrested him on 19 June. Officers seized his phone, four bottles of an unidentified substance and a knotted rope. He admitted the searches but denied intent. He was freed on habeas corpus on 13 August 2026 after 54 days, under an ankle monitor and a no-contact order. The inquiry continues.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "The 8-year-old, and his mother who was placed under a no-contact order, were shielded from a plan the father had dated to 20 June. Police say the platform's information was corroborated when they arrived. He denies intent and no charge has been filed, so a life-saving effect is not established." }, { "group": "organizational", "depth": 2, "reason": "The Polícia Civil do Espírito Santo obtained a lead through the FBI and the Ministry of Justice CyberLab. It says it had no other way to get it. Its cybercrime chief called the alert another path for police work. The Ministry rated it the gravest of three such alerts in Brazil." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "ChatGPT is a general-purpose conversational assistant. OpenAI's moderation layer is built to route conversations that indicate plans to harm others to human reviewers. Those reviewers may refer an imminent threat to law enforcement, under a policy published 26 August 2025. 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The referral that triggered the arrest was that layer doing what it was built to do, not an emergent use of the assistant." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-06-19", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-09-16-2a33", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/OpenAI; https://en.wikipedia.org/wiki/OpenAI", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "The user typed explicit, dated threats against an identifiable child into ChatGPT over about two months, naming means and a hired killer", "OpenAI's moderation process, automated classifiers plus human review, flagged the account. The company chose to refer it to the FBI under its policy on imminent threats to others", "A standing FBI-to-Brazil channel through the Ministry of Justice CyberLab relayed the file to the state police within days", "The Polícia Civil do Espírito Santo prioritized the case, identified the user, confirmed the child existed and obtained warrants before the stated date" ], "must_hold": [ "The user discloses the plan to the assistant rather than keeping it offline", "The vendor's reviewers judge the threat credible and imminent and choose referral rather than only an account ban", "A cross-border law-enforcement channel exists and acts within days", "Local police can identify the user from the vendor's data and secure judicial warrants before the stated date" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-09-16-2a33", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/OpenAI; https://en.wikipedia.org/wiki/OpenAI", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Public safety", "segment": "Law enforcement and child protection" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Polícia Civil do Espírito Santo (2026). Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial. pc.es.gov.br.", "title": "Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial", "url": "https://pc.es.gov.br/Not%C3%ADcia/alerta-do-fbi-e-do-ministerio-da-justica-auxiliam-pces-na-prisao-de-investigado-por-ameacas-contra-o-proprio-filho-em-inteligencia-artificial", "date": "2026-06-26", "written_by": "beneficiary", "quote": "Recebemos a denúncia no dia 16 de junho. Nas conversas analisadas, ele mencionava que os atos ocorreriam no dia 20." }, { "kind": "news report", "citation": "g1 Espírito Santo (2026). Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT. g1.globo.com.", "title": "Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT", "url": "https://g1.globo.com/es/espirito-santo/norte-noroeste-es/noticia/2026/06/25/pai-e-preso-por-planejar-a-morte-do-filho-para-nao-pagar-pensao-e-confessar-crime-ao-chatgpt.ghtml", "date": "2026-06-25", "written_by": "independent", "quote": "no último dia 19, um dia antes da data em que o crime aconteceria, segundo o delegado adjunto" }, { "kind": "news report", "citation": "Freitas, C. / A Gazeta (2026). Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES. agazeta.com.br.", "title": "Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES", "url": "https://www.agazeta.com.br/es/policia/conversa-com-ia-ajuda-policia-a-prender-pai-por-planejar-morte-de-filho-no-es-0626", "date": "2026-06-25", "written_by": "independent", "quote": "Os elementos foram fornecidos pela própria empresa responsável por essa inteligência artificial." }, { "kind": "news report", "citation": "Folha de S.Paulo (2026). Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta. folha.uol.com.br.", "title": "Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta", "url": "https://www1.folha.uol.com.br/cotidiano/2026/06/homem-e-preso-sob-suspeita-de-usar-ia-para-planejar-morte-de-filho-de-8-anos-plataforma-emitiu-alerta.shtml", "date": "2026-06-26", "written_by": "independent", "quote": "a OpenAI, desenvolvedora do ChatGPT, foi quem reportou ao FBI que um usuário realizava pesquisas frequentes relacionadas à intenção de matar o próprio filho" }, { "kind": "news report", "citation": "g1 Tecnologia (2026). Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades. g1.globo.com.", "title": "Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades", "url": "https://g1.globo.com/tecnologia/noticia/2026/06/27/como-chatgpt-identificou-plano-de-pai-para-matar-o-filho-e-levou-alerta-para-autoridades.ghtml", "date": "2026-06-27", "written_by": "independent", "quote": "Quando identificamos conversas que indicam um risco iminente e crível de dano a outras pessoas, podemos notificar as autoridades competentes" }, { "kind": "news report", "citation": "BBC News Brasil (2026). Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil? bbc.com/portuguese.", "title": "Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil?", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "date": "2026-08-14", "written_by": "independent", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa" }, { "kind": "press release", "citation": "OpenAI (2025). Helping people when they need it most. openai.com.", "title": "Helping people when they need it most", "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "date": "2025-08-26", "written_by": "makers", "quote": "If human reviewers determine that a case involves an imminent threat of serious physical harm to others, we may refer it to law enforcement." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0239-source-0001", "AIBD-0239-source-0002", "AIBD-0239-source-0003", "AIBD-0239-source-0004", "AIBD-0239-source-0005", "AIBD-0239-source-0006", "AIBD-0239-source-0007" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Polícia Civil do Espírito Santo (2026). Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial. pc.es.gov.br.", "title": "Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial", "url": "https://pc.es.gov.br/Not%C3%ADcia/alerta-do-fbi-e-do-ministerio-da-justica-auxiliam-pces-na-prisao-de-investigado-por-ameacas-contra-o-proprio-filho-em-inteligencia-artificial", "date": "2026-06-26", "written_by": "beneficiary", "quote": "Recebemos a denúncia no dia 16 de junho. Nas conversas analisadas, ele mencionava que os atos ocorreriam no dia 20." }, { "kind": "news report", "citation": "g1 Espírito Santo (2026). Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT. g1.globo.com.", "title": "Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT", "url": "https://g1.globo.com/es/espirito-santo/norte-noroeste-es/noticia/2026/06/25/pai-e-preso-por-planejar-a-morte-do-filho-para-nao-pagar-pensao-e-confessar-crime-ao-chatgpt.ghtml", "date": "2026-06-25", "written_by": "independent", "quote": "no último dia 19, um dia antes da data em que o crime aconteceria, segundo o delegado adjunto" }, { "kind": "news report", "citation": "Freitas, C. / A Gazeta (2026). Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES. agazeta.com.br.", "title": "Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES", "url": "https://www.agazeta.com.br/es/policia/conversa-com-ia-ajuda-policia-a-prender-pai-por-planejar-morte-de-filho-no-es-0626", "date": "2026-06-25", "written_by": "independent", "quote": "Os elementos foram fornecidos pela própria empresa responsável por essa inteligência artificial." }, { "kind": "news report", "citation": "Folha de S.Paulo (2026). Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta. folha.uol.com.br.", "title": "Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta", "url": "https://www1.folha.uol.com.br/cotidiano/2026/06/homem-e-preso-sob-suspeita-de-usar-ia-para-planejar-morte-de-filho-de-8-anos-plataforma-emitiu-alerta.shtml", "date": "2026-06-26", "written_by": "independent", "quote": "a OpenAI, desenvolvedora do ChatGPT, foi quem reportou ao FBI que um usuário realizava pesquisas frequentes relacionadas à intenção de matar o próprio filho" }, { "kind": "news report", "citation": "g1 Tecnologia (2026). Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades. g1.globo.com.", "title": "Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades", "url": "https://g1.globo.com/tecnologia/noticia/2026/06/27/como-chatgpt-identificou-plano-de-pai-para-matar-o-filho-e-levou-alerta-para-autoridades.ghtml", "date": "2026-06-27", "written_by": "independent", "quote": "Quando identificamos conversas que indicam um risco iminente e crível de dano a outras pessoas, podemos notificar as autoridades competentes" }, { "kind": "news report", "citation": "BBC News Brasil (2026). Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil? bbc.com/portuguese.", "title": "Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil?", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "date": "2026-08-14", "written_by": "independent", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa" }, { "kind": "press release", "citation": "OpenAI (2025). Helping people when they need it most. openai.com.", "title": "Helping people when they need it most", "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "date": "2025-08-26", "written_by": "makers", "quote": "If human reviewers determine that a case involves an imminent threat of serious physical harm to others, we may refer it to law enforcement." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "OpenAI flagged a user's plan to kill his 8-year-old son; relayed via the FBI, Brazilian police held him a day before his stated date.", "account": "In 2026 a 36-year-old farmer in São Gabriel da Palha, Espírito Santo, Brazil, told ChatGPT about a plan to kill his 8-year-old son. He said he had offered R$50,000 to a hitman and set 20 June as the date. OpenAI's safety systems flagged the account, and the company referred it to the FBI. The FBI passed it to the Ministry of Justice's CyberLab. The Polícia Civil received it on 16 June and, under warrants, arrested him on 19 June. Officers seized his phone, four bottles of an unidentified substance and a knotted rope. He admitted the searches but denied intent. He was freed on habeas corpus on 13 August 2026 after 54 days, under an ankle monitor and a no-contact order. The inquiry continues." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ChatGPT with OpenAI's safety-monitoring pipeline (automated classifiers plus human review and law-enforcement referral)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "OpenAI alert relayed through the FBI leads Espírito Santo police to detain a father a day before his stated date to kill his son", "headline": "OpenAI flag leads Brazilian police to hold a father a day early" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "40a6b91f6da11e7b61e17b845b9c4d3689ceaeaf7a17db97c70ddc003eb24a85" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2026 a 36-year-old farmer in São Gabriel da Palha, Espírito Santo, Brazil, told ChatGPT about a plan to kill his 8-year-old son. He said he had offered R$50,000 to a hitman and set 20 June as the date. OpenAI's safety systems flagged the account, and the company referred it to the FBI. The FBI passed it to the Ministry of Justice's CyberLab. The Polícia Civil received it on 16 June and, under warrants, arrested him on 19 June. Officers seized his phone, four bottles of an unidentified substance and a knotted rope. He admitted the searches but denied intent. He was freed on habeas corpus on 13 August 2026 after 54 days, under an ankle monitor and a no-contact order. The inquiry continues.", "replication": { "produced_by": [ "The user typed explicit, dated threats against an identifiable child into ChatGPT over about two months, naming means and a hired killer", "OpenAI's moderation process, automated classifiers plus human review, flagged the account. The company chose to refer it to the FBI under its policy on imminent threats to others", "A standing FBI-to-Brazil channel through the Ministry of Justice CyberLab relayed the file to the state police within days", "The Polícia Civil do Espírito Santo prioritized the case, identified the user, confirmed the child existed and obtained warrants before the stated date" ], "must_hold": [ "The user discloses the plan to the assistant rather than keeping it offline", "The vendor's reviewers judge the threat credible and imminent and choose referral rather than only an account ban", "A cross-border law-enforcement channel exists and acts within days", "Local police can identify the user from the vendor's data and secure judicial warrants before the stated date" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0209", "case_url": "records/AIBD-0209.html", "reason": "The developer is headquartered in United States; the case names Oman.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Oman" }, { "label": "Case location", "value": "Off the coast of Oman, near the Strait of Hormuz" } ], "filters": { "origin": "United States", "destination": [ "Oman" ] }, "record": { "id": "AIBD-0209", "title": "Saronic Corsair sea drone picks up two Apache crew off Oman and carries them to a helicopter hoist", "summary": "A Navy Corsair sea drone picked up two Apache crew off Oman and carried them to a helicopter hoist; CENTCOM reported that both were rescued within about two hours.", "date_observed": "2026-06-09", "date_basis": "event", "date_precision": "day", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "U.S. Central Command reported that two Army AH-64 Apache crew members were rescued at 7:33 p.m. Eastern time on June 8, 2026, or 3:33 a.m. on June 9 in Oman. That is the rescue time, not the time that the helicopter went down. CENTCOM said that recovery took approximately two hours and that both crew members were stable. In a direct interview with DefenseScoop, Capt. Tim Hawkins identified the vessel as a Saronic Corsair operated by the Navy’s Task Force 59. He said that it picked up the crew and carried them to another point on the water for a helicopter hoist. The official release said that the cause of the helicopter’s loss was under investigation. The sources do not identify which autonomous functions were used during the rescue.", "category": "protective response", "unexpected": true, "purpose": "The Corsair is an autonomous maritime platform that Task Force 59 had been fielding since late March 2026. In this case, the military used it to carry stranded crew members to a helicopter recovery point.", "system": "Saronic Corsair unmanned (autonomous) surface vessel operated by U.S. 5th Fleet's Task Force 59", "model_version": "Corsair; navigation-software version not reported", "developer": "Saronic Technologies (Texas)", "deployer": "U.S. Navy, 5th Fleet Task Force 59 (U.S. Naval Forces Central Command)", "location": "Off the coast of Oman, near the Strait of Hormuz", "domain": "Military search and rescue / personnel recovery at sea", "segment": "Government, security, and defense (naval unmanned surface vessels)", "benefit_type": "Improved Physical Health", "taxonomy_note": "An at-sea rescue of aircrew is read as a health outcome because no rescue type exists.", "registry_ids": { "oecd_aim": "2026-06-09-8f45" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "Two aviators were recovered from the water and transferred to a helicopter within approximately two hours, according to CENTCOM. Their condition was stable. The sources do not establish the counterfactual risk of death or whether another rescue asset could have reached them." }, { "group": "organizational", "depth": 2, "reason": "The military recovered two crew members using an unmanned vessel and a helicopter. The sources describe a successful operational recovery but do not isolate the AI’s contribution or quantify an organizational saving." } ], "ai_role": { "status": "unassessed", "statement": "Hawkins identified an unmanned Corsair as the recovery vessel. DefenseScoop describes the platform’s autonomous capabilities, but its suggestion that passive sensing helped locate the crew is an inference. The official sources do not identify the navigation mode, human supervision, or sensor contribution during this rescue. A crewed helicopter completed the recovery." }, "evidence": [ { "kind": "press release", "citation": "U.S. Central Command Public Affairs, “U.S. Army Crew Safely Rescued After Helicopter Lost at Sea,” June 9, 2026, DVIDS story 567214.", "title": "U.S. Army Crew Safely Rescued After Helicopter Lost at Sea", "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "date": "2026-06-09", "written_by": "deployer", "quote": "The Soldiers were safely rescued within approximately two hours and are in stable condition." }, { "kind": "news report", "citation": "Brandi Vincent, “Autonomous Corsair maritime drone rescues US military pilots after crash near Oman,” DefenseScoop, June 9, 2026; direct interview with CENTCOM spokesperson Capt. Tim Hawkins.", "title": "Autonomous Corsair maritime drone rescues US military pilots after crash near Oman", "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "date": "2026-06-09", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A Corsair already deployed with Task Force 59, which began fielding these vessels in late March 2026", "A vessel able to carry the crew to a location that a rescue helicopter could reach", "A crewed helicopter and coordinated military rescue effort" ], "must_hold": [ "The vessel must reach and carry the people safely", "The human and unmanned parts of the rescue must coordinate", "Navigation, sensing, and communications must be adequate for the conditions; their exact use here is not documented" ], "echoes": [] }, "paired_incident": null, "headline": "Saronic Corsair sea drone picks up two Apache crew off Oman", "metric": "both were rescued within approximately two hours", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No development after the 9-10 June 2026 reports was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-09-8f45", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.saronic.com/careers; https://en.wikipedia.org/wiki/United_States_Navy", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-23", "change": "Reviewed the occurrence date, day precision, and benefit status against ABC News and DefenseScoop. The reported time of June 8, 2026 at 7:33 p.m. EDT is June 9 in Oman. The rescue is a reported benefit. The AI contribution and depth ratings were not reassessed.", "source": "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326; https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "source": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0209", "catalog_version": "1.0.0", "source_fingerprint": "590f2c708df195209bc2591a071f233b3686c1830447724b39f9f4b64ba670e0", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Saronic Technologies (Texas)", "deployer": "U.S. Navy, 5th Fleet Task Force 59 (U.S. Naval Forces Central Command)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "unassessed", "statement": "Hawkins identified an unmanned Corsair as the recovery vessel. DefenseScoop describes the platform’s autonomous capabilities, but its suggestion that passive sensing helped locate the crew is an inference. The official sources do not identify the navigation mode, human supervision, or sensor contribution during this rescue. A crewed helicopter completed the recovery." } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. 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The sources do not establish the counterfactual risk of death or whether another rescue asset could have reached them." }, { "group": "organizational", "depth": 2, "reason": "The military recovered two crew members using an unmanned vessel and a helicopter. The sources describe a successful operational recovery but do not isolate the AI’s contribution or quantify an organizational saving." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. 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The sources describe a successful operational recovery but do not isolate the AI’s contribution or quantify an organizational saving." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "CENTCOM reports that the two aviators reached safety within about two hours and were in stable condition. The rescue restored their safety and demonstrated an operational use of the vessel. The sources do not establish a risk of death or measure the AI’s separate contribution.", "editor_note": "" } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "value_present", "value": { "kind": "text", "text": "Reported benefit" }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "benefit_status" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Two aviators were recovered from the water and transferred to a helicopter within approximately two hours, according to CENTCOM. Their condition was stable. 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Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "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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"partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "both were rescued within approximately two hours", "editor_note": "" } }, 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9-10 June 2026 reports was found in the cited sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0209" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health", "taxonomy_note": "An at-sea rescue of aircrew is read as a health outcome because no rescue type exists." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "U.S. Central Command reported that two Army AH-64 Apache crew members were rescued at 7:33 p.m. Eastern time on June 8, 2026, or 3:33 a.m. on June 9 in Oman. That is the rescue time, not the time that the helicopter went down. CENTCOM said that recovery took approximately two hours and that both crew members were stable. In a direct interview with DefenseScoop, Capt. Tim Hawkins identified the vessel as a Saronic Corsair operated by the Navy’s Task Force 59. He said that it picked up the crew and carried them to another point on the water for a helicopter hoist. The official release said that the cause of the helicopter’s loss was under investigation. The sources do not identify which autonomous functions were used during the rescue.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. 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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. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "outcomes[].status": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_status": "reported" } }, "provenance": { "kind": "source_review", "basis": "ABC News and DefenseScoop report that the Corsair recovered the two crew members and transported them to a helicopter hoist. The classification concerns a reported realized rescue. This scoped review did not reassess the AI contribution, the depth ratings, or whether the rescue prevented otherwise unavoidable deaths.", "record_fields": [ "benefit_status" ], "source_refs": [ "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326", "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/" ], "reviewed_on": "2026-10-23" }, "review_state": "reviewed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": true, "purpose": "The Corsair is an autonomous maritime platform that Task Force 59 had been fielding since late March 2026. In this case, the military used it to carry stranded crew members to a helicopter recovery point." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Off the coast of Oman, near the Strait of Hormuz", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The Corsair is an autonomous maritime platform that Task Force 59 had been fielding since late March 2026. In this case, the military used it to carry stranded crew members to a helicopter recovery point." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "purpose" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Task Force 59 used a Corsair vessel to carry the stranded aviators to a helicopter recovery point.", "editor_note": "" } }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-06-09", "date_basis": "event", "date_precision": "day", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-09-8f45", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.saronic.com/careers; https://en.wikipedia.org/wiki/United_States_Navy", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-23", "change": "Reviewed the occurrence date, day precision, and benefit status against ABC News and DefenseScoop. The reported time of June 8, 2026 at 7:33 p.m. EDT is June 9 in Oman. The rescue is a reported benefit. The AI contribution and depth ratings were not reassessed.", "source": "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326; https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "source": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "June 9, 2026 is the Oman calendar date of the rescue. The official timestamp is 7:33 p.m. ET on June 8. The previous review incorrectly labeled it the crash time. Revision history is preserved separately from case chronology.", "record_fields": [ "date_observed", "revisions", "date_basis", "date_precision" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A Corsair already deployed with Task Force 59, which began fielding these vessels in late March 2026", "A vessel able to carry the crew to a location that a rescue helicopter could reach", "A crewed helicopter and coordinated military rescue effort" ], "must_hold": [ "The vessel must reach and carry the people safely", "The human and unmanned parts of the rescue must coordinate", "Navigation, sensing, and communications must be adequate for the conditions; their exact use here is not documented" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. 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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. 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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. 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Tim Hawkins.", "title": "Autonomous Corsair maritime drone rescues US military pilots after crash near Oman", "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "date": "2026-06-09", "written_by": "independent" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "origins": [ { "source_refs": [ "AIBD-0209-source-0001" ], "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "title": "U.S. Army Crew Safely Rescued After Helicopter Lost at Sea", "published_on": "2026-06-09", "relationship": "CENTCOM Public Affairs supplies the original official rescue chronology and crew condition." }, { "source_refs": [ "AIBD-0209-source-0002" ], "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "title": "Autonomous Corsair maritime drone rescues US military pilots after crash near Oman", "published_on": "2026-06-09", "relationship": "DefenseScoop contributes a direct interview with Capt. Tim Hawkins that identifies the Corsair and the transfer to a helicopter. It is original journalism, although the rescue details remain military testimony." } ], "excluded_republications": [ { "url": "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326", "citation": "Martinez L (2026). Unmanned drone boat rescues 2 US crew members from downed Apache helicopter near Strait of Hormuz. ABC News, 9 June 2026, 3:11 PM.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." }, { "url": "https://gcaptain.com/billion-dollar-usv-builder-saronic-scores-operational-milestone-in-oman-rescue/", "citation": "Schuler M (2026). Billion-Dollar USV Builder Saronic Scores Military Rescue Milestone Near Hormuz. gCaptain, 9 June 2026; quotes CENTCOM spokesman Capt. Tim Hawkins via Reuters and a Saronic statement.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." }, { "url": "https://www.csmonitor.com/USA/Military/2026/0610/navy-drones-apache-crew-rescue", "citation": "Mulrine Grobe A (2026). Troop rescue near Iran points to rising use of sea drones in combat. The Christian Science Monitor, 10 June 2026, 11:50 a.m. ET.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." }, { "url": "https://www.dailywire.com/news/the-robot-that-beat-iran-to-two-american-pilots", "citation": "Akiva K (2026). The Robot That Beat Iran To Two American Pilots. The Daily Wire, 9 June 2026; relays Capt. Tim Hawkins's remarks to the Wall Street Journal.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." }, { "url": "https://www.saronic.com/", "citation": "Saronic Technologies (2026). Corsair Rescues Downed Pilots in the Strait of Hormuz. News caption on saronic.com, linking to the Wall Street Journal; undated.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." } ], "lead_source_ref": "AIBD-0209-source-0001", "retained_source_order": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "ordering_basis": "The first source directly supports the reported benefit or is the closest accessible original account. Each additional source has a distinct evidentiary contribution described in its relationship. Documented inaccessible origins are not represented as having been read.", "claim_lineage": [ { "claim": "Both crew members were rescued at 7:33 p.m. ET on June 8 within approximately two hours, and were stable.", "source_ref": "AIBD-0209-source-0001", "source_chain": [ { "url": "https://www.centcom.mil/MEDIA/PUBLIC-RELEASES/Article/4511869/us-army-crew-safely-rescued-after-helicopter-lost-at-sea/", "role": "original command website, access denied" }, { "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "role": "same command’s full official distribution, read" } ], "locator": "Official release paragraphs 1–3", "method": {}, "limitations": [ "No crash time is supplied. The cause was under investigation." ] }, { "claim": "Task Force 59’s Saronic Corsair picked up the crew and carried them to a helicopter hoist.", "source_ref": "AIBD-0209-source-0002", "source_chain": [ { "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "locator": "Hawkins interview, opening and paragraphs 3–4", "role": "DefenseScoop contributes a direct interview with Capt. Tim Hawkins that identifies the Corsair and the transfer to a helicopter. It is original journalism, although the rescue details remain military testimony." } ], "locator": "Hawkins interview, opening and paragraphs 3–4", "method": {}, "limitations": [ "The source does not specify the autonomy mode or the exact contribution of AI." ] }, { "claim": "The local event date is June 9, 2026 in Oman.", "source_ref": "AIBD-0209-source-0001", "source_chain": [ { "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "locator": "Official rescue timestamp", "role": "CENTCOM Public Affairs supplies the original official rescue chronology and crew condition." } ], "locator": "Official rescue timestamp", "method": { "calculation": "7:33 p.m. EDT June 8 plus eight hours equals 3:33 a.m. Gulf Standard Time June 9" }, "limitations": [ "The conversion refers to the rescue, not the helicopter’s loss." ] } ], "limitations": [ "No mission log, operator account, sensor record, or independent evaluation establishes the AI’s particular role.", "Ars Technica links the original CENTCOM release and directly reproduces Hawkins’ platform identification, but that overlaps the retained sources.", "ABC and Christian Science Monitor pages were inaccessible in this review; no claim of fresh full-text review is made." ], "access_audit": [ { "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "access": "full official release read" }, { "url": "https://www.centcom.mil/MEDIA/PUBLIC-RELEASES/Article/4511869/us-army-crew-safely-rescued-after-helicopter-lost-at-sea/", "access": "403; official DVIDS copy read instead" }, { "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "access": "full article read" }, { "url": "https://arstechnica.com/gadgets/2026/06/us-military-claims-first-drone-boat-rescue-of-downed-helicopter-crew/", "access": "full article read and original-release link followed" } ], "related_material": [], "scope": "Complete review of retained-source lineage and access status. This does not mean that inaccessible upstream documents were inspected, that every required case field is complete, or that the original claims were independently verified." } }, "provenance": { "kind": "source_review", "basis": "Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down. Access limits are enumerated in the review.", "record_fields": [ "evidence", "account", "ai_role" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "CENTCOM describes the rescue, and the Navy identifies the vessel. Mission logs, sensor records, and an operator account of the AI’s role are unavailable.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "A Navy Corsair sea drone picked up two Apache crew off Oman and carried them to a helicopter hoist; CENTCOM reported that both were rescued within about two hours.", "account": "U.S. Central Command reported that two Army AH-64 Apache crew members were rescued at 7:33 p.m. Eastern time on June 8, 2026, or 3:33 a.m. on June 9 in Oman. That is the rescue time, not the time that the helicopter went down. CENTCOM said that recovery took approximately two hours and that both crew members were stable. In a direct interview with DefenseScoop, Capt. Tim Hawkins identified the vessel as a Saronic Corsair operated by the Navy’s Task Force 59. He said that it picked up the crew and carried them to another point on the water for a helicopter hoist. The official release said that the cause of the helicopter’s loss was under investigation. The sources do not identify which autonomous functions were used during the rescue." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "A Navy Corsair sea drone carried two Apache crew members off Oman to a helicopter recovery point. CENTCOM reported that both were rescued within about two hours.", "editor_note": "" } }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The deployment used a Saronic Corsair vessel." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Corsair; navigation-software version not reported" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "model_version" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The Corsair’s navigation software release is not disclosed.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Saronic Corsair unmanned (autonomous) surface vessel operated by U.S. 5th Fleet's Task Force 59" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Saronic Technologies (Texas)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Saronic Corsair sea drone picks up two Apache crew off Oman and carries them to a helicopter hoist", "headline": "Saronic Corsair sea drone picks up two Apache crew off Oman" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "title", "headline" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "590f2c708df195209bc2591a071f233b3686c1830447724b39f9f4b64ba670e0" ], "role": "SHA-256 of normalized current case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version of the updated case record.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "U.S. Central Command reported that two Army AH-64 Apache crew members were rescued at 7:33 p.m. Eastern time on June 8, 2026, or 3:33 a.m. on June 9 in Oman. That is the rescue time, not the time that the helicopter went down. CENTCOM said that recovery took approximately two hours and that both crew members were stable. In a direct interview with DefenseScoop, Capt. Tim Hawkins identified the vessel as a Saronic Corsair operated by the Navy’s Task Force 59. He said that it picked up the crew and carried them to another point on the water for a helicopter hoist. The official release said that the cause of the helicopter’s loss was under investigation. The sources do not identify which autonomous functions were used during the rescue.", "replication": { "produced_by": [ "A Corsair already deployed with Task Force 59, which began fielding these vessels in late March 2026", "A vessel able to carry the crew to a location that a rescue helicopter could reach", "A crewed helicopter and coordinated military rescue effort" ], "must_hold": [ "The vessel must reach and carry the people safely", "The human and unmanned parts of the rescue must coordinate", "Navigation, sensing, and communications must be adequate for the conditions; their exact use here is not documented" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "A Corsair vessel reached the crew and carried them to a point where a helicopter could recover them. Task Force 59 had begun fielding these vessels in late March 2026.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 34, "reviewed": 21, "record_mapped": 13, "not_assessed": 57, "complete": false, "by_state": { "value_present": 34, "not_reported": 0, "unresolved": 0, "not_assessed": 57, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0137", "case_url": "records/AIBD-0137.html", "reason": "The developer is headquartered in United States; the case names Norway.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Norway" }, { "label": "Case location", "value": "Norway" } ], "filters": { "origin": "United States", "destination": [ "Norway" ] }, "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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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. 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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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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. 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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[].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-0134", "case_url": "records/AIBD-0134.html", "reason": "The developer is headquartered in United States; the case names Spain.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Spain" }, { "label": "Case location", "value": "Majadahonda, Spain" } ], "filters": { "origin": "United States", "destination": [ "Spain" ] }, "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-0370", "case_url": "records/AIBD-0370.html", "reason": "The developer is headquartered in Japan; the case names Taiwan.", "details": [ { "label": "Developer headquarters", "value": "Japan" }, { "label": "Case jurisdictions", "value": "Taiwan" }, { "label": "Case location", "value": "Chiayi, Taiwan" } ], "filters": { "origin": "Japan", "destination": [ "Taiwan" ] }, "record": { "id": "AIBD-0370", "title": "Fujifilm AI endoscopy at St. Martin De Porres Hospital, Chiayi, marks a 1 to 1.5 cm adenoma in a man in his 50s; pathology shows stage I colorectal cancer, removed endoscopically", "summary": "After a positive fecal occult blood test, Fujifilm AI endoscopy at St. Martin De Porres Hospital, Chiayi, marked a 1 to 1.5 cm adenoma; pathology: stage I colorectal cancer, removed endoscopically.", "date_observed": "2024-04-19", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Three Taiwanese outlets reported on 19 April 2024 on a man in his 50s. He attended free colorectal cancer screening at St. Martin De Porres Hospital in Chiayi City. He tested positive for fecal occult blood and was scheduled for an AI-assisted endoscopy (Liberty Times Net health). Gastroenterologist Zheng Xu-sheng said the hospital introduced a Fujifilm 7000-series endoscope in 2023. It installed what it calls the nation's first Fujifilm AI system (Liberty Times Net; Taiwan Good News). During the examination the AI system marked a suspicious polyp with a colored frame and sounded an alert. It was diagnosed as a 1 to 1.5 cm adenoma that biopsy confirmed as stage I colorectal cancer. It was removed immediately through the endoscope (Taiwan Good News; Guan Chuan Media). Zheng said polyps over 1 cm risk malignant change and advised free screening every two years after 50 (Guan Chuan Media). The procedure date is not given.", "category": "benefit created", "unexpected": false, "purpose": "The Fujifilm AI system is built to mark suspicious polyps on the endoscopic image with a colored frame and sound. The physician then inspects and removes them. Flagging this adenoma was that function.", "system": "Fujifilm AI endoscopy system on a Fujifilm 7000-series gastrointestinal endoscope (product name not given in the sources)", "model_version": null, "developer": "Fujifilm", "deployer": "St. Martin De Porres Hospital (天主教聖馬爾定醫院), Chiayi", "location": "Chiayi, Taiwan", "domain": "Healthcare - colorectal cancer screening", "segment": "AI-assisted colonoscopy in hospital screening", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2024-04-19-de89" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "A stage I colorectal cancer inside a 1 to 1.5 cm adenoma was removed through the endoscope without surgery, per all three reports. A survival benefit is plausible for an early-stage cancer but is not demonstrated in the sources." } ], "ai_role": { "status": "assessed", "statement": "Taiwan Good News and Guan Chuan Media state that the AI system marked the suspicious polyp with a colored frame and sound during the examination. The physician then diagnosed and removed it. No source says the physician would have missed a 1 to 1.5 cm polyp in a patient already referred for a positive stool test. So the AI's share of this detection cannot be separated from the colonoscopy itself. All three reports relay the hospital's account. No clinical study of the system at this hospital was cited." }, "evidence": [ { "kind": "news report", "citation": "丁偉杰 (2024). AI輔助醫師判讀瘜肉 大腸癌無所遁形. 自由健康網 (Liberty Times Net), 19 April 2024.", "title": "AI輔助醫師判讀瘜肉 大腸癌無所遁形", "url": "https://health.ltn.com.tw/article/breakingnews/4646402", "date": "2024-04-19", "written_by": "independent", "quote": "發現可疑瘜肉，經切片檢查證實為第一期大腸癌" }, { "kind": "news report", "citation": "劉芳妃 (2024). 聖馬爾定醫院運用AI內視鏡檢查 大腸癌無所遁形. 台灣好報 via Yahoo奇摩新聞, 19 April 2024.", "title": "聖馬爾定醫院運用AI內視鏡檢查 大腸癌無所遁形", "url": "https://tw.news.yahoo.com/%E8%81%96%E9%A6%AC%E7%88%BE%E5%AE%9A%E9%86%AB%E9%99%A2%E9%81%8B%E7%94%A8ai%E5%85%A7%E8%A6%96%E9%8F%A1%E6%AA%A2%E6%9F%A5-%E5%A4%A7%E8%85%B8%E7%99%8C%E7%84%A1%E6%89%80%E9%81%81%E5%BD%A2-052431696.html", "date": "2024-04-19", "written_by": "independent", "quote": "AI系統以色框標註並發出聲響，提示醫師可疑瘜肉" }, { "kind": "news report", "citation": "吳東林 (2024). AI內視鏡檢查 大腸癌無所遁形. 觀傳媒 via Yahoo奇摩新聞, 19 April 2024.", "title": "AI內視鏡檢查 大腸癌無所遁形", "url": "https://tw.news.yahoo.com/ai%E5%85%A7%E8%A6%96%E9%8F%A1%E6%AA%A2%E6%9F%A5-%E5%A4%A7%E8%85%B8%E7%99%8C%E7%84%A1%E6%89%80%E9%81%81%E5%BD%A2-113000020.html", "date": "2024-04-19", "written_by": "independent", "quote": "診斷為1~1.5公分的腺瘤，經切片檢查證實為第一期大腸癌" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2024). AI-Assisted Endoscopy Enables Early Detection and Removal of Colorectal Cancer. OECD.AI, 19 April 2024.", "title": "AI-Assisted Endoscopy Enables Early Detection and Removal of Colorectal Cancer", "url": "https://oecd.ai/en/incidents/2024-04-19-de89", "date": "2024-04-19", "written_by": "independent", "quote": "The AI system enabled early identification and removal of a cancerous polyp in a patient" } ], "replication": { "produced_by": [ "A positive fecal occult blood test in a free screening program, followed by colonoscopy", "A Fujifilm AI system that marks suspicious polyps with a colored frame and a sound during the examination", "Endoscopic removal and pathology at the same hospital" ], "must_hold": [ "Patients attend screening and follow a positive stool test with colonoscopy", "The endoscopist inspects and acts on the AI's marks", "Bowel preparation adequate for the camera to see the mucosa" ], "echoes": [] }, "paired_incident": null, "headline": "AI colonoscopy flags a polyp; stage I cancer removed in Chiayi", "metric": "a 1 to 1.5 cm adenoma that biopsy confirmed as stage I colorectal cancer", "comparator": null, "later": null, "value": null, "developer_country": "Japan", "deployer_country": "Taiwan", "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/2024-04-19-de89", "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": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Fujifilm; https://www.stm.org.tw/stm_en/about_02.asp", "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-0370", "catalog_version": "1.0.0", "source_fingerprint": "68e1b533424bebb2ff296a14d808d315223bbe0898c71651f0655159ed5886ed", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0370-source-0001", "AIBD-0370-source-0002", "AIBD-0370-source-0003", "AIBD-0370-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": "Fujifilm", "deployer": "St. Martin De Porres Hospital (天主教聖馬爾定醫院), Chiayi" } }, "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": "Taiwan Good News and Guan Chuan Media state that the AI system marked the suspicious polyp with a colored frame and sound during the examination. The physician then diagnosed and removed it. No source says the physician would have missed a 1 to 1.5 cm polyp in a patient already referred for a positive stool test. So the AI's share of this detection cannot be separated from the colonoscopy itself. All three reports relay the hospital's account. No clinical study of the system at this hospital was cited." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "Taiwan Good News and Guan Chuan Media state that the AI system marked the suspicious polyp with a colored frame and sound during the examination. The physician then diagnosed and removed it. No source says the physician would have missed a 1 to 1.5 cm polyp in a patient already referred for a positive stool test. So the AI's share of this detection cannot be separated from the colonoscopy itself. All three reports relay the hospital's account. No clinical study of the system at this hospital was 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": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "A stage I colorectal cancer inside a 1 to 1.5 cm adenoma was removed through the endoscope without surgery, per all three reports. A survival benefit is plausible for an early-stage cancer but is not demonstrated 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: 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": 3, "reason": "A stage I colorectal cancer inside a 1 to 1.5 cm adenoma was removed through the endoscope without surgery, per all three reports. A survival benefit is plausible for an early-stage cancer but is not demonstrated in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "A stage I colorectal cancer inside a 1 to 1.5 cm adenoma was removed through the endoscope without surgery, per all three reports. A survival benefit is plausible for an early-stage cancer but is not demonstrated in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": 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AI內視鏡檢查 大腸癌無所遁形. 觀傳媒 via Yahoo奇摩新聞, 19 April 2024.", "title": "AI內視鏡檢查 大腸癌無所遁形", "url": "https://tw.news.yahoo.com/ai%E5%85%A7%E8%A6%96%E9%8F%A1%E6%AA%A2%E6%9F%A5-%E5%A4%A7%E8%85%B8%E7%99%8C%E7%84%A1%E6%89%80%E9%81%81%E5%BD%A2-113000020.html", "date": "2024-04-19", "written_by": "independent", "quote": "診斷為1~1.5公分的腺瘤，經切片檢查證實為第一期大腸癌" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2024). AI-Assisted Endoscopy Enables Early Detection and Removal of Colorectal Cancer. 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AI內視鏡檢查 大腸癌無所遁形. 觀傳媒 via Yahoo奇摩新聞, 19 April 2024.", "title": "AI內視鏡檢查 大腸癌無所遁形", "url": "https://tw.news.yahoo.com/ai%E5%85%A7%E8%A6%96%E9%8F%A1%E6%AA%A2%E6%9F%A5-%E5%A4%A7%E8%85%B8%E7%99%8C%E7%84%A1%E6%89%80%E9%81%81%E5%BD%A2-113000020.html", "date": "2024-04-19", "written_by": "independent", "quote": "診斷為1~1.5公分的腺瘤，經切片檢查證實為第一期大腸癌" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2024). AI-Assisted Endoscopy Enables Early Detection and Removal of Colorectal Cancer. 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The procedure 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": "Fujifilm AI endoscopy system on a Fujifilm 7000-series gastrointestinal endoscope (product name not given 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": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Fujifilm" } }, "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": "Fujifilm AI endoscopy at St. Martin De Porres Hospital, Chiayi, marks a 1 to 1.5 cm adenoma in a man in his 50s; pathology shows stage I colorectal cancer, removed endoscopically", "headline": "AI colonoscopy flags a polyp; stage I cancer removed in Chiayi" } }, "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": [ "68e1b533424bebb2ff296a14d808d315223bbe0898c71651f0655159ed5886ed" ], "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": "Three Taiwanese outlets reported on 19 April 2024 on a man in his 50s. He attended free colorectal cancer screening at St. Martin De Porres Hospital in Chiayi City. He tested positive for fecal occult blood and was scheduled for an AI-assisted endoscopy (Liberty Times Net health). Gastroenterologist Zheng Xu-sheng said the hospital introduced a Fujifilm 7000-series endoscope in 2023. It installed what it calls the nation's first Fujifilm AI system (Liberty Times Net; Taiwan Good News). During the examination the AI system marked a suspicious polyp with a colored frame and sounded an alert. It was diagnosed as a 1 to 1.5 cm adenoma that biopsy confirmed as stage I colorectal cancer. It was removed immediately through the endoscope (Taiwan Good News; Guan Chuan Media). Zheng said polyps over 1 cm risk malignant change and advised free screening every two years after 50 (Guan Chuan Media). The procedure date is not given.", "replication": { "produced_by": [ "A positive fecal occult blood test in a free screening program, followed by colonoscopy", "A Fujifilm AI system that marks suspicious polyps with a colored frame and a sound during the examination", "Endoscopic removal and pathology at the same hospital" ], "must_hold": [ "Patients attend screening and follow a positive stool test with colonoscopy", "The endoscopist inspects and acts on the AI's marks", "Bowel preparation adequate for the camera to see the mucosa" ], "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": "The developer is headquartered in Canada; the case names Vietnam.", "details": [ { "label": "Developer headquarters", "value": "Canada" }, { "label": "Case jurisdictions", "value": "Vietnam" }, { "label": "Case location", "value": "Ho Chi Minh City, Vietnam" } ], "filters": { "origin": "Canada", "destination": [ "Vietnam" ] }, "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": 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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. 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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." } }, "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. 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(Toronto)" } }, "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": "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", "headline": "Modus V Synaptive guides awake removal of a 4 cm brain clot" } }, "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": [ "e603a114bf59fa1fabdce8892a052cad025e112d8e70282017cf0e55fdaae32f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 10 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.", "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-0019", "case_url": "records/AIBD-0019.html", "reason": "The developer is headquartered in United States; the case names Peru.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Peru" }, { "label": "Case location", "value": "Nazca, Peru" } ], "filters": { "origin": "United States", "destination": [ "Peru" ] }, "record": { "id": "AIBD-0019", "title": "AI-guided survey confirms 303 new figurative Nazca geoglyphs in Peru", "summary": "An IBM–Yamagata deep-learning survey guided fieldwork that confirmed 303 new Nazca geoglyphs, published in PNAS (Sep 2024).", "date_observed": "2022-09-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Between September 2022 and February 2023 (sources give only months, so the first day of September is used) archaeologists surveyed the Nazca Pampa in Peru. Masato Sakai of Yamagata University's Institute of Nasca led them. They used candidate locations proposed by a deep-learning model developed with IBM Research. It screened aerial photographs at 10-centimeter resolution over more than 600 square kilometers. Field verification confirmed 303 previously unknown figurative relief-type geoglyphs, almost doubling the known total. 178 were individually suggested by the model and 125 were found during the AI-guided survey without an individual suggestion. The team screened about 36 AI suggestions per promising candidate and reports a sixteen-fold increase in discovery rate. The results appeared in PNAS on 23 September 2024.", "category": "benefit created", "unexpected": false, "purpose": "Built to flag candidate geoglyph locations in aerial imagery so that archaeologists could prioritize ground survey; the discoveries were that purpose.", "system": "Geoglyph classifier developed by IBM and Yamagata University: an ImageNet-pretrained ResNet50 with a two-layer classifier, applied to aerial imagery through IBM PAIRS", "model_version": null, "developer": "IBM Research (IBM T.J. Watson Research Center) with Yamagata University and DLR", "deployer": "Yamagata University (Institute of Nasca field team)", "location": "Nazca, Peru", "domain": "Archaeology", "segment": "Academic research with industry partner", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "Yamagata University's Institute of Nasca reports a sixteen-fold increase in discovery rate, achieving in six months what DLR says previously took decades." }, { "group": "societal", "depth": 3, "reason": "The known corpus of figurative geoglyphs at a UNESCO World Heritage site nearly doubled, with 303 confirmed sites added to Peru's archaeological record." } ], "ai_role": { "status": "assessed", "statement": "The model proposed candidate locations that directed fieldwork. 178 of the 303 confirmed geoglyphs were individually suggested by the AI. Of the remaining 125, 66 were found as part of an AI-discovered group. The other 59 were found without any help from AI or in screening outside the AI-suggested boxes. Confirmation was by ground survey, not by the model." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Sakai, M., Sakurai, A., Lu, S., Olano, J., Albrecht, C. M., Hamann, H. F. and Freitag, M. (2024). AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs and sheds light on their purpose. PNAS 121(40), e2407652121 (PMC11459208).", "title": "AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs and sheds light on their purpose", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11459208/", "date": "2024-09-23", "written_by": "makers", "quote": "303 new figurative relief-type geoglyphs, almost doubling the amount of known relief-type geoglyphs." }, { "kind": "press release", "citation": "Yamagata University (2024). AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs and sheds light on their purpose. University news.", "title": "AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs and sheds light on their purpose", "url": "https://www.yamagata-u.ac.jp/en/information/info/20240924/", "date": "2024-09-24", "written_by": "makers", "quote": "This represents a 16 fold increase in the rate of discovery by using IBM's AI." }, { "kind": "press release", "citation": "German Aerospace Center DLR, Earth Observation Center (2024). Accelerated Discovery of Nasca Geoglyphs Guided by AI.", "title": "Accelerated Discovery of Nasca Geoglyphs Guided by AI", "url": "https://www.dlr.de/en/eoc/latest/news/2024/accelerated-discovery-nasca-geoglyphs-by-ai", "date": "2024-10-09", "written_by": "makers", "quote": "The AI system revealed the distribution of candidate geoglyphs from aerial photographs, drawing the archaeologists' attention to areas where to take a closer look at the ground on site." }, { "kind": "news report", "citation": "Archaeology Magazine, Archaeological Institute of America (2024). More Than 300 Geoglyphs Discovered in Peru.", "title": "More Than 300 Geoglyphs Discovered in Peru", "url": "https://archaeology.org/news/2024/09/27/more-than-300-geoglyphs-discovered-in-peru/", "date": "2024-09-27", "written_by": "independent", "quote": "The use of AI in research has allowed us to map the distribution of geoglyphs more quickly and accurately." }, { "kind": "news report", "citation": "del Río ML. More than 200 geoglyphs discovered in Nazca Desert with help of AI. The Art Newspaper, 14 August 2025.", "title": "More than 200 geoglyphs discovered in Nazca Desert with help of AI", "url": "https://www.theartnewspaper.com/2025/08/14/248-nazca-lines-discovered-artificial-intelligence-peru", "date": "2025-08-14", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "High-resolution aerial imagery of the whole survey area", "A detector trained with limited labeled examples of relief-type geoglyphs", "Archaeologist screening of candidates followed by ground verification" ], "must_hold": [ "Targets must be visible at the imagery resolution (about 10 cm)", "Each candidate must be confirmed on the ground before being counted" ], "echoes": [] }, "paired_incident": null, "headline": "IBM–Yamagata AI survey confirms 303 new Nazca geoglyphs in Peru", "metric": "303 previously unknown figurative relief-type geoglyphs, almost doubling the known total", "comparator": { "against": "the discovery rate with remotely sensed high-resolution imagery but without AI (2004 to 2020)", "before": "18.7/y from 2004 to 2020", "after": "303 new figurative geoglyphs during the 2022/23 season of field work (a 16-fold acceleration)", "basis": "measured", "quote": "It has accelerated due to the availability of remotely sensed high-resolution imagery to 18.7/y from 2004 to 2020. Our current work represents another 16-fold acceleration (303 new figurative geoglyphs during the 2022/23 season of field work) using big geospatial data technologies and data mining with the aid of AI.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11459208/" }, "later": { "status": "scaled", "date": "2025-08-14", "note": "The same Yamagata-IBM team announced 248 further AI-assisted geoglyph finds at Expo 2025 Osaka, bringing the total to 893, of which 781 came from AI and aerial-image analysis.", "quote": "An international team led by Japan's Yamagata University and IBM, in collaboration with Peruvian researchers, has announced the discovery of 248 new geoglyphs among the famous Nazca Lines.", "url": "https://www.theartnewspaper.com/2025/08/14/248-nazca-lines-discovered-artificial-intelligence-peru" }, "value": null, "developer_country": "United States", "deployer_country": "Japan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Rewrote the statement of the system's part to follow the PNAS breakdown, because the earlier wording credited all 125 non-individually-suggested geoglyphs to AI-prioritized areas when the paper attributes 66 to an AI-discovered group and 59 to finds made without AI help or outside the AI-suggested boxes.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11459208/", "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/PMC11459208/; https://www.theartnewspaper.com/2025/08/14/248-nazca-lines-discovered-artificial-intelligence-peru", "editor": "AIBD" }, { "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/IBM_Research; https://www.yamagata-u.ac.jp/en/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Yamagata University.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Identified the AI model, software, configuration, and human controls from the original paper, supplement, or author documentation.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11459208/", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, 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-0019", "catalog_version": "1.0.0", "source_fingerprint": "199358dd8dd5cf6a0df989eb1748dbbb10aca72413bdcd649ee588881e64495f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0019-source-0001", "AIBD-0019-source-0002", "AIBD-0019-source-0003", "AIBD-0019-source-0004", "AIBD-0019-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": "IBM Research (IBM T.J. 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(Nature, published 16 February 2022; received 14 July 2021) describe a single deep neural network controller. It learns in simulation to command all 19 magnetic control coils of the Tokamak à Configuration Variable (TCV). TCV is a research tokamak at the Swiss Plasma Center, EPFL. Deployed on the real device, the controller produced and controlled elongated conventional shapes and advanced configurations such as negative triangularity and “snowflake” plasmas. It tracked location, current and shape. In one demonstration it established a snowflake with X-points 34 cm apart and narrowed the separation to 6.6 cm, combined RMSE 3.7 cm. It also sustained “droplets”, two separate plasmas maintained simultaneously in the vessel. MIT Technology Review (16 February 2022) reported that the AI controlled the real reactor without additional fine-tuning. 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Towards practical reinforcement learning for tokamak magnetic control. arXiv:2307.11546, 21 July 2023 (v2 5 October 2023).", "title": "Towards practical reinforcement learning for tokamak magnetic control", "url": "https://arxiv.org/abs/2307.11546", "date": "2023-07-21", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "A high-fidelity simulator (FGE) and equilibrium reconstruction (LIUQE) of the target tokamak available for training", "A flexible research tokamak with 19 independently actuated coils", "A real-time control system able to run a neural network policy at the required rate" ], "must_hold": [ "A simulator accurate enough for zero-shot transfer to the device", "Operator-specified targets within the device's physical and operational constraints", "Access to machine time on a research tokamak" ], "echoes": [ { "date": "2024-02-21", "setting": "DIII-D tokamak, San Diego; deep RL controller for tearing-instability avoidance (different objective)", "model_version": null, "outcome": "recurred", "source": "https://www.nature.com/articles/s41586-024-07024-9" } ] }, "paired_incident": null, "headline": "DeepMind RL controller shapes TCV plasmas and sustains droplets", "metric": "narrowed the separation to 6.6 cm with a combined RMSE of 3.7 cm", "comparator": null, "later": { "status": "persisted", "date": "2023-07-21", "note": "DeepMind and the Swiss Plasma Center reported upgraded RL controllers with up to 65% better shape accuracy in simulation and new validating experiments on TCV.", "quote": "We present simulation results that show up to 65\\% improvement in shape accuracy, achieve substantial reduction in the long-term bias of the plasma current, and additionally reduce the training time required to learn new tasks by a factor of 3 or more.", "url": "https://arxiv.org/abs/2307.11546" }, "value": null, "developer_country": "United Kingdom", "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": "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": "Added what happened since from the cited sources.", "source": "https://arxiv.org/abs/2307.11546", "editor": "AIBD" }, { "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.epfl.ch/research/domains/swiss-plasma-center/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, the makers' peer-reviewed study and the makers' preprint. 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(Nature, published 16 February 2022; received 14 July 2021) describe a single deep neural network controller. It learns in simulation to command all 19 magnetic control coils of the Tokamak à Configuration Variable (TCV). TCV is a research tokamak at the Swiss Plasma Center, EPFL. Deployed on the real device, the controller produced and controlled elongated conventional shapes and advanced configurations such as negative triangularity and “snowflake” plasmas. It tracked location, current and shape. In one demonstration it established a snowflake with X-points 34 cm apart and narrowed the separation to 6.6 cm, combined RMSE 3.7 cm. It also sustained “droplets”, two separate plasmas maintained simultaneously in the vessel. MIT Technology Review (16 February 2022) reported that the AI controlled the real reactor without additional fine-tuning. It quoted independent expert Jonathan Citrin of the Dutch Institute for Fundamental Energy Research and Swiss Plasma Center director Ambrogio Fasoli. The experiment dates are not given.", "replication": { "produced_by": [ "A high-fidelity simulator (FGE) and equilibrium reconstruction (LIUQE) of the target tokamak available for training", "A flexible research tokamak with 19 independently actuated coils", "A real-time control system able to run a neural network policy at the required rate" ], "must_hold": [ "A simulator accurate enough for zero-shot transfer to the device", "Operator-specified targets within the device's physical and operational constraints", "Access to machine time on a research tokamak" ], "echoes": [ { "date": "2024-02-21", "setting": "DIII-D tokamak, San Diego; deep RL controller for tearing-instability avoidance (different objective)", "outcome": "recurred", "source": "https://www.nature.com/articles/s41586-024-07024-9" } ] } } }, "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-0110", "case_url": "records/AIBD-0110.html", "reason": "The developer is headquartered in United States; the case names Singapore.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Singapore" }, { "label": "Case location", "value": "Ang Mo Kio, Singapore" } ], "filters": { "origin": "United States", "destination": [ "Singapore" ] }, "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. 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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-0108", "case_url": "records/AIBD-0108.html", "reason": "The developer is headquartered in United States; the case names United Kingdom.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "United Kingdom" }, { "label": "Case location", "value": "Sturminster Marshall, United Kingdom" } ], "filters": { "origin": "United States", "destination": [ "United Kingdom" ] }, "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. 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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/", "written_by": "independent", "quote": "an oak tree came crashing down onto their bonnet rather than their roof, thanks to the smart safety feature" } ] } }, "provenance": { "kind": "legacy_record", "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-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" ], "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": "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/", "written_by": "independent", "quote": "an oak tree came crashing down onto their bonnet rather than their roof, thanks to the smart safety feature" } ] } }, "provenance": { "kind": "legacy_record", "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": "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.", "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." } }, "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. 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": "Both vehicles were Tesla Model X cars, including one loan car. The installed Autopilot software was not identified." }, "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; both cars were Tesla Model X, one a Tesla loan car, and no Autopilot hardware or software version is given" } }, "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 driver-assistance suite with Automatic Emergency Braking, on two Model X cars" } }, "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": "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", "headline": "Two Tesla Model X cars stop as an oak falls; eight walk away" } }, "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": [ "711695c7ca3fbdc1da9ec1e4e39bb39f6e8dcfa022a9f5500d8c002d80cf18d3" ], "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 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.", "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": "The developer is headquartered in Netherlands; the case names United Kingdom.", "details": [ { "label": "Developer headquarters", "value": "Netherlands" }, { "label": "Case jurisdictions", "value": "United Kingdom" }, { "label": "Case location", "value": "London, United Kingdom" } ], "filters": { "origin": "Netherlands", "destination": [ "United Kingdom" ] }, "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. 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"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. 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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. 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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. 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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-0029", "case_url": "records/AIBD-0029.html", "reason": "The developer is headquartered in United States; the case names Cambodia.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Cambodia" }, { "label": "Case location", "value": "Srepok Wildlife Sanctuary (Mondulkiri), Cambodia" } ], "filters": { "origin": "United States", "destination": [ "Cambodia" ] }, "record": { "id": "AIBD-0029", "title": "PAWS patrol-planning field test removes over 1,000 snares in Srepok Wildlife Sanctuary, Cambodia", "summary": "PAWS-directed ranger patrols in Srepok Wildlife Sanctuary removed over 1,000 snares in December 2018, versus 200–300 in a typical month.", "date_observed": "2018-12-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "PAWS (Protection Assistant for Wildlife Security) is a machine-learning pipeline that predicts poaching risk and plans patrols. It was field-tested with WWF Cambodia and Ministry of Environment rangers in Srepok Wildlife Sanctuary, Cambodia, from December 2018. Only the month is given, so the first day is used. Rangers received center coordinates of fifteen 3 x 3 km blocks chosen from PAWS risk predictions, without being told each block's risk class. In the first month, 72 rangers in teams of eight detected poaching in 32 cells and removed over one thousand snares. They had reported removing two or three hundred in an average month. Low-risk blocks yielded no poaching. A second trial ran February to March 2019.", "category": "benefit created", "unexpected": false, "purpose": "PAWS was built to predict where poaching will occur and to prescribe patrol routes that maximize detection and removal of snares. The snare removals are exactly this intended benefit.", "system": "PAWS (Protection Assistant for Wildlife Security), enhanced iWare-E predictive model with Gaussian-process uncertainty and game-theoretic patrol planning", "model_version": "iWare-E with Gaussian-process weak learners trained on January 2015 to April 2018 dry-season data (Xu et al., ICDE 2020)", "developer": "Harvard University and University of Southern California (Milind Tambe and colleagues), with WWF, WCS and Uganda Wildlife Authority co-authors", "deployer": "WWF Cambodia with Cambodia Ministry of Environment rangers, Srepok Wildlife Sanctuary", "location": "Srepok Wildlife Sanctuary (Mondulkiri), Cambodia", "domain": "Conservation / anti-poaching law enforcement", "segment": "Protected-area ranger patrols", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The ranger force's monthly snare removals rose from a reported two or three hundred to over one thousand. A material gain in patrol effectiveness for the operating team. A chi-squared test confirmed predictions discriminated risk (p = 0.023)." }, { "group": "societal", "depth": 3, "reason": "Over one thousand snares were removed from a sanctuary identified as a tiger-reintroduction site. The paper states each snare “represents the life of one animal potentially saved”. A material landscape-scale conservation benefit rather than a documented population-level change." } ], "ai_role": { "status": "assessed", "statement": "The AI's output (risk-ranked 3 x 3 km blocks) determined where patrols went. Rangers were blinded to risk class. High-risk blocks yielded detections while low-risk blocks yielded none. The paper itself cautions that the first QENP test's increased findings “may be partially explained by factors beyond the predictive model”. The 1,000-versus-200–300 comparison relies on rangers' reported averages, not a controlled baseline. The peer-reviewed paper is written by the system's academic developers together with staff of the operating organizations (WWF, WCS, Uganda Wildlife Authority). There is no commercial vendor." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Xu L, Gholami S, Mc Carthy S, Dilkina B, Plumptre A, Tambe M, Singh R, Nsubuga M, Mabonga J, Driciru M, Wanyama F, Rwetsiba A, Okello T, Enyel E (2020). Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations. IEEE ICDE 2020.", "title": "Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations", "url": "https://arxiv.org/pdf/1903.06669", "date": "2019-11-06", "written_by": "makers", "quote": "During the first month of field tests in SWS, park rangers detected poaching in 32 cells and removed over one thousand snares" }, { "kind": "peer-reviewed study", "citation": "Xu L et al. (2020). Stay Ahead of Poachers (abstract page; states the work is a short paper published in ICDE 2020). arXiv.", "title": "Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (abstract)", "url": "https://arxiv.org/abs/1903.06669", "date": "2019-11-06", "written_by": "makers", "quote": "confirm that the predictive power of PAWS extends promisingly to multiple parks" }, { "kind": "news report", "citation": "Geib C (2020). Where to patrol next: 'Netflix' of ranger AI serves up poaching predictions. Mongabay.", "title": "Where to patrol next: 'Netflix' of ranger AI serves up poaching predictions", "url": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/", "date": "2020-11-05", "written_by": "independent", "quote": "rangers in the park have reported finding between 5 and 20 times more snares since they started using the PAWS system" } ], "replication": { "produced_by": [ "Historical SMART patrol data (2015–2018) sufficient to train a risk model despite 3.6% positive labels", "Rangers willing to patrol the suggested blocks and record findings in SMART", "Dry-season deployment when rivers are passable and poaching concentrates in the north of the sanctuary" ], "must_hold": [ "Patrol capacity exists to act on predictions (SWS ranger numbers had more than doubled since 2016)", "Poacher behavior remains spatially predictable from past detections", "Findings are measured against a comparable baseline. The paper notes increases may partly reflect factors beyond the model" ], "echoes": [ { "date": "2019-02-01", "setting": "Second SWS trial, February-March 2019: high-risk blocks yielded 7 detections in 22 cells, low-risk blocks 0 in 34 cells (Table III of the same paper)", "model_version": "iWare-E predictions for January-February 2019", "outcome": "recurred", "source": "https://arxiv.org/pdf/1903.06669" }, { "date": "2020-11-05", "setting": "Ongoing use in Srepok Wildlife Sanctuary; rangers reported finding 5 to 20 times more snares since adopting PAWS, and PAWS was incorporated into SMART", "model_version": null, "outcome": "recurred", "source": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "219", "title": "Poachers Evaded AI Cameras and Killed Four Rhinos", "url": "https://incidentdatabase.ai/cite/219", "severity": null, "group": "societal", "basis": "Same task and domain: AI-directed anti-poaching in a protected area; PAWS patrols removed 1,000 snares, while Ezemvelo KZN Wildlife's AI cameras failed to detect poachers and four rhinos were killed." }, "headline": "PAWS-directed patrols remove over 1,000 snares in Cambodia", "metric": "removed over 1,000 snares in December 2018, versus 200–300 in a typical month", "comparator": { "against": "the rangers' usual monthly snare removal without PAWS", "before": "two or three hundred snares in an average month", "after": "over one thousand snares in the first month of field tests", "basis": "measured", "quote": "During the first month of field tests in SWS, park rangers detected poaching in 32 cells and removed over one thousand snares—each of which represents the life of one animal potentially saved. The rangers informed us that, by comparison, they typically remove two or three hundred snares in an average month.", "url": "https://arxiv.org/pdf/1903.06669" }, "later": { "status": "scaled", "date": "2020-11-05", "note": "By November 2020 PAWS had been incorporated into the SMART conservation software used in parks worldwide, and Srepok rangers reported finding 5 to 20 times more snares since adopting it.", "quote": "PAWS has been incorporated into the latest update to SMART, which is already used widely by rangers and other park employees to gather data and track observations of illegal activity in parks around the world.", "url": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/" }, "value": null, "developer_country": "United States", "deployer_country": "Cambodia", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 219 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/219", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://arxiv.org/pdf/1903.06669; https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.harvard.edu/about/; https://www.wikidata.org/wiki/Q117405694", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Harvard University.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and two peer-reviewed studies from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0029", "catalog_version": "1.0.0", "source_fingerprint": "faaa16a57c0d26842d7dd6d15debcd4322f41ff602ffdd7fce38bf8542a42d9c", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0029-source-0001", "AIBD-0029-source-0002", "AIBD-0029-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": "Harvard University and University of Southern California (Milind Tambe and colleagues), with WWF, WCS and Uganda Wildlife Authority co-authors", "deployer": "WWF Cambodia with Cambodia Ministry of Environment rangers, Srepok Wildlife Sanctuary" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI's output (risk-ranked 3 x 3 km blocks) determined where patrols went. Rangers were blinded to risk class. High-risk blocks yielded detections while low-risk blocks yielded none. The paper itself cautions that the first QENP test's increased findings “may be partially explained by factors beyond the predictive model”. The 1,000-versus-200–300 comparison relies on rangers' reported averages, not a controlled baseline. The peer-reviewed paper is written by the system's academic developers together with staff of the operating organizations (WWF, WCS, Uganda Wildlife Authority). There is no commercial vendor." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The AI's output (risk-ranked 3 x 3 km blocks) determined where patrols went. Rangers were blinded to risk class. High-risk blocks yielded detections while low-risk blocks yielded none. The paper itself cautions that the first QENP test's increased findings “may be partially explained by factors beyond the predictive model”. The 1,000-versus-200–300 comparison relies on rangers' reported averages, not a controlled baseline. The peer-reviewed paper is written by the system's academic developers together with staff of the operating organizations (WWF, WCS, Uganda Wildlife Authority). There is no commercial vendor." } } }, "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 ranger force's monthly snare removals rose from a reported two or three hundred to over one thousand. A material gain in patrol effectiveness for the operating team. A chi-squared test confirmed predictions discriminated risk (p = 0.023)." }, { "group": "societal", "depth": 3, "reason": "Over one thousand snares were removed from a sanctuary identified as a tiger-reintroduction site. The paper states each snare “represents the life of one animal potentially saved”. A material landscape-scale conservation benefit rather than a documented population-level change." } ] } }, "provenance": { "kind": "legacy_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 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": "organizational", "depth": 2, "reason": "The ranger force's monthly snare removals rose from a reported two or three hundred to over one thousand. A material gain in patrol effectiveness for the operating team. A chi-squared test confirmed predictions discriminated risk (p = 0.023)." }, { "group": "societal", "depth": 3, "reason": "Over one thousand snares were removed from a sanctuary identified as a tiger-reintroduction site. The paper states each snare “represents the life of one animal potentially saved”. A material landscape-scale conservation benefit rather than a documented population-level change." } ] } }, "provenance": { "kind": "legacy_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 ranger force's monthly snare removals rose from a reported two or three hundred to over one thousand. A material gain in patrol effectiveness for the operating team. A chi-squared test confirmed predictions discriminated risk (p = 0.023)." }, { "group": "societal", "depth": 3, "reason": "Over one thousand snares were removed from a sanctuary identified as a tiger-reintroduction site. The paper states each snare “represents the life of one animal potentially saved”. 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It was field-tested with WWF Cambodia and Ministry of Environment rangers in Srepok Wildlife Sanctuary, Cambodia, from December 2018. Only the month is given, so the first day is used. Rangers received center coordinates of fifteen 3 x 3 km blocks chosen from PAWS risk predictions, without being told each block's risk class. In the first month, 72 rangers in teams of eight detected poaching in 32 cells and removed over one thousand snares. They had reported removing two or three hundred in an average month. Low-risk blocks yielded no poaching. 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No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The iWare-E models were trained on dry-season data from January 2015 through April 2018." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "value_present", "value": { "kind": "text", "text": "The models used historical patrol data to estimate where poaching was likely." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "system", "ai_role", "replication", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "Gaussian-process models within iWare-E." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "iWare-E with Gaussian-process weak learners trained on January 2015 to April 2018 dry-season data (Xu et al., ICDE 2020)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "PAWS (Protection Assistant for Wildlife Security), enhanced iWare-E predictive model with Gaussian-process uncertainty and game-theoretic patrol planning" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Harvard University and University of Southern California (Milind Tambe and colleagues), with WWF, WCS and Uganda Wildlife Authority co-authors" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "PAWS patrol-planning field test removes over 1,000 snares in Srepok Wildlife Sanctuary, Cambodia", "headline": "PAWS-directed patrols remove over 1,000 snares in Cambodia" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "faaa16a57c0d26842d7dd6d15debcd4322f41ff602ffdd7fce38bf8542a42d9c" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "PAWS (Protection Assistant for Wildlife Security) is a machine-learning pipeline that predicts poaching risk and plans patrols. It was field-tested with WWF Cambodia and Ministry of Environment rangers in Srepok Wildlife Sanctuary, Cambodia, from December 2018. Only the month is given, so the first day is used. Rangers received center coordinates of fifteen 3 x 3 km blocks chosen from PAWS risk predictions, without being told each block's risk class. In the first month, 72 rangers in teams of eight detected poaching in 32 cells and removed over one thousand snares. They had reported removing two or three hundred in an average month. Low-risk blocks yielded no poaching. A second trial ran February to March 2019.", "replication": { "produced_by": [ "Historical SMART patrol data (2015–2018) sufficient to train a risk model despite 3.6% positive labels", "Rangers willing to patrol the suggested blocks and record findings in SMART", "Dry-season deployment when rivers are passable and poaching concentrates in the north of the sanctuary" ], "must_hold": [ "Patrol capacity exists to act on predictions (SWS ranger numbers had more than doubled since 2016)", "Poacher behavior remains spatially predictable from past detections", "Findings are measured against a comparable baseline. The paper notes increases may partly reflect factors beyond the model" ], "echoes": [ { "date": "2019-02-01", "setting": "Second SWS trial, February-March 2019: high-risk blocks yielded 7 detections in 22 cells, low-risk blocks 0 in 34 cells (Table III of the same paper)", "model_version": "iWare-E predictions for January-February 2019", "outcome": "recurred", "source": "https://arxiv.org/pdf/1903.06669" }, { "date": "2020-11-05", "setting": "Ongoing use in Srepok Wildlife Sanctuary; rangers reported finding 5 to 20 times more snares since adopting PAWS, and PAWS was incorporated into SMART", "outcome": "recurred", "source": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 36, "reviewed": 0, "record_mapped": 36, "not_assessed": 55, "complete": false, "by_state": { "value_present": 36, "not_reported": 0, "unresolved": 0, "not_assessed": 55, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0196", "case_url": "records/AIBD-0196.html", "reason": "The developer is headquartered in United States; the case names Kiribati.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Kiribati" }, { "label": "Case location", "value": "Phoenix Islands Protected Area, Kiribati" } ], "filters": { "origin": "United States", "destination": [ "Kiribati" ] }, "record": { "id": "AIBD-0196", "title": "Global Fishing Watch data lets Kiribati fine a vessel US$1 million for fishing in the Phoenix Islands Protected Area", "summary": "Kiribati used the Google-Oceana-SkyTruth platform to document Marshalls 203 fishing illegally in PIPA; a US$1 million fine plus a US$1 million goodwill grant followed (reported September 2016).", "date_observed": "2016-09-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Global Fishing Watch, developed by Oceana, SkyTruth and Google, launched publicly at the Our Ocean conference on 15 September 2016. It processes AIS tracks of tens of thousands of vessels. In Oceana's description, it uses machine learning and cloud computing to identify when they are fishing. Before launch, the Washington Post reported, the government of Kiribati used it to document how a tuna-fishing vessel had operated illegally. That was inside the Phoenix Islands Protected Area, declared off-limits to commercial fishing in early 2015, resulting in a US$1 million fine. Oceana board member Ted Danson wrote in Grist that the vessel was the Marshall Islands-flagged Marshalls 203. The fine was against its owner, Central Pacific Fishing Company. A goodwill arrangement to pay a further US$1 million as a grant came with it. Oceana's Jacqueline Savitz wrote in BBC Future in 2018 that the technology helped Kiribati extract a US$2 million fine, about 1% of GDP. The cited sources do not date the fine.", "category": "benefit created", "unexpected": false, "purpose": "Make commercial fishing activity visible in near real time by processing AIS vessel tracks to identify when vessels are fishing.", "system": "Global Fishing Watch (AIS-based fishing detection using machine learning)", "model_version": null, "developer": "Oceana, SkyTruth and Google", "deployer": "Government of Kiribati", "location": "Phoenix Islands Protected Area, Kiribati", "domain": "Fisheries enforcement", "segment": "Marine protected area enforcement", "benefit_type": "Reduced Violence", "taxonomy_note": "The criminal activity penalized is illegal fishing in a protected area, and the fine is the only documented outcome.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 3, "reason": "Kiribati secured a US$1 million fine against Central Pacific Fishing Company plus a US$1 million goodwill grant (Grist). The BBC piece puts the total at US$2 million, about 1% of the country's GDP." }, { "group": "societal", "depth": null, "reason": "No source documents subsequent fishing pressure or deterrence inside the protected area." } ], "ai_role": { "status": "assessed", "statement": "An independent newspaper confirms that Kiribati used the platform to document the illegal operation. Oceana describes the platform's fishing detection as machine learning over AIS data. No cited source says whether the classifier's output, as opposed to the vessel's track, carried the case. Developer (Oceana, SkyTruth, Google) and deployer (Kiribati) are distinct." }, "evidence": [ { "kind": "news report", "citation": "Dennis B. How Google is helping to crack down on illegal fishing - from space. The Washington Post, 15 September 2016.", "title": "How Google is helping to crack down on illegal fishing - from space", "url": "https://www.washingtonpost.com/news/energy-environment/wp/2016/09/15/from-space-a-new-effort-to-crack-down-on-illegal-fishing-across-the-globe/", "date": "2016-09-15", "written_by": "independent", "quote": "The government of the Pacific island nation of Kiribati used it to document how a tuna-fishing vessel had operated illegally inside the Phoenix Islands Protected Area" }, { "kind": "news report", "citation": "Danson T. This new digital tool can help fight overfishing around the world. Grist, 15 September 2016 (author is an Oceana board member).", "title": "This new digital tool can help fight overfishing around the world", "url": "https://grist.org/science/this-new-digital-tool-could-help-fight-overfishing-around-the-world/", "date": "2016-09-15", "written_by": "makers", "quote": "This helped the country secure a $1 million fine against the Central Pacific Fishing Company, the owner of Marshalls 203." }, { "kind": "news report", "citation": "Savitz J. How to spot the secretive activities of rogue fishing boats. BBC Future, 7 June 2018 (author is Oceana's chief policy officer for North America).", "title": "How to spot the secretive activities of rogue fishing boats", "url": "https://www.bbc.com/future/article/20180607-how-to-spot-the-secretive-activities-of-rogue-fishing-boats", "date": "2018-06-07", "written_by": "makers", "quote": "Then we run them all through the Global Fishing Watch algorithm, and use machine learning and cloud computing to identify when they are fishing." }, { "kind": "press release", "citation": "Cutlip K. Indonesia Makes its Fishing Fleet Visible to the World. Global Fishing Watch, 7 June 2017.", "title": "Indonesia Makes its Fishing Fleet Visible to the World", "url": "https://globalfishingwatch.org/transparency/indonesia-shares-vms-with-global-fishing-watch/", "date": "2017-06-07", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "A vessel broadcasting AIS inside a no-take area that Kiribati closed to commercial fishing from 1 January 2015", "A government willing to prosecute on third-party satellite-derived evidence" ], "must_hold": [ "AIS transmitters left on (the BBC piece notes vessels can switch them off)", "Admissibility of AIS-derived evidence under the coastal state's law" ], "echoes": [ { "date": "2024-01-03", "setting": "Global coastal waters 2017–2021: Global Fishing Watch deep-learning mapping of untracked industrial vessels", "model_version": "SAR imagery with deep-learning models", "outcome": "unknown", "source": "Paolo FS, Kroodsma D et al. 2024, Nature, doi:10.1038/s41586-023-06825-8" } ] }, "paired_incident": null, "headline": "Global Fishing Watch helps Kiribati fine a vessel US$1 million", "metric": "a US$1 million fine plus a US$1 million goodwill grant followed", "comparator": null, "later": { "status": "scaled", "date": "2017-06-07", "note": "In June 2017 Indonesia became the first country to publish its proprietary vessel monitoring system data through Global Fishing Watch, adding nearly 5,000 vessels to the platform, with Peru announcing it would follow.", "quote": "This week, at the United Nation’s Ocean Conference, the Republic of Indonesia becomes the first nation ever to publish Vessel Monitoring System (VMS) data revealing the location and activity of its commercial fishing fleet.", "url": "https://globalfishingwatch.org/transparency/indonesia-shares-vms-with-global-fishing-watch/" }, "value": { "amount": "$1 million", "what": "fine secured by Kiribati against the owner of Marshalls 203 for fishing illegally in the Phoenix Islands Protected Area, plus an additional $1 million paid as a grant under a goodwill arrangement", "who": "Government of Kiribati", "estimate": false, "quote": "This helped the country secure a $1 million fine against the Central Pacific Fishing Company, the owner of Marshalls 203. The company also made a special “goodwill arrangement” with the government, agreeing to pay an additional $1 million in the form of a grant.", "url": "https://grist.org/science/this-new-digital-tool-could-help-fight-overfishing-around-the-world/" }, "developer_country": "United States", "deployer_country": "Kiribati", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the value as stated from the cited sources.", "source": "https://globalfishingwatch.org/transparency/indonesia-shares-vms-with-global-fishing-watch/; https://grist.org/science/this-new-digital-tool-could-help-fight-overfishing-around-the-world/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Oceana_(non-profit_group); https://en.wikipedia.org/wiki/Kiribati", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, two news reports from the makers and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0196", "catalog_version": "1.0.0", "source_fingerprint": "d51c291149dabfc21514acaa1e3cb88a3cec30bb0c61bb885ded2889f9236ed8", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0196-source-0001", "AIBD-0196-source-0002", "AIBD-0196-source-0003", "AIBD-0196-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-0196-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": "Oceana, SkyTruth and Google", "deployer": "Government of Kiribati" } }, "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": "An independent newspaper confirms that Kiribati used the platform to document the illegal operation. Oceana describes the platform's fishing detection as machine learning over AIS data. No cited source says whether the classifier's output, as opposed to the vessel's track, carried the case. Developer (Oceana, SkyTruth, Google) and deployer (Kiribati) are distinct." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "An independent newspaper confirms that Kiribati used the platform to document the illegal operation. Oceana describes the platform's fishing detection as machine learning over AIS data. No cited source says whether the classifier's output, as opposed to the vessel's track, carried the case. Developer (Oceana, SkyTruth, Google) and deployer (Kiribati) are distinct." } } }, "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": 3, "reason": "Kiribati secured a US$1 million fine against Central Pacific Fishing Company plus a US$1 million goodwill grant (Grist). 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The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 3, "reason": "Kiribati secured a US$1 million fine against Central Pacific Fishing Company plus a US$1 million goodwill grant (Grist). 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Global Fishing Watch, 7 June 2017.", "title": "Indonesia Makes its Fishing Fleet Visible to the World", "url": "https://globalfishingwatch.org/transparency/indonesia-shares-vms-with-global-fishing-watch/", "date": "2017-06-07", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Kiribati used the Google-Oceana-SkyTruth platform to document Marshalls 203 fishing illegally in PIPA; a US$1 million fine plus a US$1 million goodwill grant followed (reported September 2016).", "account": "Global Fishing Watch, developed by Oceana, SkyTruth and Google, launched publicly at the Our Ocean conference on 15 September 2016. It processes AIS tracks of tens of thousands of vessels. In Oceana's description, it uses machine learning and cloud computing to identify when they are fishing. Before launch, the Washington Post reported, the government of Kiribati used it to document how a tuna-fishing vessel had operated illegally. That was inside the Phoenix Islands Protected Area, declared off-limits to commercial fishing in early 2015, resulting in a US$1 million fine. Oceana board member Ted Danson wrote in Grist that the vessel was the Marshall Islands-flagged Marshalls 203. The fine was against its owner, Central Pacific Fishing Company. A goodwill arrangement to pay a further US$1 million as a grant came with it. Oceana's Jacqueline Savitz wrote in BBC Future in 2018 that the technology helped Kiribati extract a US$2 million fine, about 1% of GDP. The cited sources do not date the fine." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Global Fishing Watch (AIS-based fishing detection using machine learning)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Oceana, SkyTruth and Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Global Fishing Watch data lets Kiribati fine a vessel US$1 million for fishing in the Phoenix Islands Protected Area", "headline": "Global Fishing Watch helps Kiribati fine a vessel US$1 million" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "d51c291149dabfc21514acaa1e3cb88a3cec30bb0c61bb885ded2889f9236ed8" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Global Fishing Watch, developed by Oceana, SkyTruth and Google, launched publicly at the Our Ocean conference on 15 September 2016. It processes AIS tracks of tens of thousands of vessels. In Oceana's description, it uses machine learning and cloud computing to identify when they are fishing. Before launch, the Washington Post reported, the government of Kiribati used it to document how a tuna-fishing vessel had operated illegally. That was inside the Phoenix Islands Protected Area, declared off-limits to commercial fishing in early 2015, resulting in a US$1 million fine. Oceana board member Ted Danson wrote in Grist that the vessel was the Marshall Islands-flagged Marshalls 203. The fine was against its owner, Central Pacific Fishing Company. A goodwill arrangement to pay a further US$1 million as a grant came with it. Oceana's Jacqueline Savitz wrote in BBC Future in 2018 that the technology helped Kiribati extract a US$2 million fine, about 1% of GDP. The cited sources do not date the fine.", "replication": { "produced_by": [ "A vessel broadcasting AIS inside a no-take area that Kiribati closed to commercial fishing from 1 January 2015", "A government willing to prosecute on third-party satellite-derived evidence" ], "must_hold": [ "AIS transmitters left on (the BBC piece notes vessels can switch them off)", "Admissibility of AIS-derived evidence under the coastal state's law" ], "echoes": [ { "date": "2024-01-03", "setting": "Global coastal waters 2017–2021: Global Fishing Watch deep-learning mapping of untracked industrial vessels", "model_version": "SAR imagery with deep-learning models", "outcome": "unknown", "source": "Paolo FS, Kroodsma D et al. 2024, Nature, doi:10.1038/s41586-023-06825-8" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 0, "record_mapped": 32, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0343", "case_url": "records/AIBD-0343.html", "reason": "The developer is headquartered in France; the case names Canada.", "details": [ { "label": "Developer headquarters", "value": "France" }, { "label": "Case jurisdictions", "value": "Canada" }, { "label": "Case location", "value": "Aerial oil and gas field, southeastern Alberta, Canada" } ], "filters": { "origin": "France", "destination": [ "Canada" ] }, "record": { "id": "AIBD-0343", "title": "After a UNEP MARS alert, operators repair a failed ball valve that released 175 t of methane in one week at the Aerial oil and gas field, Alberta, a week after detection (April 2026)", "summary": "Trade press relaying UNEP's August 2026 snapshot: a 175 t/week methane plume at Alberta's Aerial field in April 2026 was traced to a failed ball valve on pigging equipment and repaired within a week.", "date_observed": "2026-04-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Chemical Engineer reported on 4 September 2026 on UNEP's monthly snapshot of the biggest satellite-detected methane plumes recorded in August. UNEP confirmed that operators had plugged a source at the Aerial oil and gas field in southeastern Alberta, Canada. The source had released 175 t of methane (4,900 tCO2e) in a single week in April 2026. The cause was a failed ball valve connected to pigging equipment. Operators completed the repair one week after the plume was first detected. The repair followed an alert issued under UNEP's Methane Alert and Response System (MARS). The article states that UNEP does not name suspected or known operators. It states that since 2023 MARS has sent over 3,500 alerts but confirmed just 43 mitigation cases. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments. It couples that with scientific expertise and advanced AI models to notify governments and companies. The August 2026 UNEP snapshot itself is not among the sources; UNEP's snapshot page now carries the September 2026 update, which does not mention Canada. UNEP's press pages were not available and are not among the sources. The case rests on one trade-press report relaying UNEP.", "category": "benefit created", "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose.", "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation", "model_version": null, "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed operators at the Aerial oil and gas field (repair)", "location": "Aerial oil and gas field, southeastern Alberta, Canada", "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas production; intergovernmental observatory", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 2, "reason": "The operator found and fixed a failed valve releasing 175 t of methane a week, within one week of detection. The operator is unnamed, and no cost or further detail is reported." }, { "group": "societal", "depth": 1, "reason": "A single confirmed case among about 43 since 2023, against more than 3,500 alerts. A one-week 4,900 tCO2e release was stopped." } ], "ai_role": { "status": "assessed", "statement": "The report credits the repair to a MARS alert and UNEP's confirmation. Only UNEP's general page describes MARS detection and analysis as using satellites with advanced AI models. The AI contribution to this specific alert is not isolated. One news item relaying the system operator's snapshot documents the event, and the snapshot itself is not among the sources." }, "evidence": [ { "kind": "news report", "citation": "Baker, S. (2026). Canada plugs 'major source' of methane emissions from oil and gas field leak. The Chemical Engineer, 4 Sep 2026.", "title": "Canada plugs 'major source' of methane emissions from oil and gas field leak", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Operators completed the repair work one week after the methane plume was first detected" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emission estimate.", "title": "Canada plugs 'major source' of methane emissions (emission estimate)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "175 t of methane (4,900 tCO2e) in a single week in April" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, cause of the leak.", "title": "Canada plugs 'major source' of methane emissions (cause)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "a failed ball valve connected to some pigging equipment" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, programme totals.", "title": "Canada plugs 'major source' of methane emissions (MARS totals)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Since 2023, MARS has sent over 3,500 alerts to operators of sites releasing methane plumes, but has confirmed just 43 mitigation cases." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "date": null, "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ], "replication": { "produced_by": [ "Satellite detection of a large plume at a producing field", "A MARS alert reaching operators able to inspect equipment within days", "A discrete equipment failure (ball valve on pigging equipment) that a repair could end" ], "must_hold": [ "Responsive operators or a national focal point. UNEP's snapshot says most mitigation occurs where focal points direct information", "Clear-sky satellite coverage to detect and later confirm cessation", "Independent confirmation beyond UNEP's own snapshot, which is not among the sources" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped by rerouting gas after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "model_version": null, "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091): MARS notification followed by repair", "model_version": null, "outcome": "recurred", "source": "AIBD-0091" } ] }, "paired_incident": null, "headline": "UNEP MARS alert leads to Alberta methane leak repair in a week", "metric": "released 175 t of methane (4,900 tCO2e) in a single week", "comparator": { "against": "the leak before repair", "before": "175 t of methane (4,900 tCO2e) in a single week in April", "after": "Operators completed the repair work one week after the methane plume was first detected.", "basis": "measured", "quote": "In its monthly snapshot of the biggest methane plumes recorded by satellites in August, the UNEP confirmed operators had plugged a source of methane that emitted 175 t of methane (4,900 tCO2e) in a single week in April.", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" }, "later": { "status": "unknown", "date": null, "note": "Nothing dated after the 4 September 2026 article was found; UNEP's September 2026 snapshot does not mention Canada.", "quote": null, "url": null }, "value": null, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0343", "catalog_version": "1.0.0", "source_fingerprint": "0ef391e4175d499d06ad640f10c507dee33241c2b451a8983764232aa2f2583e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0343-source-0001", "AIBD-0343-source-0002", "AIBD-0343-source-0003", "AIBD-0343-source-0004", "AIBD-0343-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed operators at the Aerial oil and gas field (repair)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The report credits the repair to a MARS alert and UNEP's confirmation. Only UNEP's general page describes MARS detection and analysis as using satellites with advanced AI models. The AI contribution to this specific alert is not isolated. One news item relaying the system operator's snapshot documents the event, and the snapshot itself is not among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The report credits the repair to a MARS alert and UNEP's confirmation. Only UNEP's general page describes MARS detection and analysis as using satellites with advanced AI models. The AI contribution to this specific alert is not isolated. One news item relaying the system operator's snapshot documents the event, and the snapshot itself is not among the sources." } } }, "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." }, { "group": "organizational", "depth": 2, "reason": "The operator found and fixed a failed valve releasing 175 t of methane a week, within one week of detection. The operator is unnamed, and no cost or further detail is reported." }, { "group": "societal", "depth": 1, "reason": "A single confirmed case among about 43 since 2023, against more than 3,500 alerts. A one-week 4,900 tCO2e release was stopped." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 2, "reason": "The operator found and fixed a failed valve releasing 175 t of methane a week, within one week of detection. The operator is unnamed, and no cost or further detail is reported." }, { "group": "societal", "depth": 1, "reason": "A single confirmed case among about 43 since 2023, against more than 3,500 alerts. A one-week 4,900 tCO2e release was stopped." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "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." }, { "group": "organizational", "depth": 2, "reason": "The operator found and fixed a failed valve releasing 175 t of methane a week, within one week of detection. The operator is unnamed, and no cost or further detail is reported." }, { "group": "societal", "depth": 1, "reason": "A single confirmed case among about 43 since 2023, against more than 3,500 alerts. A one-week 4,900 tCO2e release was stopped." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "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", 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"provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Chemical Engineer reported on 4 September 2026 on UNEP's monthly snapshot of the biggest satellite-detected methane plumes recorded in August. UNEP confirmed that operators had plugged a source at the Aerial oil and gas field in southeastern Alberta, Canada. The source had released 175 t of methane (4,900 tCO2e) in a single week in April 2026. The cause was a failed ball valve connected to pigging equipment. Operators completed the repair one week after the plume was first detected. The repair followed an alert issued under UNEP's Methane Alert and Response System (MARS). The article states that UNEP does not name suspected or known operators. It states that since 2023 MARS has sent over 3,500 alerts but confirmed just 43 mitigation cases. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments. It couples that with scientific expertise and advanced AI models to notify governments and companies. The August 2026 UNEP snapshot itself is not among the sources; UNEP's snapshot page now carries the September 2026 update, which does not mention Canada. UNEP's press pages were not available and are not among the sources. The case rests on one trade-press report relaying UNEP.", "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 2, "reason": "The operator found and fixed a failed valve releasing 175 t of methane a week, within one week of detection. The operator is unnamed, and no cost or further detail is reported." }, { "group": "societal", "depth": 1, "reason": "A single confirmed case among about 43 since 2023, against more than 3,500 alerts. A one-week 4,900 tCO2e release was stopped." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Aerial oil and gas field, southeastern Alberta, Canada", "developer_country": "France", "deployer_country": "France" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. 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UNEP's snapshot says most mitigation occurs where focal points direct information", "Clear-sky satellite coverage to detect and later confirm cessation", "Independent confirmation beyond UNEP's own snapshot, which is not among the sources" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped by rerouting gas after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091): MARS notification followed by repair", "outcome": "recurred", "source": "AIBD-0091" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite detection of a large plume at a producing field", "A MARS alert reaching operators able to inspect equipment within days", "A discrete equipment failure (ball valve on pigging equipment) that a repair could end" ], "must_hold": [ "Responsive operators or a national focal point. 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(2026). Canada plugs 'major source' of methane emissions from oil and gas field leak. The Chemical Engineer, 4 Sep 2026.", "title": "Canada plugs 'major source' of methane emissions from oil and gas field leak", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Operators completed the repair work one week after the methane plume was first detected" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emission estimate.", "title": "Canada plugs 'major source' of methane emissions (emission estimate)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "175 t of methane (4,900 tCO2e) in a single week in April" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, cause of the leak.", "title": "Canada plugs 'major source' of methane emissions (cause)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "a failed ball valve connected to some pigging equipment" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, programme totals.", "title": "Canada plugs 'major source' of methane emissions (MARS totals)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Since 2023, MARS has sent over 3,500 alerts to operators of sites releasing methane plumes, but has confirmed just 43 mitigation cases." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0343-source-0001", "AIBD-0343-source-0002", "AIBD-0343-source-0003", "AIBD-0343-source-0004", "AIBD-0343-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Baker, S. (2026). Canada plugs 'major source' of methane emissions from oil and gas field leak. The Chemical Engineer, 4 Sep 2026.", "title": "Canada plugs 'major source' of methane emissions from oil and gas field leak", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Operators completed the repair work one week after the methane plume was first detected" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emission estimate.", "title": "Canada plugs 'major source' of methane emissions (emission estimate)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "175 t of methane (4,900 tCO2e) in a single week in April" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, cause of the leak.", "title": "Canada plugs 'major source' of methane emissions (cause)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "a failed ball valve connected to some pigging equipment" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, programme totals.", "title": "Canada plugs 'major source' of methane emissions (MARS totals)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Since 2023, MARS has sent over 3,500 alerts to operators of sites releasing methane plumes, but has confirmed just 43 mitigation cases." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Trade press relaying UNEP's August 2026 snapshot: a 175 t/week methane plume at Alberta's Aerial field in April 2026 was traced to a failed ball valve on pigging equipment and repaired within a week.", "account": "The Chemical Engineer reported on 4 September 2026 on UNEP's monthly snapshot of the biggest satellite-detected methane plumes recorded in August. UNEP confirmed that operators had plugged a source at the Aerial oil and gas field in southeastern Alberta, Canada. The source had released 175 t of methane (4,900 tCO2e) in a single week in April 2026. The cause was a failed ball valve connected to pigging equipment. Operators completed the repair one week after the plume was first detected. The repair followed an alert issued under UNEP's Methane Alert and Response System (MARS). The article states that UNEP does not name suspected or known operators. It states that since 2023 MARS has sent over 3,500 alerts but confirmed just 43 mitigation cases. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments. It couples that with scientific expertise and advanced AI models to notify governments and companies. The August 2026 UNEP snapshot itself is not among the sources; UNEP's snapshot page now carries the September 2026 update, which does not mention Canada. UNEP's press pages were not available and are not among the sources. The case rests on one trade-press report relaying UNEP." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "After a UNEP MARS alert, operators repair a failed ball valve that released 175 t of methane in one week at the Aerial oil and gas field, Alberta, a week after detection (April 2026)", "headline": "UNEP MARS alert leads to Alberta methane leak repair in a week" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "0ef391e4175d499d06ad640f10c507dee33241c2b451a8983764232aa2f2583e" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Chemical Engineer reported on 4 September 2026 on UNEP's monthly snapshot of the biggest satellite-detected methane plumes recorded in August. UNEP confirmed that operators had plugged a source at the Aerial oil and gas field in southeastern Alberta, Canada. The source had released 175 t of methane (4,900 tCO2e) in a single week in April 2026. The cause was a failed ball valve connected to pigging equipment. Operators completed the repair one week after the plume was first detected. The repair followed an alert issued under UNEP's Methane Alert and Response System (MARS). The article states that UNEP does not name suspected or known operators. It states that since 2023 MARS has sent over 3,500 alerts but confirmed just 43 mitigation cases. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments. It couples that with scientific expertise and advanced AI models to notify governments and companies. The August 2026 UNEP snapshot itself is not among the sources; UNEP's snapshot page now carries the September 2026 update, which does not mention Canada. UNEP's press pages were not available and are not among the sources. The case rests on one trade-press report relaying UNEP.", "replication": { "produced_by": [ "Satellite detection of a large plume at a producing field", "A MARS alert reaching operators able to inspect equipment within days", "A discrete equipment failure (ball valve on pigging equipment) that a repair could end" ], "must_hold": [ "Responsive operators or a national focal point. UNEP's snapshot says most mitigation occurs where focal points direct information", "Clear-sky satellite coverage to detect and later confirm cessation", "Independent confirmation beyond UNEP's own snapshot, which is not among the sources" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped by rerouting gas after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091): MARS notification followed by repair", "outcome": "recurred", "source": "AIBD-0091" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0158", "case_url": "records/AIBD-0158.html", "reason": "The developer is headquartered in United States; the case names Australia.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Australia" }, { "label": "Case location", "value": "Sydney, Australia" } ], "filters": { "origin": "United States", "destination": [ "Australia" ] }, "record": { "id": "AIBD-0158", "title": "Paul Conyngham uses ChatGPT and AlphaFold to choose targets for a personalized mRNA vaccine that UNSW made for his dog Rosie's mast cell cancer; her largest tumor halved", "summary": "AI-chosen targets, a UNSW-made mRNA vaccine and a checkpoint inhibitor halved Rosie's largest tumor by early 2026; partial, not a cure.", "date_observed": "2026-03-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Paul Conyngham, a Sydney machine-learning engineer, used ChatGPT to plan treatment for his dog Rosie. Rosie is an eight-year-old Staffy-Shar Pei cross with mast cell cancer diagnosed in 2024. He used AlphaFold to model mutated proteins from tumor sequencing he paid $3,000 for at UNSW's Ramaciotti Centre for Genomics. He chose neoantigen targets and confirmed them “again using AI”. Pall Thordarson's UNSW RNA Institute made the mRNA vaccine in under two months. Rachel Allavena of the University of Queensland injected it at Gatton in December 2025, with a checkpoint inhibitor, and gave a booster in February 2026. The tennis-ball-sized tumor on Rosie's hock halved, and she was chasing rabbits by late January 2026. Some tumors did not respond. By August 2026 the cancer had returned after a later surgery.", "category": "benefit created", "unexpected": false, "purpose": "ChatGPT is a general-purpose assistant and AlphaFold a protein-structure predictor. Conyngham used the first to plan and navigate the veterinary and genomic work. He used the second to model mutated proteins as candidate targets. Both are uses within what each tool was built to do. Neither was built to treat Rosie. The vaccine was designed by Conyngham, made by UNSW and given by a veterinary school. The therapeutic response is a downstream use of the tools rather than their product purpose.", "system": "ChatGPT (OpenAI) and AlphaFold (Google DeepMind), with Grok (xAI) reported for the final mRNA construct, used with Conyngham's own analysis to select tumor neoantigen targets for a personalized mRNA vaccine", "model_version": null, "developer": "OpenAI (ChatGPT); Google DeepMind (AlphaFold); xAI (Grok)", "deployer": "Paul Conyngham (owner), with UNSW's Ramaciotti Centre for Genomics (sequencing), the UNSW RNA Institute (vaccine manufacture) and the University of Queensland School of Veterinary Science, Gatton (administration)", "location": "Sydney, Australia", "domain": "Veterinary oncology / personalized mRNA vaccine design", "segment": "Companion animal with mast cell cancer", "benefit_type": "Improved Physical Health", "taxonomy_note": "The patient is a dog; Trustible's health type is written for people.", "registry_ids": { "oecd_aim": "2026-03-15-f424" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "Rosie was given months to live after surgery and chemotherapy. Her largest tumor halved and she regained mobility by late January 2026. Per ABC on 22 June 2026, she was still running and playing. The response was partial and confounded by a checkpoint inhibitor, and her cancer later returned." }, { "group": "organizational", "depth": 1, "reason": "UNSW's RNA Institute and Ramaciotti Center got what Thordarson called the first personalized cancer vaccine designed for a dog. They describe the case as a milestone. Conyngham's spin-out GAMGEE raised a $4 million seed round in August 2026 on the strength of it." } ], "ai_role": { "status": "assessed", "statement": "The AI tools worked at the design stage. ChatGPT suggested immunotherapy, pointed Conyngham to UNSW's genomics center and helped him plan the analysis. AlphaFold modeled mutated proteins. He says he “confirmed, again using AI, that those targets were good cancer candidates”. Humans sequenced the genome (Martin Smith), made the mRNA and nanoparticles (Pall Thordarson) and injected it (Rachel Allavena). A checkpoint inhibitor was given at the same time. UNSW says “We don't know how much of the response came from the checkpoint inhibitor and how much from the mRNA vaccine”. The AI's share of the outcome therefore cannot be isolated. It is a single uncontrolled case, and no outcome data have been published outside interviews and statements." }, "evidence": [ { "kind": "press release", "citation": "Melville, T. / UNSW Sydney (2026). Meet the man who designed a cancer vaccine for his dog. UNSW Newsroom, 17 March 2026.", "title": "Meet the man who designed a cancer vaccine for his dog", "url": "https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog", "date": "2026-03-17", "written_by": "deployer", "quote": "We don't know how much of the response came from the checkpoint inhibitor and how much from the mRNA vaccine." }, { "kind": "news report", "citation": "Ma, J. / Fortune (2026). A tech entrepreneur used AI to help create the first-ever bespoke cancer vaccine for a dog. Fortune, 15 March 2026.", "title": "A tech entrepreneur used AI to help create the first-ever bespoke cancer vaccine for a dog", "url": "https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/", "date": "2026-03-15", "written_by": "independent", "quote": "Rosie got her first injection of the cancer treatment this past December, then received a booster in February." }, { "kind": "news report", "citation": "Bita, N. / The Australian, republished by Stockhead (2026). Tech boss uses AI and ChatGPT to create cancer vaccine for his dying dog. Stockhead, 18 March 2026.", "title": "Tech boss uses AI and ChatGPT to create cancer vaccine for his dying dog", "url": "https://stockhead.com.au/tech/tech-boss-uses-ai-and-chatgpt-to-create-cancer-vaccine-for-his-dying-dog/", "date": "2026-03-18", "written_by": "independent", "quote": "Mr Conyngham drove 10 hours with Rosie for her first injection in December, followed by a booster shot last month and another due next week." }, { "kind": "other", "citation": "Stebbing, J. / The Conversation (2026). A man used AI to help make a cancer vaccine for his dog – an oncologist urges caution. The Conversation, 20 March 2026 (academic commentary).", "title": "A man used AI to help make a cancer vaccine for his dog – an oncologist urges caution", "url": "https://theconversation.com/a-man-used-ai-to-help-make-a-cancer-vaccine-for-his-dog-an-oncologist-urges-caution-278735", "date": "2026-03-20", "written_by": "independent", "quote": "several tumours shrank markedly, her overall tumour burden fell, and her energy and behaviour improved" }, { "kind": "news report", "citation": "Roberts, R. / The Scientist (2026). ChatGPT and AlphaFold Help Design Personalized Vaccine for Dog with Cancer. The Scientist, online 16 March 2026, June 2026 issue.", "title": "ChatGPT and AlphaFold Help Design Personalized Vaccine for Dog with Cancer", "url": "https://www.the-scientist.com/chatgpt-and-alphafold-help-design-personalized-vaccine-for-dog-with-cancer-74227", "date": "2026-03-16", "written_by": "independent", "quote": "it is difficult to disentangle which of the treatments had the greatest effect." }, { "kind": "news report", "citation": "AFP via Phys.org (2026). One man, his dog, and ChatGPT: Australia's AI vaccine saga. Phys.org, 30 March 2026.", "title": "One man, his dog, and ChatGPT: Australia's AI vaccine saga", "url": "https://phys.org/news/2026-03-dog-chatgpt-australia-ai-vaccine.html", "date": "2026-03-30", "written_by": "independent", "quote": "This was not a clinical trial by any means" }, { "kind": "news report", "citation": "Kim, S. / Newsweek (2026). Owner With No Medical Background Invents Cure for Dog's Terminal Cancer. Newsweek, 16 March 2026.", "title": "Owner With No Medical Background Invents Cure for Dog's Terminal Cancer", "url": "https://www.newsweek.com/owner-no-medical-background-invents-cure-dogs-terminal-cancer-11684882", "date": "2026-03-16", "written_by": "independent", "quote": "And, toward the end of January, she was jumping over a fence to chase a rabbit." }, { "kind": "news report", "citation": "Bailey, R. / Reason (2026). Man Successfully Designs mRNA Vaccine To Treat His Dog's Cancer. Reason, 19 March 2026.", "title": "Man Successfully Designs mRNA Vaccine To Treat His Dog's Cancer", "url": "https://reason.com/2026/03/19/man-successfully-designs-mrna-vaccine-to-treat-his-dogs-cancer/", "date": "2026-03-19", "written_by": "independent", "quote": "If we can do this for a dog, why aren't we rolling this out to all humans with cancer?" }, { "kind": "news report", "citation": "Davis, M. / ABC News (2026). AI designs cancer vaccine for dog but scientists says red tape a barrier for human care. ABC News, 22 June 2026.", "title": "AI designs cancer vaccine for dog but scientists says red tape a barrier for human care", "url": "https://www.abc.net.au/news/2026-06-22/australian-dog-cancer-treated-via-mrna-vaccine-and-ai/106692668", "date": "2026-06-22", "written_by": "independent", "quote": "AI did not cure Rosie's cancer, nor did it create or administer the vaccine" }, { "kind": "news report", "citation": "Palmer, A. / Medical Daily (2026). Rosie's Cancer Came Back After Her AI-Designed Vaccine, and Her Owner Just Raised $4 Million. Medical Daily, 18 August 2026.", "title": "Rosie's Cancer Came Back After Her AI-Designed Vaccine, and Her Owner Just Raised $4 Million", "url": "https://www.medicaldaily.com/rosie-dog-ai-mrna-cancer-vaccine-gamgee-launch-477500", "date": "2026-08-18", "written_by": "independent", "quote": "Rosie entered remission, but her cancer returned following a later surgery." }, { "kind": "press release", "citation": "GAMGEE via GlobeNewswire (2026). One Dog's Legacy Becomes GAMGEE. GlobeNewswire press release, Sydney, 17 August 2026, as carried by Yahoo Finance.", "title": "One Dog's Legacy Becomes GAMGEE", "url": "https://finance.yahoo.com/healthcare/articles/one-dogs-legacy-becomes-gamgee-190000768.html", "date": "2026-08-17", "written_by": "deployer", "quote": "While her story may have ended, her legacy lives on in the work that followed." } ], "replication": { "produced_by": [ "Whole-genome sequencing of tumor and healthy tissue, about 150 billion DNA letters for $3,000, from which neoantigen targets could be chosen with AI help.", "An academic RNA facility, the UNSW RNA Institute, willing to make a custom mRNA vaccine from an owner-designed sequence in under two months.", "A veterinary school with ethics cover and an immunotherapy specialist to administer it, after about three months of ethics paperwork.", "An owner with 17 years of machine-learning experience able to run the process, pay for it and drive ten hours for each dose." ], "must_hold": [ "The response cannot be credited to the vaccine alone. A checkpoint inhibitor was given at the same time, and UNSW says the split is unknown.", "One uncontrolled animal. Mast cell tumors behave unpredictably, and some of Rosie's tumors did not respond.", "The benefit was time, not cure. The cancer returned after a later surgery, and the company founded on the case wrote in August 2026 that her story may have ended." ], "echoes": [] }, "paired_incident": null, "headline": "ChatGPT and AlphaFold pick vaccine targets; dog's tumor halves", "metric": "halved Rosie's largest tumor by early 2026", "comparator": { "against": "veterinary chemotherapy and surgery given before the vaccine", "before": "slowed but failed to shrink the tumours", "after": "the tennis ball-sized tumour on Rosie’s hock has shrunk in half", "basis": "assumed", "quote": "Heartbroken when his fur-baby was diagnosed with a deadly mast cell cancer in 2024, Mr ­Conyngham threw thousands of dollars at veterinary chemotherapy and surgery, which slowed but failed to shrink the tumours. Now, after treatment with a custom mRNA cancer vaccine over the Christmas break, the tennis ball-sized tumour on Rosie’s hock has shrunk in half, in a recovery that has astounded researchers at the cutting-edge of human cancer treatments.", "url": "https://stockhead.com.au/tech/tech-boss-uses-ai-and-chatgpt-to-create-cancer-vaccine-for-his-dying-dog/" }, "later": { "status": "reversed", "date": "2026-08-17", "note": "The GAMGEE launch release of 17 August 2026 says Rosie's cancer returned after a later surgery and writes of her in the past tense.", "quote": "Rosie entered remission, but her cancer returned following a later surgery.", "url": "https://finance.yahoo.com/healthcare/articles/one-dogs-legacy-becomes-gamgee-190000768.html" }, "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 eleven sources on file: UNSW's newsroom story of 17 March 2026, The Australian's report by Natasha Bita as republished by Stockhead, Fortune, The Conversation (Justin Stebbing, Anglia Ruskin University), The Scientist, AFP via Phys.org, Newsweek, Reason, ABC News of 22 June 2026, Medical Daily of 18 August 2026 and GAMGEE's GlobeNewswire release, marked written by the makers because the company was founded by Conyngham on this case. Kept at benefit created or restored: a therapeutic response for one identifiable animal and her owner, not a prevented specified harm; the Monitor labels it an incident with harm type health impact. Dates: the first injection was in December 2025 and the booster in February 2026 per The Australian (“last month” in a piece dated 18 March) and Fortune; Medical Daily says “the following month”; no source gives a day, so, as for AIBD-0052, the Monitor's date, which is Fortune's report date, is used. Outcome figures differ by speaker: Allavena “probably halved” for the hock tumor; Conyngham “about 75 percent of the cancer” on Today; UNSW “partial response”. A checkpoint inhibitor was given with the vaccine (UNSW, The Scientist, AFP), which is why the system's part says the split is unknown. WinBuzzer's “December 2024” is contradicted by every other source and not used. The Australian (the original of 13 March and a 26 June 2026 piece whose headline in a Google News feed reads “Rescue dog dies after owner's pioneering AI cancer fight”), The Verge, news.com.au, Daily Mail and NY Post could not be read; the death is therefore not asserted in the account, which records the relapse reported by Medical Daily and GAMGEE. The WebSearch budget was exhausted, so sources were found through the Monitor's link list and Google and Bing news feeds.", "source": "https://oecd.ai/en/incidents/2026-03-15-f424", "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, what happened since from the cited sources.", "source": "https://stockhead.com.au/tech/tech-boss-uses-ai-and-chatgpt-to-create-cancer-vaccine-for-his-dying-dog/; https://finance.yahoo.com/healthcare/articles/one-dogs-legacy-becomes-gamgee-190000768.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" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: eight independent news reports, one other independent source 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-0158", "catalog_version": "1.0.0", "source_fingerprint": "1a21cd2cdf56ace508c40de6d3b87e3a17d0d57899f0cfcce9bb7c49722ccd36", "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" } ], "source_ids": [ "AIBD-0158-source-0001", "AIBD-0158-source-0002", "AIBD-0158-source-0003", "AIBD-0158-source-0004", "AIBD-0158-source-0005", "AIBD-0158-source-0006", "AIBD-0158-source-0007", "AIBD-0158-source-0008", "AIBD-0158-source-0009", "AIBD-0158-source-0010", "AIBD-0158-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": "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 (ChatGPT); Google DeepMind (AlphaFold); xAI (Grok)", "deployer": "Paul Conyngham (owner), with UNSW's Ramaciotti Centre for Genomics (sequencing), the UNSW RNA Institute (vaccine manufacture) and the University of Queensland School of Veterinary Science, Gatton (administration)" } }, "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 tools worked at the design stage. ChatGPT suggested immunotherapy, pointed Conyngham to UNSW's genomics center and helped him plan the analysis. AlphaFold modeled mutated proteins. He says he “confirmed, again using AI, that those targets were good cancer candidates”. Humans sequenced the genome (Martin Smith), made the mRNA and nanoparticles (Pall Thordarson) and injected it (Rachel Allavena). A checkpoint inhibitor was given at the same time. UNSW says “We don't know how much of the response came from the checkpoint inhibitor and how much from the mRNA vaccine”. The AI's share of the outcome therefore cannot be isolated. It is a single uncontrolled case, and no outcome data have been published outside interviews and 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", "display": { "answer": "Conyngham used ChatGPT to plan the investigation and AlphaFold to model mutated proteins when selecting targets for a personalized cancer vaccine for his dog. Grok was also reported as assisting with the final mRNA construct.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The AI tools worked at the design stage. ChatGPT suggested immunotherapy, pointed Conyngham to UNSW's genomics center and helped him plan the analysis. AlphaFold modeled mutated proteins. He says he “confirmed, again using AI, that those targets were good cancer candidates”. Humans sequenced the genome (Martin Smith), made the mRNA and nanoparticles (Pall Thordarson) and injected it (Rachel Allavena). A checkpoint inhibitor was given at the same time. UNSW says “We don't know how much of the response came from the checkpoint inhibitor and how much from the mRNA vaccine”. The AI's share of the outcome therefore cannot be isolated. It is a single uncontrolled case, and no outcome data have been published outside interviews and 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", "depth": 2, "reason": "Rosie was given months to live after surgery and chemotherapy. Her largest tumor halved and she regained mobility by late January 2026. Per ABC on 22 June 2026, she was still running and playing. The response was partial and confounded by a checkpoint inhibitor, and her cancer later returned." }, { "group": "organizational", "depth": 1, "reason": "UNSW's RNA Institute and Ramaciotti Center got what Thordarson called the first personalized cancer vaccine designed for a dog. They describe the case as a milestone. Conyngham's spin-out GAMGEE raised a $4 million seed round in August 2026 on the strength of it." } ] } }, "provenance": { "kind": "legacy_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, one other independent source 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": 2, "reason": "Rosie was given months to live after surgery and chemotherapy. Her largest tumor halved and she regained mobility by late January 2026. Per ABC on 22 June 2026, she was still running and playing. The response was partial and confounded by a checkpoint inhibitor, and her cancer later returned." }, { "group": "organizational", "depth": 1, "reason": "UNSW's RNA Institute and Ramaciotti Center got what Thordarson called the first personalized cancer vaccine designed for a dog. They describe the case as a milestone. Conyngham's spin-out GAMGEE raised a $4 million seed round in August 2026 on the strength of it." } ] } }, "provenance": { "kind": "legacy_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": "Rosie was given months to live after surgery and chemotherapy. Her largest tumor halved and she regained mobility by late January 2026. Per ABC on 22 June 2026, she was still running and playing. The response was partial and confounded by a checkpoint inhibitor, and her cancer later returned." }, { "group": "organizational", "depth": 1, "reason": "UNSW's RNA Institute and Ramaciotti Center got what Thordarson called the first personalized cancer vaccine designed for a dog. They describe the case as a milestone. Conyngham's spin-out GAMGEE raised a $4 million seed round in August 2026 on the strength of it." } ] } }, "provenance": { "kind": "legacy_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": "halved Rosie's largest tumor by early 2026", "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 dog received a checkpoint inhibitor alongside the vaccine. UNSW said that the contribution of each treatment was unknown. The uncontrolled account does not isolate AI’s contribution, and the reported results come from interviews and statements.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-03-15-f424" } } }, "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": "2026-08-17", "note": "The GAMGEE launch release of 17 August 2026 says Rosie's cancer returned after a later surgery and writes of her in the past tense.", "quote": "Rosie entered remission, but her cancer returned following a later surgery.", "url": "https://finance.yahoo.com/healthcare/articles/one-dogs-legacy-becomes-gamgee-190000768.html" } } }, "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": "Researchers sequenced the tumor and manufactured the vaccine. A veterinarian administered the treatment." }, "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-0158" } }, "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; Trustible's health type is written for people." } }, "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": "Paul Conyngham, a Sydney machine-learning engineer, used ChatGPT to plan treatment for his dog Rosie. Rosie is an eight-year-old Staffy-Shar Pei cross with mast cell cancer diagnosed in 2024. He used AlphaFold to model mutated proteins from tumor sequencing he paid $3,000 for at UNSW's Ramaciotti Centre for Genomics. He chose neoantigen targets and confirmed them “again using AI”. Pall Thordarson's UNSW RNA Institute made the mRNA vaccine in under two months. Rachel Allavena of the University of Queensland injected it at Gatton in December 2025, with a checkpoint inhibitor, and gave a booster in February 2026. The tennis-ball-sized tumor on Rosie's hock halved, and she was chasing rabbits by late January 2026. Some tumors did not respond. By August 2026 the cancer had returned after a later 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": "Rosie was given months to live after surgery and chemotherapy. Her largest tumor halved and she regained mobility by late January 2026. Per ABC on 22 June 2026, she was still running and playing. The response was partial and confounded by a checkpoint inhibitor, and her cancer later returned." }, { "group": "organizational", "depth": 1, "reason": "UNSW's RNA Institute and Ramaciotti Center got what Thordarson called the first personalized cancer vaccine designed for a dog. They describe the case as a milestone. Conyngham's spin-out GAMGEE raised a $4 million seed round in August 2026 on the strength of it." } ] } }, "provenance": { "kind": "legacy_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 assistant and AlphaFold a protein-structure predictor. Conyngham used the first to plan and navigate the veterinary and genomic work. He used the second to model mutated proteins as candidate targets. Both are uses within what each tool was built to do. Neither was built to treat Rosie. The vaccine was designed by Conyngham, made by UNSW and given by a veterinary school. The therapeutic response is a downstream use of the tools rather than their product 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": "Sydney, Australia", "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 and AlphaFold a protein-structure predictor. Conyngham used the first to plan and navigate the veterinary and genomic work. He used the second to model mutated proteins as candidate targets. Both are uses within what each tool was built to do. Neither was built to treat Rosie. The vaccine was designed by Conyngham, made by UNSW and given by a veterinary school. The therapeutic response is a downstream use of the tools rather than their product 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": "2026-03-15", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and eleven sources on file: UNSW's newsroom story of 17 March 2026, The Australian's report by Natasha Bita as republished by Stockhead, Fortune, The Conversation (Justin Stebbing, Anglia Ruskin University), The Scientist, AFP via Phys.org, Newsweek, Reason, ABC News of 22 June 2026, Medical Daily of 18 August 2026 and GAMGEE's GlobeNewswire release, marked written by the makers because the company was founded by Conyngham on this case. Kept at benefit created or restored: a therapeutic response for one identifiable animal and her owner, not a prevented specified harm; the Monitor labels it an incident with harm type health impact. Dates: the first injection was in December 2025 and the booster in February 2026 per The Australian (“last month” in a piece dated 18 March) and Fortune; Medical Daily says “the following month”; no source gives a day, so, as for AIBD-0052, the Monitor's date, which is Fortune's report date, is used. Outcome figures differ by speaker: Allavena “probably halved” for the hock tumor; Conyngham “about 75 percent of the cancer” on Today; UNSW “partial response”. A checkpoint inhibitor was given with the vaccine (UNSW, The Scientist, AFP), which is why the system's part says the split is unknown. WinBuzzer's “December 2024” is contradicted by every other source and not used. The Australian (the original of 13 March and a 26 June 2026 piece whose headline in a Google News feed reads “Rescue dog dies after owner's pioneering AI cancer fight”), The Verge, news.com.au, Daily Mail and NY Post could not be read; the death is therefore not asserted in the account, which records the relapse reported by Medical Daily and GAMGEE. The WebSearch budget was exhausted, so sources were found through the Monitor's link list and Google and Bing news feeds.", "source": "https://oecd.ai/en/incidents/2026-03-15-f424", "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, what happened since from the cited sources.", "source": "https://stockhead.com.au/tech/tech-boss-uses-ai-and-chatgpt-to-create-cancer-vaccine-for-his-dying-dog/; https://finance.yahoo.com/healthcare/articles/one-dogs-legacy-becomes-gamgee-190000768.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" } ] } }, "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": [ "Whole-genome sequencing of tumor and healthy tissue, about 150 billion DNA letters for $3,000, from which neoantigen targets could be chosen with AI help.", "An academic RNA facility, the UNSW RNA Institute, willing to make a custom mRNA vaccine from an owner-designed sequence in under two months.", "A veterinary school with ethics cover and an immunotherapy specialist to administer it, after about three months of ethics paperwork.", "An owner with 17 years of machine-learning experience able to run the process, pay for it and drive ten hours for each dose." ], "must_hold": [ "The response cannot be credited to the vaccine alone. A checkpoint inhibitor was given at the same time, and UNSW says the split is unknown.", "One uncontrolled animal. Mast cell tumors behave unpredictably, and some of Rosie's tumors did not respond.", "The benefit was time, not cure. The cancer returned after a later surgery, and the company founded on the case wrote in August 2026 that her story may have ended." ], "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 eleven sources on file: UNSW's newsroom story of 17 March 2026, The Australian's report by Natasha Bita as republished by Stockhead, Fortune, The Conversation (Justin Stebbing, Anglia Ruskin University), The Scientist, AFP via Phys.org, Newsweek, Reason, ABC News of 22 June 2026, Medical Daily of 18 August 2026 and GAMGEE's GlobeNewswire release, marked written by the makers because the company was founded by Conyngham on this case. Kept at benefit created or restored: a therapeutic response for one identifiable animal and her owner, not a prevented specified harm; the Monitor labels it an incident with harm type health impact. Dates: the first injection was in December 2025 and the booster in February 2026 per The Australian (“last month” in a piece dated 18 March) and Fortune; Medical Daily says “the following month”; no source gives a day, so, as for AIBD-0052, the Monitor's date, which is Fortune's report date, is used. Outcome figures differ by speaker: Allavena “probably halved” for the hock tumor; Conyngham “about 75 percent of the cancer” on Today; UNSW “partial response”. A checkpoint inhibitor was given with the vaccine (UNSW, The Scientist, AFP), which is why the system's part says the split is unknown. WinBuzzer's “December 2024” is contradicted by every other source and not used. The Australian (the original of 13 March and a 26 June 2026 piece whose headline in a Google News feed reads “Rescue dog dies after owner's pioneering AI cancer fight”), The Verge, news.com.au, Daily Mail and NY Post could not be read; the death is therefore not asserted in the account, which records the relapse reported by Medical Daily and GAMGEE. The WebSearch budget was exhausted, so sources were found through the Monitor's link list and Google and Bing news feeds.", "source": "https://oecd.ai/en/incidents/2026-03-15-f424", "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, what happened since from the cited sources.", "source": "https://stockhead.com.au/tech/tech-boss-uses-ai-and-chatgpt-to-create-cancer-vaccine-for-his-dying-dog/; https://finance.yahoo.com/healthcare/articles/one-dogs-legacy-becomes-gamgee-190000768.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" } ] } }, "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": "Veterinary oncology / personalized mRNA vaccine design", "segment": "Companion animal with mast cell cancer" } }, "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": "Melville, T. / UNSW Sydney (2026). Meet the man who designed a cancer vaccine for his dog. UNSW Newsroom, 17 March 2026.", "title": "Meet the man who designed a cancer vaccine for his dog", "url": "https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog", "date": "2026-03-17", "written_by": "deployer", "quote": "We don't know how much of the response came from the checkpoint inhibitor and how much from the mRNA vaccine." }, { "kind": "news report", "citation": "Ma, J. / Fortune (2026). A tech entrepreneur used AI to help create the first-ever bespoke cancer vaccine for a dog. Fortune, 15 March 2026.", "title": "A tech entrepreneur used AI to help create the first-ever bespoke cancer vaccine for a dog", "url": "https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/", "date": "2026-03-15", "written_by": "independent", "quote": "Rosie got her first injection of the cancer treatment this past December, then received a booster in February." }, { "kind": "news report", "citation": "Bita, N. / The Australian, republished by Stockhead (2026). Tech boss uses AI and ChatGPT to create cancer vaccine for his dying dog. Stockhead, 18 March 2026.", "title": "Tech boss uses AI and ChatGPT to create cancer vaccine for his dying dog", "url": "https://stockhead.com.au/tech/tech-boss-uses-ai-and-chatgpt-to-create-cancer-vaccine-for-his-dying-dog/", "date": "2026-03-18", "written_by": "independent", "quote": "Mr Conyngham drove 10 hours with Rosie for her first injection in December, followed by a booster shot last month and another due next week." }, { "kind": "other", "citation": "Stebbing, J. / The Conversation (2026). A man used AI to help make a cancer vaccine for his dog – an oncologist urges caution. The Conversation, 20 March 2026 (academic commentary).", "title": "A man used AI to help make a cancer vaccine for his dog – an oncologist urges caution", "url": "https://theconversation.com/a-man-used-ai-to-help-make-a-cancer-vaccine-for-his-dog-an-oncologist-urges-caution-278735", "date": "2026-03-20", "written_by": "independent", "quote": "several tumours shrank markedly, her overall tumour burden fell, and her energy and behaviour improved" }, { "kind": "news report", "citation": "Roberts, R. / The Scientist (2026). ChatGPT and AlphaFold Help Design Personalized Vaccine for Dog with Cancer. The Scientist, online 16 March 2026, June 2026 issue.", "title": "ChatGPT and AlphaFold Help Design Personalized Vaccine for Dog with Cancer", "url": "https://www.the-scientist.com/chatgpt-and-alphafold-help-design-personalized-vaccine-for-dog-with-cancer-74227", "date": "2026-03-16", "written_by": "independent", "quote": "it is difficult to disentangle which of the treatments had the greatest effect." }, { "kind": "news report", "citation": "AFP via Phys.org (2026). One man, his dog, and ChatGPT: Australia's AI vaccine saga. Phys.org, 30 March 2026.", "title": "One man, his dog, and ChatGPT: Australia's AI vaccine saga", "url": "https://phys.org/news/2026-03-dog-chatgpt-australia-ai-vaccine.html", "date": "2026-03-30", "written_by": "independent", "quote": "This was not a clinical trial by any means" }, { "kind": "news report", "citation": "Kim, S. / Newsweek (2026). Owner With No Medical Background Invents Cure for Dog's Terminal Cancer. Newsweek, 16 March 2026.", "title": "Owner With No Medical Background Invents Cure for Dog's Terminal Cancer", "url": "https://www.newsweek.com/owner-no-medical-background-invents-cure-dogs-terminal-cancer-11684882", "date": "2026-03-16", "written_by": "independent", "quote": "And, toward the end of January, she was jumping over a fence to chase a rabbit." }, { "kind": "news report", "citation": "Bailey, R. / Reason (2026). Man Successfully Designs mRNA Vaccine To Treat His Dog's Cancer. Reason, 19 March 2026.", "title": "Man Successfully Designs mRNA Vaccine To Treat His Dog's Cancer", "url": "https://reason.com/2026/03/19/man-successfully-designs-mrna-vaccine-to-treat-his-dogs-cancer/", "date": "2026-03-19", "written_by": "independent", "quote": "If we can do this for a dog, why aren't we rolling this out to all humans with cancer?" }, { "kind": "news report", "citation": "Davis, M. / ABC News (2026). AI designs cancer vaccine for dog but scientists says red tape a barrier for human care. ABC News, 22 June 2026.", "title": "AI designs cancer vaccine for dog but scientists says red tape a barrier for human care", "url": "https://www.abc.net.au/news/2026-06-22/australian-dog-cancer-treated-via-mrna-vaccine-and-ai/106692668", "date": "2026-06-22", "written_by": "independent", "quote": "AI did not cure Rosie's cancer, nor did it create or administer the vaccine" }, { "kind": "news report", "citation": "Palmer, A. / Medical Daily (2026). Rosie's Cancer Came Back After Her AI-Designed Vaccine, and Her Owner Just Raised $4 Million. Medical Daily, 18 August 2026.", "title": "Rosie's Cancer Came Back After Her AI-Designed Vaccine, and Her Owner Just Raised $4 Million", "url": "https://www.medicaldaily.com/rosie-dog-ai-mrna-cancer-vaccine-gamgee-launch-477500", "date": "2026-08-18", "written_by": "independent", "quote": "Rosie entered remission, but her cancer returned following a later surgery." }, { "kind": "press release", "citation": "GAMGEE via GlobeNewswire (2026). One Dog's Legacy Becomes GAMGEE. 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Fortune, 15 March 2026.", "title": "A tech entrepreneur used AI to help create the first-ever bespoke cancer vaccine for a dog", "url": "https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/", "date": "2026-03-15", "written_by": "independent", "quote": "Rosie got her first injection of the cancer treatment this past December, then received a booster in February." }, { "kind": "news report", "citation": "Bita, N. / The Australian, republished by Stockhead (2026). Tech boss uses AI and ChatGPT to create cancer vaccine for his dying dog. Stockhead, 18 March 2026.", "title": "Tech boss uses AI and ChatGPT to create cancer vaccine for his dying dog", "url": "https://stockhead.com.au/tech/tech-boss-uses-ai-and-chatgpt-to-create-cancer-vaccine-for-his-dying-dog/", "date": "2026-03-18", "written_by": "independent", "quote": "Mr Conyngham drove 10 hours with Rosie for her first injection in December, followed by a booster shot last month and another due next week." }, { "kind": "other", "citation": "Stebbing, J. / The Conversation (2026). A man used AI to help make a cancer vaccine for his dog – an oncologist urges caution. The Conversation, 20 March 2026 (academic commentary).", "title": "A man used AI to help make a cancer vaccine for his dog – an oncologist urges caution", "url": "https://theconversation.com/a-man-used-ai-to-help-make-a-cancer-vaccine-for-his-dog-an-oncologist-urges-caution-278735", "date": "2026-03-20", "written_by": "independent", "quote": "several tumours shrank markedly, her overall tumour burden fell, and her energy and behaviour improved" }, { "kind": "news report", "citation": "Roberts, R. / The Scientist (2026). ChatGPT and AlphaFold Help Design Personalized Vaccine for Dog with Cancer. The Scientist, online 16 March 2026, June 2026 issue.", "title": "ChatGPT and AlphaFold Help Design Personalized Vaccine for Dog with Cancer", "url": "https://www.the-scientist.com/chatgpt-and-alphafold-help-design-personalized-vaccine-for-dog-with-cancer-74227", "date": "2026-03-16", "written_by": "independent", "quote": "it is difficult to disentangle which of the treatments had the greatest effect." }, { "kind": "news report", "citation": "AFP via Phys.org (2026). One man, his dog, and ChatGPT: Australia's AI vaccine saga. Phys.org, 30 March 2026.", "title": "One man, his dog, and ChatGPT: Australia's AI vaccine saga", "url": "https://phys.org/news/2026-03-dog-chatgpt-australia-ai-vaccine.html", "date": "2026-03-30", "written_by": "independent", "quote": "This was not a clinical trial by any means" }, { "kind": "news report", "citation": "Kim, S. / Newsweek (2026). Owner With No Medical Background Invents Cure for Dog's Terminal Cancer. Newsweek, 16 March 2026.", "title": "Owner With No Medical Background Invents Cure for Dog's Terminal Cancer", "url": "https://www.newsweek.com/owner-no-medical-background-invents-cure-dogs-terminal-cancer-11684882", "date": "2026-03-16", "written_by": "independent", "quote": "And, toward the end of January, she was jumping over a fence to chase a rabbit." }, { "kind": "news report", "citation": "Bailey, R. / Reason (2026). Man Successfully Designs mRNA Vaccine To Treat His Dog's Cancer. Reason, 19 March 2026.", "title": "Man Successfully Designs mRNA Vaccine To Treat His Dog's Cancer", "url": "https://reason.com/2026/03/19/man-successfully-designs-mrna-vaccine-to-treat-his-dogs-cancer/", "date": "2026-03-19", "written_by": "independent", "quote": "If we can do this for a dog, why aren't we rolling this out to all humans with cancer?" }, { "kind": "news report", "citation": "Davis, M. / ABC News (2026). AI designs cancer vaccine for dog but scientists says red tape a barrier for human care. ABC News, 22 June 2026.", "title": "AI designs cancer vaccine for dog but scientists says red tape a barrier for human care", "url": "https://www.abc.net.au/news/2026-06-22/australian-dog-cancer-treated-via-mrna-vaccine-and-ai/106692668", "date": "2026-06-22", "written_by": "independent", "quote": "AI did not cure Rosie's cancer, nor did it create or administer the vaccine" }, { "kind": "news report", "citation": "Palmer, A. / Medical Daily (2026). Rosie's Cancer Came Back After Her AI-Designed Vaccine, and Her Owner Just Raised $4 Million. Medical Daily, 18 August 2026.", "title": "Rosie's Cancer Came Back After Her AI-Designed Vaccine, and Her Owner Just Raised $4 Million", "url": "https://www.medicaldaily.com/rosie-dog-ai-mrna-cancer-vaccine-gamgee-launch-477500", "date": "2026-08-18", "written_by": "independent", "quote": "Rosie entered remission, but her cancer returned following a later surgery." }, { "kind": "press release", "citation": "GAMGEE via GlobeNewswire (2026). One Dog's Legacy Becomes GAMGEE. 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Rosie is an eight-year-old Staffy-Shar Pei cross with mast cell cancer diagnosed in 2024. He used AlphaFold to model mutated proteins from tumor sequencing he paid $3,000 for at UNSW's Ramaciotti Centre for Genomics. He chose neoantigen targets and confirmed them “again using AI”. Pall Thordarson's UNSW RNA Institute made the mRNA vaccine in under two months. Rachel Allavena of the University of Queensland injected it at Gatton in December 2025, with a checkpoint inhibitor, and gave a booster in February 2026. The tennis-ball-sized tumor on Rosie's hock halved, and she was chasing rabbits by late January 2026. Some tumors did not respond. By August 2026 the cancer had returned after a later 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": "ChatGPT (OpenAI) and AlphaFold (Google DeepMind), with Grok (xAI) reported for the final mRNA construct, used with Conyngham's own analysis to select tumor neoantigen targets for a personalized mRNA vaccine" } }, "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 (ChatGPT); Google DeepMind (AlphaFold); xAI (Grok)" } }, "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": "Paul Conyngham uses ChatGPT and AlphaFold to choose targets for a personalized mRNA vaccine that UNSW made for his dog Rosie's mast cell cancer; her largest tumor halved", "headline": "ChatGPT and AlphaFold pick vaccine targets; dog's tumor halves" } }, "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": [ "1a21cd2cdf56ace508c40de6d3b87e3a17d0d57899f0cfcce9bb7c49722ccd36" ], "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": "Paul Conyngham, a Sydney machine-learning engineer, used ChatGPT to plan treatment for his dog Rosie. Rosie is an eight-year-old Staffy-Shar Pei cross with mast cell cancer diagnosed in 2024. He used AlphaFold to model mutated proteins from tumor sequencing he paid $3,000 for at UNSW's Ramaciotti Centre for Genomics. He chose neoantigen targets and confirmed them “again using AI”. Pall Thordarson's UNSW RNA Institute made the mRNA vaccine in under two months. Rachel Allavena of the University of Queensland injected it at Gatton in December 2025, with a checkpoint inhibitor, and gave a booster in February 2026. The tennis-ball-sized tumor on Rosie's hock halved, and she was chasing rabbits by late January 2026. Some tumors did not respond. By August 2026 the cancer had returned after a later surgery.", "replication": { "produced_by": [ "Whole-genome sequencing of tumor and healthy tissue, about 150 billion DNA letters for $3,000, from which neoantigen targets could be chosen with AI help.", "An academic RNA facility, the UNSW RNA Institute, willing to make a custom mRNA vaccine from an owner-designed sequence in under two months.", "A veterinary school with ethics cover and an immunotherapy specialist to administer it, after about three months of ethics paperwork.", "An owner with 17 years of machine-learning experience able to run the process, pay for it and drive ten hours for each dose." ], "must_hold": [ "The response cannot be credited to the vaccine alone. A checkpoint inhibitor was given at the same time, and UNSW says the split is unknown.", "One uncontrolled animal. Mast cell tumors behave unpredictably, and some of Rosie's tumors did not respond.", "The benefit was time, not cure. The cancer returned after a later surgery, and the company founded on the case wrote in August 2026 that her story may have ended." ], "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-0185", "case_url": "records/AIBD-0185.html", "reason": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Bengaluru, India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "record": { "id": "AIBD-0185", "title": "ChatGPT flags red flags on a fake pre-IPO adviser; 77-year-old in Bengaluru reports Rs 67.10 lakh fraud to police", "summary": "After withdrawals were refused, a 77-year-old Binnypet resident checked the firm's credentials with ChatGPT, which flagged complaints and inconsistencies; he reported the Rs 67.10 lakh fraud to Ben...", "date_observed": "2026-01-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The New Indian Express (6 January 2026) reported on a 77-year-old resident of Binnypet, Bengaluru. In November 2025 a man calling himself Yatin Shah contacted him. He claimed to be an adviser with IIFL Wealth Management and promised assured allotment of pre-IPO shares. The victim first transferred Rs 5 lakh for a supposed ICICI Prudential AMC pre-IPO and was shown profits in a digital wallet. Over the following weeks he made multiple RTGS transfers totaling Rs 67.10 lakh. When requests to withdraw even part of the funds were denied, he searched for details about the company and its representatives using ChatGPT. It reportedly flagged complaints, inconsistencies in company credentials and indicators associated with investment fraud. He then approached the police, who registered a case under the Information Technology Act. The police said that efforts were under way to trace the money. Deccan Herald (Prajwal D'Souza, 6 January 2026) framed the ChatGPT response as saving him from losing more money. 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No money was recovered in the reports read." }, { "group": "societal", "depth": null, "reason": "No outcome beyond the single complaint is documented." }, { "group": "organizational", "depth": null, "reason": "The police registered a case. No organizational outcome is documented." } ], "ai_role": { "status": "assessed", "statement": "Both reports attribute the discovery to ChatGPT's response flagging complaints and credential inconsistencies when the victim queried the firm. That came after his own suspicion at refused withdrawals. The exact output is not reproduced. 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external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0185-source-0001", "AIBD-0185-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "The New Indian Express, Bengaluru, 6 January 2026", "title": "ChatGPT alert helps Bengaluru senior citizen uncover Rs 67 lakh pre-IPO investment fraud", "url": "https://www.newindianexpress.com/cities/bengaluru/2026/Jan/06/chatgpt-alert-helps-bengaluru-senior-citizen-uncover-rs-67-lakh-pre-ipo-investment-fraud", "date": "2026-01-06", "written_by": "independent", "quote": "The fraud came to light after the victim checked the firm's credentials using ChatGPT, which flagged multiple warning signs." }, { "kind": "news report", "citation": "Prajwal D'Souza, Deccan Herald, 6 January 2026", "title": "Bengaluru: ChatGPT response saves 77-year-old man from losing more money to cybercriminals", "url": "https://www.deccanherald.com/india/karnataka/bengaluru/bengaluru-chatgpt-response-saves-77-year-old-man-from-losing-more-money-to-cybercriminals-3853142", "date": "2026-01-06", "written_by": "independent", "quote": "The scammers had already cheated him of Rs 67.10 lakh through a fake investment scam." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "After withdrawals were refused, a 77-year-old Binnypet resident checked the firm's credentials with ChatGPT, which flagged complaints and inconsistencies; he reported the Rs 67.10 lakh fraud to Ben...", "account": "The New Indian Express (6 January 2026) reported on a 77-year-old resident of Binnypet, Bengaluru. In November 2025 a man calling himself Yatin Shah contacted him. He claimed to be an adviser with IIFL Wealth Management and promised assured allotment of pre-IPO shares. The victim first transferred Rs 5 lakh for a supposed ICICI Prudential AMC pre-IPO and was shown profits in a digital wallet. Over the following weeks he made multiple RTGS transfers totaling Rs 67.10 lakh. When requests to withdraw even part of the funds were denied, he searched for details about the company and its representatives using ChatGPT. It reportedly flagged complaints, inconsistencies in company credentials and indicators associated with investment fraud. He then approached the police, who registered a case under the Information Technology Act. The police said that efforts were under way to trace the money. Deccan Herald (Prajwal D'Souza, 6 January 2026) framed the ChatGPT response as saving him from losing more money. No police official is named, no recovery is reported and the victim is unnamed." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ChatGPT" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ChatGPT flags red flags on a fake pre-IPO adviser; 77-year-old in Bengaluru reports Rs 67.10 lakh fraud to police", "headline": "ChatGPT flags fake adviser; victim reports Rs 67.10 lakh fraud" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f6c0a087d5b24db8a8b779d822e10293d456ab2962ad17de12e5fcfedd1835c7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The New Indian Express (6 January 2026) reported on a 77-year-old resident of Binnypet, Bengaluru. In November 2025 a man calling himself Yatin Shah contacted him. He claimed to be an adviser with IIFL Wealth Management and promised assured allotment of pre-IPO shares. The victim first transferred Rs 5 lakh for a supposed ICICI Prudential AMC pre-IPO and was shown profits in a digital wallet. Over the following weeks he made multiple RTGS transfers totaling Rs 67.10 lakh. When requests to withdraw even part of the funds were denied, he searched for details about the company and its representatives using ChatGPT. It reportedly flagged complaints, inconsistencies in company credentials and indicators associated with investment fraud. He then approached the police, who registered a case under the Information Technology Act. The police said that efforts were under way to trace the money. Deccan Herald (Prajwal D'Souza, 6 January 2026) framed the ChatGPT response as saving him from losing more money. No police official is named, no recovery is reported and the victim is unnamed.", "replication": { "produced_by": [ "A victim querying a large language model about a company and its representatives after withdrawals were refused", "Public complaints and credential inconsistencies about the scheme that the model could surface" ], "must_hold": [ "Suspicion arising before further payments", "Model output that points to specific red flags rather than generic caution", "Police registration for any tracing or recovery" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0153", "case_url": "records/AIBD-0153.html", "reason": "The developer is headquartered in Switzerland; the case names United States.", "details": [ { "label": "Developer headquarters", "value": "Switzerland" }, { "label": "Case jurisdictions", "value": "United States" }, { "label": "Case location", "value": "Broomfield, United States" } ], "filters": { "origin": "Switzerland", "destination": [ "United States" ] }, "record": { "id": "AIBD-0153", "title": "Garmin Autoland engages after a King Air B200 loses cabin pressure at 23,000 ft and lands it at Broomfield, Colorado, in its first real emergency use", "summary": "On 20 December 2025 Garmin Autoland flew a depressurised King Air B200 to a landing at Rocky Mountain Metropolitan Airport; two pilots unhurt.", "date_observed": "2025-12-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Saturday 20 December 2025 Buffalo River Aviation's Beechcraft Super King Air B200, N479BR, left Aspen-Pitkin County Airport at 1:43 p.m. It carried two pilots and no passengers. At about 23,000 ft it suffered a rapid, uncommanded loss of pressurization. When cabin altitude passed the safe limit, Garmin Autoland engaged automatically. It sent automated emergency broadcasts to air traffic control. It flew the aircraft to a landing on Runway 30 at Rocky Mountain Metropolitan Airport, Broomfield, Colorado, shortly after 2 p.m. The crew chose to leave it engaged. Nobody was treated. The Federal Aviation Administration (FAA) said it was investigating and the NTSB that it was gathering information. Garmin called it the first use of Autoland from start to finish in an actual emergency.", "category": "protective response", "unexpected": false, "purpose": "Autoland exists to take complete control of an aircraft and land it when the pilot cannot fly. It engages on its own when cabin altitude exceeds a set level. That is what happened here. The two pilots turned out not to be incapacitated, though, and chose to let it finish.", "system": "Garmin Autoland (Emergency Autoland)", "model_version": null, "developer": "Garmin", "deployer": "Buffalo River Aviation", "location": "Broomfield, United States", "domain": "Aviation safety / automatic emergency landing", "segment": "Charter turboprop operations", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-12-22-41ce" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. No damage is reported, and the FAA investigation was still open." } ], "ai_role": { "status": "assessed", "statement": "Autoland engaged itself when cabin altitude exceeded the limit. It declared the emergency to controllers with automated “pilot incapacitation” broadcasts. It flew the descent, approach and landing. The pilots elected to leave it engaged rather than take over. So the landing is the system's work. But the counterfactual, that they could not have landed, is not established. Garmin, the FAA and the operator all call it an automated system. None calls it AI. It is here because the OECD Monitor lists it as an AI system. The Register describes it choosing landing sites from runway length, distance and fuel range." }, "evidence": [ { "kind": "news report", "citation": "Sarah Horbacewicz / CBS News Colorado (2025). Plane makes safe emergency landing in Colorado without a pilot's help, first 'Autoland' use. CBS News, 22 December 2025.", "title": "Plane makes safe emergency landing in Colorado without a pilot's help, first 'Autoland' use", "url": "https://www.cbsnews.com/colorado/news/plane-emergency-landing-colorado-autoland/", "date": "2025-12-22", "written_by": "independent", "quote": "An onboard emergency autoland system was activated. Two people were on board. The FAA is investigating." }, { "kind": "news report", "citation": "Sam Sweeney and Ayesha Ali / ABC News (2025). Small plane lands safely with Autoland system after pilot is incapacitated. ABC News, 22 December 2025.", "title": "Small plane lands safely with Autoland system after pilot is incapacitated", "url": "https://abcnews.com/US/small-plane-lands-safely-autoland-system-after-pilot/story?id=128629603", "date": "2025-12-22", "written_by": "independent", "quote": "first use of Autoland from start-to-finish in an actual emergency" }, { "kind": "news report", "citation": "Jude Karabus / The Register (2025). Garmin autopilot lands small plane without pilot's help. The Register, 24 December 2025.", "title": "Garmin autopilot lands small plane without pilot's help", "url": "https://www.theregister.com/2025/12/24/autocomms/", "date": "2025-12-24", "written_by": "independent", "quote": "automatically engaged exactly as designed when the cabin altitude exceeded the prescribed safe levels" }, { "kind": "news report", "citation": "Katie Parkins and Óscar Contreras / Denver7 (2025). In a first, plane makes emergency landing at RMMA using automated technology due to 'pilot incapacitation'. Denver7 (KMGH), 22 December 2025.", "title": "In a first, plane makes emergency landing at RMMA using automated technology due to 'pilot incapacitation'", "url": "https://www.denver7.com/news/front-range/broomfield/in-a-first-plane-makes-emergency-landing-at-rmma-using-automated-technology-due-to-pilot-incapacitation", "date": "2025-12-22", "written_by": "independent", "quote": "no one on the plane was treated on scene or taken to a hospital" }, { "kind": "news report", "citation": "Matt Ryan / AVweb (2025). Garmin Autoland Safely Lands King Air After Pilot Incapacitation. AVweb, 22 December 2025.", "title": "Garmin Autoland Safely Lands King Air After Pilot Incapacitation", "url": "https://avweb.com/aviation-news/garmin-autoland-safely-lands-king-air/", "date": "2025-12-22", "written_by": "independent", "quote": "transmitting automated broadcasts indicating a pilot incapacitation and intent to conduct an emergency autoland" }, { "kind": "news report", "citation": "Rachel Dobkin / The Independent (2025). Airplane automatically lands itself after an inflight emergency. The Independent, syndicated on Yahoo News, 24 December 2025.", "title": "Airplane automatically lands itself after an inflight emergency", "url": "https://www.yahoo.com/news/articles/airplane-automatically-lands-itself-inflight-044612392.html", "date": "2025-12-24", "written_by": "independent", "quote": "No patients were treated on scene or transported to local hospitals." } ], "replication": { "produced_by": [ "An aircraft fitted with Garmin Autoland. Garmin says more than 1,700 are equipped.", "A rapid, uncommanded loss of pressurization that pushed cabin altitude past the system's automatic trigger.", "A crew that decided to let the automatic sequence run to touchdown rather than take over." ], "must_hold": [ "The aircraft remains airworthy. Autoland is a fail-safe for a working aircraft whose pilot cannot fly it, in the words of the journalist CBS quoted.", "A suitable airport is in range. The Register describes selection by runway length, distance and fuel range.", "Controllers and ground services act on the automated broadcasts, as Broomfield's two fire departments did." ], "echoes": [] }, "paired_incident": null, "headline": "Garmin Autoland lands depressurised King Air B200; pilots unhurt", "metric": "two pilots unhurt", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The FAA said it was investigating and the NTSB that it was collecting information to decide whether the event met its criteria; no FAA or NTSB report or other development after December 2025 was found. The aircraft flew on to Oklahoma City the next morning.", "quote": null, "url": null }, "value": null, "developer_country": "Switzerland", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and six news reports. The Monitor dates the event 22 December 2025; every report puts the landing on Saturday 20 December, which is used here. Early reports (AVweb, Denver7, ABC's headline) said the pilot was incapacitated; the operator's CEO, quoted by CBS, ABC and The Register, says the system engaged on cabin-altitude exceedance and the crew were not incapacitated. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented because the crew were conscious and elected to leave the system engaged, so no counterfactual is established. Aircraft type is given as Super King Air 200 (ABC, Denver7) and King Air B200 (AVweb, The Register). The Monitor's General Aviation News and FOX31 follow-up links returned HTTP 403, AVweb's “crew decision” piece returned 404, and the Independent blocked fetching, so its Yahoo syndication is cited. Garmin's Autonomí pages returned only navigation, so no vendor source is cited; Garmin's statements are quoted via the news reports.", "source": "https://oecd.ai/en/incidents/2025-12-22-41ce", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Garmin; https://buffaloriveraviation.com/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0153", "catalog_version": "1.0.0", "source_fingerprint": "e067d3612528a25b90b00340bad1e609571c414ab9142cd0bea9cf9db371b9b5", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0153-source-0001", "AIBD-0153-source-0002", "AIBD-0153-source-0003", "AIBD-0153-source-0004", "AIBD-0153-source-0005", "AIBD-0153-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Garmin", "deployer": "Buffalo River Aviation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Autoland engaged itself when cabin altitude exceeded the limit. It declared the emergency to controllers with automated “pilot incapacitation” broadcasts. It flew the descent, approach and landing. The pilots elected to leave it engaged rather than take over. So the landing is the system's work. But the counterfactual, that they could not have landed, is not established. Garmin, the FAA and the operator all call it an automated system. None calls it AI. It is here because the OECD Monitor lists it as an AI system. The Register describes it choosing landing sites from runway length, distance and fuel range." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "Autoland engaged itself when cabin altitude exceeded the limit. It declared the emergency to controllers with automated “pilot incapacitation” broadcasts. It flew the descent, approach and landing. The pilots elected to leave it engaged rather than take over. So the landing is the system's work. But the counterfactual, that they could not have landed, is not established. Garmin, the FAA and the operator all call it an automated system. None calls it AI. It is here because the OECD Monitor lists it as an AI system. The Register describes it choosing landing sites from runway length, distance and fuel range." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. No damage is reported, and the FAA investigation was still open." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: six independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. No damage is reported, and the FAA investigation was still open." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. 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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": "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-0342", "case_url": "records/AIBD-0342.html", "reason": "The developer is headquartered in France; the case names Kazakhstan.", "details": [ { "label": "Developer headquarters", "value": "France" }, { "label": "Case jurisdictions", "value": "Kazakhstan" }, { "label": "Case location", "value": "Kazakhstan, 43.39767°, 52.49505° (no place name in the UNEP document)" } ], "filters": { "origin": "France", "destination": [ "Kazakhstan" ] }, "record": { "id": "AIBD-0342", "title": "UNEP MARS notification leads an operator to stop a 1,200-tonne methane pipeline leak in Kazakhstan (KAZ_S_136); IMEO observed emissions at the site again by May 2026", "summary": "UNEP IMEO case KAZ_S_136: pipeline leak first detected Sept 2025, operator rerouted gas, mitigation reported 26 Nov 2025, last emissions 12 Mar 2026; subsequent emissions seen as of 21 May 2026.", "date_observed": "2025-11-26", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "UNEP's International Methane Emissions Observatory (IMEO) publishes a Methane Alert and Response System (MARS) case document for source KAZ_S_136. It is an oil-and-gas pipeline in Kazakhstan at 43.39767°, 52.49505°. The document gives coordinates only. The document records first detection in September 2025, a plume imaged by DLR's EnMAP on 29 October 2025 and estimated emissions of 1,200 tonnes. The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions. Mitigation was reported on 26 November 2025. Last emissions were detected on 12 March 2026 (document updated 15 April 2026). A footnote states that as of 21 May 2026 IMEO observed subsequent methane emissions at the location and continues to engage stakeholders. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments coupled with scientific expertise and advanced AI models. It uses them to notify governments and companies. The Chemical Engineer (5 May 2026), relaying UNEP's snapshot, counts one Kazakhstan source among six that stopped emitting in 2026 after MARS alerts. UNEP's September 2026 snapshot reports a confirmed Kazakhstan gas-pipeline mitigation of an estimated 365 tonnes over one month. Neither gives a case ID, so their correspondence to KAZ_S_136 is not established.", "category": "benefit created", "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators and governments mitigate them. This case was that purpose.", "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation", "model_version": null, "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed pipeline operator in Kazakhstan (mitigation)", "location": "Kazakhstan, 43.39767°, 52.49505° (no place name in the UNEP document)", "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas transmission; intergovernmental observatory", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ], "ai_role": { "status": "assessed", "statement": "UNEP's case document records detection, notification, the operator's rerouting of gas and satellite confirmation. It names DLR's EnMAP for the plume image. The only description of an AI role is UNEP's general statement that IMEO analyzes satellite data with advanced AI models. The AI contribution to this specific detection is therefore not isolated. The sources are UNEP's own (system operator) plus trade press relaying UNEP. No operator or government statement is among the sources." }, "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2026). Methane Alert and Response System (MARS) case document, Kazakhstan KAZ_S_136 (pipeline), updated 15 Apr 2026. wedocs.unep.org.", "title": "MARS case KAZ_S_136, Kazakhstan", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, footnote on subsequent emissions.", "title": "MARS case KAZ_S_136, Kazakhstan (subsequent emissions footnote)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "As of 21 May 2026, IMEO observed subsequent methane emissions at this location." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, image caption naming the detecting instrument.", "title": "MARS case KAZ_S_136, Kazakhstan (detection)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The image shows a plume detected by DLR's EnMAP in Kazakhstan on 29 October 2025." }, { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "one each in Libya, Iraq and Kazakhstan" }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (the snapshot gives no case ID for the Kazakhstan mitigation).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "date": null, "written_by": "makers", "quote": "A gas pipeline leak resulted in the release of an estimated 365 tonnes of methane over the course of one month." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "date": null, "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ], "replication": { "produced_by": [ "Satellite plume detection (EnMAP image of 29 October 2025) analyzed by IMEO", "A MARS notification reaching an operator able to reroute gas flow", "Follow-up satellite observation to confirm cessation" ], "must_hold": [ "A country focal point or operator that answers notifications (UNEP's snapshot says most mitigation occurs where focal points direct information)", "A source type that can be isolated quickly (pipeline rerouting)", "Sustained repair: at this site emissions were observed again by 21 May 2026" ], "echoes": [ { "date": "2026-05-21", "setting": "Same site KAZ_S_136: IMEO observed subsequent methane emissions after the confirmed mitigation", "model_version": null, "outcome": "did not recur", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (DZA_S_012): MARS notification followed by repair of a decades-old source (existing record AIBD-0091)", "model_version": null, "outcome": "recurred", "source": "AIBD-0091" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection, per trade press relaying UNEP's August 2026 snapshot", "model_version": null, "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] }, "paired_incident": null, "headline": "UNEP MARS alert stops Kazakhstan leak; emissions seen again", "metric": "estimated emissions of 1,200 tonnes", "comparator": null, "later": null, "value": null, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, the makers' case report, the makers' report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0342", "catalog_version": "1.0.0", "source_fingerprint": "a32e17efc837fdd8167a283df56b125c7814e01b21df66f2b2a10f3d8cba07fc", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0342-source-0001", "AIBD-0342-source-0002", "AIBD-0342-source-0003", "AIBD-0342-source-0004", "AIBD-0342-source-0005", "AIBD-0342-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed pipeline operator in Kazakhstan (mitigation)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "UNEP's case document records detection, notification, the operator's rerouting of gas and satellite confirmation. It names DLR's EnMAP for the plume image. The only description of an AI role is UNEP's general statement that IMEO analyzes satellite data with advanced AI models. The AI contribution to this specific detection is therefore not isolated. The sources are UNEP's own (system operator) plus trade press relaying UNEP. No operator or government statement is among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "UNEP's case document records detection, notification, the operator's rerouting of gas and satellite confirmation. It names DLR's EnMAP for the plume image. The only description of an AI role is UNEP's general statement that IMEO analyzes satellite data with advanced AI models. The AI contribution to this specific detection is therefore not isolated. The sources are UNEP's own (system operator) plus trade press relaying UNEP. No operator or government statement is among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report, the makers' case report, the makers' report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "estimated emissions of 1,200 tonnes", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0342" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNEP's International Methane Emissions Observatory (IMEO) publishes a Methane Alert and Response System (MARS) case document for source KAZ_S_136. It is an oil-and-gas pipeline in Kazakhstan at 43.39767°, 52.49505°. The document gives coordinates only. The document records first detection in September 2025, a plume imaged by DLR's EnMAP on 29 October 2025 and estimated emissions of 1,200 tonnes. The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions. Mitigation was reported on 26 November 2025. Last emissions were detected on 12 March 2026 (document updated 15 April 2026). A footnote states that as of 21 May 2026 IMEO observed subsequent methane emissions at the location and continues to engage stakeholders. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments coupled with scientific expertise and advanced AI models. It uses them to notify governments and companies. The Chemical Engineer (5 May 2026), relaying UNEP's snapshot, counts one Kazakhstan source among six that stopped emitting in 2026 after MARS alerts. UNEP's September 2026 snapshot reports a confirmed Kazakhstan gas-pipeline mitigation of an estimated 365 tonnes over one month. Neither gives a case ID, so their correspondence to KAZ_S_136 is not established.", "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators and governments mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Kazakhstan, 43.39767°, 52.49505° (no place name in the UNEP document)", "developer_country": "France", "deployer_country": "France" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Satellite-based detection and notification of large methane plumes so that operators and governments mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-11-26", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite plume detection (EnMAP image of 29 October 2025) analyzed by IMEO", "A MARS notification reaching an operator able to reroute gas flow", "Follow-up satellite observation to confirm cessation" ], "must_hold": [ "A country focal point or operator that answers notifications (UNEP's snapshot says most mitigation occurs where focal points direct information)", "A source type that can be isolated quickly (pipeline rerouting)", "Sustained repair: at this site emissions were observed again by 21 May 2026" ], "echoes": [ { "date": "2026-05-21", "setting": "Same site KAZ_S_136: IMEO observed subsequent methane emissions after the confirmed mitigation", "outcome": "did not recur", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (DZA_S_012): MARS notification followed by repair of a decades-old source (existing record AIBD-0091)", "outcome": "recurred", "source": "AIBD-0091" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection, per trade press relaying UNEP's August 2026 snapshot", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite plume detection (EnMAP image of 29 October 2025) analyzed by IMEO", "A MARS notification reaching an operator able to reroute gas flow", "Follow-up satellite observation to confirm cessation" ], "must_hold": [ "A country focal point or operator that answers notifications (UNEP's snapshot says most mitigation occurs where focal points direct information)", "A source type that can be isolated quickly (pipeline rerouting)", "Sustained repair: at this site emissions were observed again by 21 May 2026" ], "echoes": [ { "date": "2026-05-21", "setting": "Same site KAZ_S_136: IMEO observed subsequent methane emissions after the confirmed mitigation", "outcome": "did not recur", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (DZA_S_012): MARS notification followed by repair of a decades-old source (existing record AIBD-0091)", "outcome": "recurred", "source": "AIBD-0091" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection, per trade press relaying UNEP's August 2026 snapshot", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "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 Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas transmission; intergovernmental observatory" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2026). Methane Alert and Response System (MARS) case document, Kazakhstan KAZ_S_136 (pipeline), updated 15 Apr 2026. wedocs.unep.org.", "title": "MARS case KAZ_S_136, Kazakhstan", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, footnote on subsequent emissions.", "title": "MARS case KAZ_S_136, Kazakhstan (subsequent emissions footnote)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "As of 21 May 2026, IMEO observed subsequent methane emissions at this location." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, image caption naming the detecting instrument.", "title": "MARS case KAZ_S_136, Kazakhstan (detection)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The image shows a plume detected by DLR's EnMAP in Kazakhstan on 29 October 2025." }, { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "one each in Libya, Iraq and Kazakhstan" }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (the snapshot gives no case ID for the Kazakhstan mitigation).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "written_by": "makers", "quote": "A gas pipeline leak resulted in the release of an estimated 365 tonnes of methane over the course of one month." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0342-source-0001", "AIBD-0342-source-0002", "AIBD-0342-source-0003", "AIBD-0342-source-0004", "AIBD-0342-source-0005", "AIBD-0342-source-0006" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2026). Methane Alert and Response System (MARS) case document, Kazakhstan KAZ_S_136 (pipeline), updated 15 Apr 2026. wedocs.unep.org.", "title": "MARS case KAZ_S_136, Kazakhstan", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, footnote on subsequent emissions.", "title": "MARS case KAZ_S_136, Kazakhstan (subsequent emissions footnote)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "As of 21 May 2026, IMEO observed subsequent methane emissions at this location." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, image caption naming the detecting instrument.", "title": "MARS case KAZ_S_136, Kazakhstan (detection)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The image shows a plume detected by DLR's EnMAP in Kazakhstan on 29 October 2025." }, { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "one each in Libya, Iraq and Kazakhstan" }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (the snapshot gives no case ID for the Kazakhstan mitigation).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "written_by": "makers", "quote": "A gas pipeline leak resulted in the release of an estimated 365 tonnes of methane over the course of one month." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "UNEP IMEO case KAZ_S_136: pipeline leak first detected Sept 2025, operator rerouted gas, mitigation reported 26 Nov 2025, last emissions 12 Mar 2026; subsequent emissions seen as of 21 May 2026.", "account": "UNEP's International Methane Emissions Observatory (IMEO) publishes a Methane Alert and Response System (MARS) case document for source KAZ_S_136. It is an oil-and-gas pipeline in Kazakhstan at 43.39767°, 52.49505°. The document gives coordinates only. The document records first detection in September 2025, a plume imaged by DLR's EnMAP on 29 October 2025 and estimated emissions of 1,200 tonnes. The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions. Mitigation was reported on 26 November 2025. Last emissions were detected on 12 March 2026 (document updated 15 April 2026). A footnote states that as of 21 May 2026 IMEO observed subsequent methane emissions at the location and continues to engage stakeholders. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments coupled with scientific expertise and advanced AI models. It uses them to notify governments and companies. The Chemical Engineer (5 May 2026), relaying UNEP's snapshot, counts one Kazakhstan source among six that stopped emitting in 2026 after MARS alerts. UNEP's September 2026 snapshot reports a confirmed Kazakhstan gas-pipeline mitigation of an estimated 365 tonnes over one month. Neither gives a case ID, so their correspondence to KAZ_S_136 is not established." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UNEP MARS notification leads an operator to stop a 1,200-tonne methane pipeline leak in Kazakhstan (KAZ_S_136); IMEO observed emissions at the site again by May 2026", "headline": "UNEP MARS alert stops Kazakhstan leak; emissions seen again" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "a32e17efc837fdd8167a283df56b125c7814e01b21df66f2b2a10f3d8cba07fc" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNEP's International Methane Emissions Observatory (IMEO) publishes a Methane Alert and Response System (MARS) case document for source KAZ_S_136. It is an oil-and-gas pipeline in Kazakhstan at 43.39767°, 52.49505°. The document gives coordinates only. The document records first detection in September 2025, a plume imaged by DLR's EnMAP on 29 October 2025 and estimated emissions of 1,200 tonnes. The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions. Mitigation was reported on 26 November 2025. Last emissions were detected on 12 March 2026 (document updated 15 April 2026). A footnote states that as of 21 May 2026 IMEO observed subsequent methane emissions at the location and continues to engage stakeholders. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments coupled with scientific expertise and advanced AI models. It uses them to notify governments and companies. The Chemical Engineer (5 May 2026), relaying UNEP's snapshot, counts one Kazakhstan source among six that stopped emitting in 2026 after MARS alerts. UNEP's September 2026 snapshot reports a confirmed Kazakhstan gas-pipeline mitigation of an estimated 365 tonnes over one month. Neither gives a case ID, so their correspondence to KAZ_S_136 is not established.", "replication": { "produced_by": [ "Satellite plume detection (EnMAP image of 29 October 2025) analyzed by IMEO", "A MARS notification reaching an operator able to reroute gas flow", "Follow-up satellite observation to confirm cessation" ], "must_hold": [ "A country focal point or operator that answers notifications (UNEP's snapshot says most mitigation occurs where focal points direct information)", "A source type that can be isolated quickly (pipeline rerouting)", "Sustained repair: at this site emissions were observed again by 21 May 2026" ], "echoes": [ { "date": "2026-05-21", "setting": "Same site KAZ_S_136: IMEO observed subsequent methane emissions after the confirmed mitigation", "outcome": "did not recur", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (DZA_S_012): MARS notification followed by repair of a decades-old source (existing record AIBD-0091)", "outcome": "recurred", "source": "AIBD-0091" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection, per trade press relaying UNEP's August 2026 snapshot", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0148", "case_url": "records/AIBD-0148.html", "reason": "The developer is headquartered in France; the case names Hungary.", "details": [ { "label": "Developer headquarters", "value": "France" }, { "label": "Case jurisdictions", "value": "Hungary" }, { "label": "Case location", "value": "Szeged, Hungary" } ], "filters": { "origin": "France", "destination": [ "Hungary" ] }, "record": { "id": "AIBD-0148", "title": "Veesion gesture-recognition cameras in Coop Szeged's 247 Shop on Szent István tér, unstaffed at night, may have flagged a shopper hiding goods under clothing and led police to the perpetrator within hours, one of three attempts exposed since May 2025", "summary": "AI cameras in Szeged's unstaffed-at-night Coop flagged a shopper hiding goods; police caught them within hours, the operators said.", "date_observed": "2025-10-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Coop Szeged Zrt. and Laurel Kft. opened the 247 Shop on Szent István tér in Szeged at the end of May 2025. It is staffed by day, automated and unstaffed outside normal hours. Cameras running the French company Veesion's software analyze shoppers' movements without identifying faces. They alert a remote dispatch service to gestures such as concealing goods. On 9 October 2025 the operators issued a press release relayed by Index.hu and MTI. It said over four and a half months the system had exposed three theft attempts. In one, a person hid products under their clothing during night mode. Police caught the perpetrator within hours using the footage. Dates, recovered goods and any police statement are not reported.", "category": "protective response", "unexpected": false, "purpose": "Veesion's gesture-recognition software is built to spot shoplifting movements such as concealing goods and to raise an immediate alert. In Laurel's 247 Shop it is the loss-prevention layer for unstaffed operation. Flagging a concealment was its intended use.", "system": "Veesion AI gesture-recognition camera analysis integrated into Laurel Kft.'s 247 Shop hybrid-store platform, with alerts to a remote dispatch service", "model_version": "Not reported", "developer": "Veesion", "deployer": "Coop Szeged Zrt. (store operator), with Laurel Kft. as technology partner", "location": "Szeged, Hungary", "domain": "Retail loss prevention", "segment": "Grocery retail", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-10-09-70ff" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ], "ai_role": { "status": "unassessed", "statement": "By the operators' account, the Veesion analysis flagged the shopper's concealment gesture in real time and alerted the remote dispatcher. The camera footage let police catch the perpetrator within hours. No police statement, loss figure or description of what the dispatcher did at the moment of the alert was found. Whether the theft was stopped in progress or only documented, and whether the goods came back, is unknown. The claim rests on a joint press release by the deployer and its integrator, relayed by independent outlets." }, "evidence": [ { "kind": "news report", "citation": "Simon B K (2025). Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni. Index.hu, 9 October 2025, 20:39; the article says its facts are 'olvasható a sajtóközleményben', i.e. taken from the companies' press release.", "title": "Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni", "url": "https://index.hu/gazdasag/2025/10/09/magyar-okosbolt-szeged-ai-lopas-lebuktatas/", "date": "2025-10-09", "written_by": "independent", "quote": "a rendőrség a kamerafelvételek alapján néhány órán belül elfogta az elkövetőt" }, { "kind": "news report", "citation": "Mínuszos.hu (2025). AI leplezett le tolvajokat a szegedi hibrid okosboltban. Mínuszos.hu, 11 October 2025; names Veesion as a French company and the store as the Szent István tér Coop opened at the end of May.", "title": "AI leplezett le tolvajokat a szegedi hibrid okosboltban", "url": "https://www.minuszos.hu/ai-leplezett-le-tolvajokat-a-szegedi-hibrid-okosboltban/", "date": "2025-10-11", "written_by": "independent", "quote": "A mesterséges intelligenciával (AI) támogatott rendszer az elmúlt hónapokban három lopási kísérletet is sikeresen azonosított és akadályozott meg." }, { "kind": "news report", "citation": "ProfitLine.hu / MTI (2025). Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt. ProfitLine.hu, 11 October 2025, carrying the MTI agency report of the companies' statement; gives 150 square metres, 4,000 SKUs, and quotes István Bessenyei (Laurel) and János Kelemen (Coop Szeged).", "title": "Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt.", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176", "date": "2025-10-11", "written_by": "independent", "quote": "az arcok azonosítása nélkül elemzi a vásárlói mozgásokat, és azonnal jelzi a távoli diszpécserszolgálatnak a gyanús viselkedést" }, { "kind": "news report", "citation": "Turizmus Online / MTI (2025). A szegediek szeretik az okosboltokat – újabbakat nyitnak. Turizmus Online, 13 October 2025; reports 8,500 purchases in four and a half months and no rise in shrinkage despite the longer hours, per the operators.", "title": "A szegediek szeretik az okosboltokat – újabbakat nyitnak", "url": "https://turizmusonline.hu/cikkek/gasztro/a-szegediek-szeretik-az-okosboltokat--ujabbakat-nyitnak.html", "date": "2025-10-13", "written_by": "independent", "quote": "az új típusú bolt az indulás óta stabilan, a várakozások felett teljesít" }, { "kind": "news report", "citation": "Economx (2026). Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket. economx.hu, 30 September 2026.", "title": "Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "date": "2026-09-30", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Gesture-recognition analysis watching every shopper during the hours when no staff are present, tuned to concealment movements rather than faces", "A remote dispatch service that receives the alert immediately, and camera footage that can be handed to police", "A sales floor small enough (150 square meters, 4,000 SKUs) for the cameras to cover it entirely" ], "must_hold": [ "The flagged gesture is a real theft rather than an innocent movement. The operators report no false-alarm figures", "Police are willing and able to act on the footage quickly, here within hours", "The system keeps analyzing movements without identifying faces, as the operators describe it" ], "echoes": [] }, "paired_incident": null, "headline": "Veesion alert may have led police to a Szeged shoplifter", "metric": "over four and a half months the system had exposed three theft attempts", "comparator": { "against": "inventory shrinkage under the store's previous staffed-only opening hours", "before": null, "after": "nem nőtt meg a leltárhiány", "basis": "measured", "quote": "Tájékoztatásuk szerint a két cég együttműködésével elindult szegedi üzletben négy és fél hónap alatt már 8500-an vásároltak az éjszakai, automata rendszerben, és az egész naposra kiterjesztett nyitvatartással járó jelentős forgalomtöbblet dacára nem nőtt meg a leltárhiány.", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176" }, "later": { "status": "scaled", "date": "2026-09-30", "note": "Coop Szeged and Laurel opened a second staffless-after-hours store, a full supermarket on Temesvári körút, on 30 September 2026, after more than 27,000 unstaffed purchases in a year at the first store; the report describes camera monitoring and a remote dispatch service but does not name Veesion.", "quote": "most pedig ugyanebben a városban, a Temesvári körúton nyílt meg a második, ami immáron a méreteit tekintve egy kész szupermarket.", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/" }, "value": null, "developer_country": "France", "deployer_country": "Hungary", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-09-70ff", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176; https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://veesion.io/mentions-legales/; https://www.coopszeged.hu/kapcsolat", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0148", "catalog_version": "1.0.0", "source_fingerprint": "426c9b02e8e63eb5fd423b6a1cf78ec516aaabb2e070563d9f80bd9468b3faa2", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0148-source-0001", "AIBD-0148-source-0002", "AIBD-0148-source-0003", "AIBD-0148-source-0004", "AIBD-0148-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Veesion", "deployer": "Coop Szeged Zrt. (store operator), with Laurel Kft. as technology partner" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "unassessed", "statement": "By the operators' account, the Veesion analysis flagged the shopper's concealment gesture in real time and alerted the remote dispatcher. The camera footage let police catch the perpetrator within hours. No police statement, loss figure or description of what the dispatcher did at the moment of the alert was found. Whether the theft was stopped in progress or only documented, and whether the goods came back, is unknown. The claim rests on a joint press release by the deployer and its integrator, relayed by independent outlets." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "over four and a half months the system had exposed three theft attempts", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-10-09-70ff" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-09-30", "note": "Coop Szeged and Laurel opened a second staffless-after-hours store, a full supermarket on Temesvári körút, on 30 September 2026, after more than 27,000 unstaffed purchases in a year at the first store; the report describes camera monitoring and a remote dispatch service but does not name Veesion.", "quote": "most pedig ugyanebben a városban, a Temesvári körúton nyílt meg a második, ami immáron a méreteit tekintve egy kész szupermarket.", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0148" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Coop Szeged Zrt. and Laurel Kft. opened the 247 Shop on Szent István tér in Szeged at the end of May 2025. It is staffed by day, automated and unstaffed outside normal hours. Cameras running the French company Veesion's software analyze shoppers' movements without identifying faces. They alert a remote dispatch service to gestures such as concealing goods. On 9 October 2025 the operators issued a press release relayed by Index.hu and MTI. It said over four and a half months the system had exposed three theft attempts. In one, a person hid products under their clothing during night mode. Police caught the perpetrator within hours using the footage. Dates, recovered goods and any police statement are not reported.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Veesion's gesture-recognition software is built to spot shoplifting movements such as concealing goods and to raise an immediate alert. In Laurel's 247 Shop it is the loss-prevention layer for unstaffed operation. Flagging a concealment was its intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Szeged, Hungary", "developer_country": "France", "deployer_country": "Hungary" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Veesion's gesture-recognition software is built to spot shoplifting movements such as concealing goods and to raise an immediate alert. In Laurel's 247 Shop it is the loss-prevention layer for unstaffed operation. Flagging a concealment was its intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-10-09", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-09-70ff", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176; https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://veesion.io/mentions-legales/; https://www.coopszeged.hu/kapcsolat", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Gesture-recognition analysis watching every shopper during the hours when no staff are present, tuned to concealment movements rather than faces", "A remote dispatch service that receives the alert immediately, and camera footage that can be handed to police", "A sales floor small enough (150 square meters, 4,000 SKUs) for the cameras to cover it entirely" ], "must_hold": [ "The flagged gesture is a real theft rather than an innocent movement. The operators report no false-alarm figures", "Police are willing and able to act on the footage quickly, here within hours", "The system keeps analyzing movements without identifying faces, as the operators describe it" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-09-70ff", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176; https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://veesion.io/mentions-legales/; https://www.coopszeged.hu/kapcsolat", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Retail loss prevention", "segment": "Grocery retail" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Simon B K (2025). Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni. Index.hu, 9 October 2025, 20:39; the article says its facts are 'olvasható a sajtóközleményben', i.e. taken from the companies' press release.", "title": "Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni", "url": "https://index.hu/gazdasag/2025/10/09/magyar-okosbolt-szeged-ai-lopas-lebuktatas/", "date": "2025-10-09", "written_by": "independent", "quote": "a rendőrség a kamerafelvételek alapján néhány órán belül elfogta az elkövetőt" }, { "kind": "news report", "citation": "Mínuszos.hu (2025). AI leplezett le tolvajokat a szegedi hibrid okosboltban. Mínuszos.hu, 11 October 2025; names Veesion as a French company and the store as the Szent István tér Coop opened at the end of May.", "title": "AI leplezett le tolvajokat a szegedi hibrid okosboltban", "url": "https://www.minuszos.hu/ai-leplezett-le-tolvajokat-a-szegedi-hibrid-okosboltban/", "date": "2025-10-11", "written_by": "independent", "quote": "A mesterséges intelligenciával (AI) támogatott rendszer az elmúlt hónapokban három lopási kísérletet is sikeresen azonosított és akadályozott meg." }, { "kind": "news report", "citation": "ProfitLine.hu / MTI (2025). Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt. ProfitLine.hu, 11 October 2025, carrying the MTI agency report of the companies' statement; gives 150 square metres, 4,000 SKUs, and quotes István Bessenyei (Laurel) and János Kelemen (Coop Szeged).", "title": "Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt.", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176", "date": "2025-10-11", "written_by": "independent", "quote": "az arcok azonosítása nélkül elemzi a vásárlói mozgásokat, és azonnal jelzi a távoli diszpécserszolgálatnak a gyanús viselkedést" }, { "kind": "news report", "citation": "Turizmus Online / MTI (2025). A szegediek szeretik az okosboltokat – újabbakat nyitnak. Turizmus Online, 13 October 2025; reports 8,500 purchases in four and a half months and no rise in shrinkage despite the longer hours, per the operators.", "title": "A szegediek szeretik az okosboltokat – újabbakat nyitnak", "url": "https://turizmusonline.hu/cikkek/gasztro/a-szegediek-szeretik-az-okosboltokat--ujabbakat-nyitnak.html", "date": "2025-10-13", "written_by": "independent", "quote": "az új típusú bolt az indulás óta stabilan, a várakozások felett teljesít" }, { "kind": "news report", "citation": "Economx (2026). Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket. economx.hu, 30 September 2026.", "title": "Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "date": "2026-09-30", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0148-source-0001", "AIBD-0148-source-0002", "AIBD-0148-source-0003", "AIBD-0148-source-0004", "AIBD-0148-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Simon B K (2025). Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni. Index.hu, 9 October 2025, 20:39; the article says its facts are 'olvasható a sajtóközleményben', i.e. taken from the companies' press release.", "title": "Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni", "url": "https://index.hu/gazdasag/2025/10/09/magyar-okosbolt-szeged-ai-lopas-lebuktatas/", "date": "2025-10-09", "written_by": "independent", "quote": "a rendőrség a kamerafelvételek alapján néhány órán belül elfogta az elkövetőt" }, { "kind": "news report", "citation": "Mínuszos.hu (2025). AI leplezett le tolvajokat a szegedi hibrid okosboltban. Mínuszos.hu, 11 October 2025; names Veesion as a French company and the store as the Szent István tér Coop opened at the end of May.", "title": "AI leplezett le tolvajokat a szegedi hibrid okosboltban", "url": "https://www.minuszos.hu/ai-leplezett-le-tolvajokat-a-szegedi-hibrid-okosboltban/", "date": "2025-10-11", "written_by": "independent", "quote": "A mesterséges intelligenciával (AI) támogatott rendszer az elmúlt hónapokban három lopási kísérletet is sikeresen azonosított és akadályozott meg." }, { "kind": "news report", "citation": "ProfitLine.hu / MTI (2025). Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt. ProfitLine.hu, 11 October 2025, carrying the MTI agency report of the companies' statement; gives 150 square metres, 4,000 SKUs, and quotes István Bessenyei (Laurel) and János Kelemen (Coop Szeged).", "title": "Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt.", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176", "date": "2025-10-11", "written_by": "independent", "quote": "az arcok azonosítása nélkül elemzi a vásárlói mozgásokat, és azonnal jelzi a távoli diszpécserszolgálatnak a gyanús viselkedést" }, { "kind": "news report", "citation": "Turizmus Online / MTI (2025). A szegediek szeretik az okosboltokat – újabbakat nyitnak. Turizmus Online, 13 October 2025; reports 8,500 purchases in four and a half months and no rise in shrinkage despite the longer hours, per the operators.", "title": "A szegediek szeretik az okosboltokat – újabbakat nyitnak", "url": "https://turizmusonline.hu/cikkek/gasztro/a-szegediek-szeretik-az-okosboltokat--ujabbakat-nyitnak.html", "date": "2025-10-13", "written_by": "independent", "quote": "az új típusú bolt az indulás óta stabilan, a várakozások felett teljesít" }, { "kind": "news report", "citation": "Economx (2026). Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket. economx.hu, 30 September 2026.", "title": "Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "date": "2026-09-30", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "AI cameras in Szeged's unstaffed-at-night Coop flagged a shopper hiding goods; police caught them within hours, the operators said.", "account": "Coop Szeged Zrt. and Laurel Kft. opened the 247 Shop on Szent István tér in Szeged at the end of May 2025. It is staffed by day, automated and unstaffed outside normal hours. Cameras running the French company Veesion's software analyze shoppers' movements without identifying faces. They alert a remote dispatch service to gestures such as concealing goods. On 9 October 2025 the operators issued a press release relayed by Index.hu and MTI. It said over four and a half months the system had exposed three theft attempts. In one, a person hid products under their clothing during night mode. Police caught the perpetrator within hours using the footage. Dates, recovered goods and any police statement are not reported." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Not reported" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Veesion AI gesture-recognition camera analysis integrated into Laurel Kft.'s 247 Shop hybrid-store platform, with alerts to a remote dispatch service" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Veesion" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Veesion gesture-recognition cameras in Coop Szeged's 247 Shop on Szent István tér, unstaffed at night, may have flagged a shopper hiding goods under clothing and led police to the perpetrator within hours, one of three attempts exposed since May 2025", "headline": "Veesion alert may have led police to a Szeged shoplifter" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "426c9b02e8e63eb5fd423b6a1cf78ec516aaabb2e070563d9f80bd9468b3faa2" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Coop Szeged Zrt. and Laurel Kft. opened the 247 Shop on Szent István tér in Szeged at the end of May 2025. It is staffed by day, automated and unstaffed outside normal hours. Cameras running the French company Veesion's software analyze shoppers' movements without identifying faces. They alert a remote dispatch service to gestures such as concealing goods. On 9 October 2025 the operators issued a press release relayed by Index.hu and MTI. It said over four and a half months the system had exposed three theft attempts. In one, a person hid products under their clothing during night mode. Police caught the perpetrator within hours using the footage. Dates, recovered goods and any police statement are not reported.", "replication": { "produced_by": [ "Gesture-recognition analysis watching every shopper during the hours when no staff are present, tuned to concealment movements rather than faces", "A remote dispatch service that receives the alert immediately, and camera footage that can be handed to police", "A sales floor small enough (150 square meters, 4,000 SKUs) for the cameras to cover it entirely" ], "must_hold": [ "The flagged gesture is a real theft rather than an innocent movement. The operators report no false-alarm figures", "Police are willing and able to act on the footage quickly, here within hours", "The system keeps analyzing movements without identifying faces, as the operators describe it" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0305", "case_url": "records/AIBD-0305.html", "reason": "The developer is headquartered in United States; the case names United Kingdom.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "United Kingdom" }, { "label": "Case location", "value": "Cardiff, United Kingdom" } ], "filters": { "origin": "United States", "destination": [ "United Kingdom" ] }, "record": { "id": "AIBD-0305", "title": "ChatGPT lists hereditary spastic paraplegia for Phoebe Tesoriere, 23, after four years of misdiagnosis; her GP agrees and genetic testing confirms it", "summary": "A Cardiff teacher put her symptoms into ChatGPT after a coma; it listed hereditary spastic paraplegia, her GP called it plausible and genetic testing confirmed it in August 2025 (BBC, WalesOnline).", "date_observed": "2025-08-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Phoebe Tesoriere, 23, from Cardiff, Wales, is a former special educational needs teacher. She had a limp since childhood. From 2022 she was diagnosed with epilepsy, then Todd's paralysis, and repeatedly told her symptoms were anxiety. In January 2025 she fell down stairs and spent months in hospital. In July 2025 a seizure left her in a coma (three days per BBC Wales, 48 hours per the SWNS report in WalesOnline). After discharge she entered her symptoms into ChatGPT. It returned a list of possible conditions including hereditary spastic paraplegia (HSP). She took the suggestion to her GP, who agreed it was a “plausible reason”. Genetic testing confirmed HSP, in August 2025 according to WalesOnline, which also reports the complex quadriplegic type. The first day of August is used because no day is given. There is no treatment. She uses a wheelchair, attends physiotherapy and is fundraising for a specialist chair. Cardiff and Vale University Health Board said it was “sorry to hear about Phoebe's experience while in our care”. It would not comment on an individual case. The account rests on her own statements to BBC Wales (10 April 2026) and SWNS (6 April 2026). No clinical document is among the sources.", "category": "benefit created", "unexpected": true, "purpose": "ChatGPT is a general-purpose chatbot not built or cleared for diagnosis. The patient used it to generate candidate conditions from her symptom list.", "system": "ChatGPT", "model_version": null, "developer": "OpenAI", "deployer": null, "location": "Cardiff, United Kingdom", "domain": "Healthcare (rare-disease diagnosis)", "segment": "Patient-initiated use of a consumer LLM", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "A confirmed diagnosis replaced “anxiety” on her records and lets her plan care and equipment. The condition is progressive with no treatment. So the gain is certainty and appropriate management rather than a changed prognosis." } ], "ai_role": { "status": "assessed", "statement": "By her account the chatbot produced a list of candidates that included HSP. She decided to take it to her GP. The GP ordered genetic testing, and the test made the diagnosis. The prompt, the model version and the full candidate list are not reported. Only news reports quoting the patient are among the sources. The health board declined to comment on the case." }, "evidence": [ { "kind": "news report", "citation": "Herbert D (2026). ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis. BBC Wales, 10 April 2026.", "title": "ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis", "url": "https://www.bbc.com/news/articles/cx24njzzkgjo", "date": "2026-04-10", "written_by": "independent", "quote": "The chatbot came back with a list of possible conditions, including hereditary spastic paraplegia." }, { "kind": "news report", "citation": "Phillips E / SWNS (2026). Cardiff woman uses Chat GPT to uncover rare condition doctor thought was anxiety. WalesOnline, 6 April 2026.", "title": "Cardiff woman uses Chat GPT to uncover rare condition doctor thought was anxiety", "url": "https://www.walesonline.co.uk/news/wales-news/cardiff-woman-uses-chat-gpt-33722339", "date": "2026-04-06", "written_by": "independent", "quote": "Phoebe underwent genetic testing which confirmed in August 2025 that she had the complex quadriplegic type of the condition" }, { "kind": "news report", "citation": "Herbert D (2026). BBC Wales, 10 April 2026 (health board statement).", "title": "ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis", "url": "https://www.bbc.com/news/articles/cx24njzzkgjo", "date": "2026-04-10", "written_by": "independent", "quote": "Cardiff and Vale University Health Board said: \"We are sorry to hear about Phoebe's experience while in our care.\"" } ], "replication": { "produced_by": [ "A patient with a long list of concrete symptoms (gait, incontinence, sensory loss, reflex changes, seizures) and no settled diagnosis", "Entry of the symptom list into a general large language model (LLM)", "A GP willing to test a patient-sourced hypothesis and access to genetic testing" ], "must_hold": [ "The true condition is among the candidates the model lists and the patient pursues it", "Clinicians confirm by genetic testing rather than accepting the suggestion" ], "echoes": [] }, "paired_incident": null, "headline": "ChatGPT lists HSP for Phoebe Tesoriere; genetic testing confirms", "metric": null, "comparator": { "against": "four years of diagnoses by doctors (anxiety, depression, epilepsy)", "before": "she was told she was anxious, depressed, had epilepsy", "after": "Genetic testing then confirmed the AI suggestion.", "basis": "assumed", "quote": "ChatGPT has helped to uncover a woman's rare condition after years of being misdiagnosed by doctors. Phoebe Tesoriere, 23, claims she was told she was anxious, depressed, had epilepsy and warned she'd be treated as a mental health patient if she kept returning to A&E.", "url": "https://www.bbc.com/news/articles/cx24njzzkgjo" }, "later": { "status": "unknown", "date": null, "note": "Nothing dated after the April 2026 reports was found; WalesOnline says she is fundraising for a specialist wheelchair and there is no treatment.", "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 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.bbc.com/news/articles/cx24njzzkgjo", "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: 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-0305", "catalog_version": "1.0.0", "source_fingerprint": "d355588efc47bda5156c54087fdc76185cc4bfe5d67496bee3285c5043edec3f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0305-source-0001", "AIBD-0305-source-0002", "AIBD-0305-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": "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": "By her account the chatbot produced a list of candidates that included HSP. She decided to take it to her GP. The GP ordered genetic testing, and the test made the diagnosis. The prompt, the model version and the full candidate list are not reported. Only news reports quoting the patient are among the sources. The health board declined to comment on the case." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "By her account the chatbot produced a list of candidates that included HSP. She decided to take it to her GP. The GP ordered genetic testing, and the test made the diagnosis. The prompt, the model version and the full candidate list are not reported. Only news reports quoting the patient are among the sources. The health board declined to comment on the case." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A confirmed diagnosis replaced “anxiety” on her records and lets her plan care and equipment. The condition is progressive with no treatment. So the gain is certainty and appropriate management rather than a changed prognosis." } ] } }, "provenance": { "kind": "legacy_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": "A confirmed diagnosis replaced “anxiety” on her records and lets her plan care and equipment. The condition is progressive with no treatment. So the gain is certainty and appropriate management rather than a changed prognosis." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A confirmed diagnosis replaced “anxiety” on her records and lets her plan care and equipment. The condition is progressive with no treatment. So the gain is certainty and appropriate management rather than a changed prognosis." } ] } }, "provenance": { "kind": "legacy_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": 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{ "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Phoebe Tesoriere, 23, from Cardiff, Wales, is a former special educational needs teacher. She had a limp since childhood. From 2022 she was diagnosed with epilepsy, then Todd's paralysis, and repeatedly told her symptoms were anxiety. In January 2025 she fell down stairs and spent months in hospital. In July 2025 a seizure left her in a coma (three days per BBC Wales, 48 hours per the SWNS report in WalesOnline). After discharge she entered her symptoms into ChatGPT. It returned a list of possible conditions including hereditary spastic paraplegia (HSP). She took the suggestion to her GP, who agreed it was a “plausible reason”. Genetic testing confirmed HSP, in August 2025 according to WalesOnline, which also reports the complex quadriplegic type. The first day of August is used because no day is given. There is no treatment. She uses a wheelchair, attends physiotherapy and is fundraising for a specialist chair. Cardiff and Vale University Health Board said it was “sorry to hear about Phoebe's experience while in our care”. It would not comment on an individual case. The account rests on her own statements to BBC Wales (10 April 2026) and SWNS (6 April 2026). No clinical document 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": 2, "reason": "A confirmed diagnosis replaced “anxiety” on her records and lets her plan care and equipment. The condition is progressive with no treatment. So the gain is certainty and appropriate management rather than a changed prognosis." } ] } }, "provenance": { "kind": "legacy_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 chatbot not built or cleared for diagnosis. The patient used it to generate candidate conditions from her symptom list." } }, "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": "Cardiff, United Kingdom", "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 chatbot not built or cleared for diagnosis. The patient used it to generate candidate conditions from her symptom list." } }, "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": "2026-10-21", "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.bbc.com/news/articles/cx24njzzkgjo", "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 patient with a long list of concrete symptoms (gait, incontinence, sensory loss, reflex changes, seizures) and no settled diagnosis", "Entry of the symptom list into a general large language model (LLM)", "A GP willing to test a patient-sourced hypothesis and access to genetic testing" ], "must_hold": [ "The true condition is among the candidates the model lists and the patient pursues it", "Clinicians confirm by genetic testing rather than accepting the suggestion" ], "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 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.bbc.com/news/articles/cx24njzzkgjo", "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 (rare-disease diagnosis)", "segment": "Patient-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": "news report", "citation": "Herbert D (2026). ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis. BBC Wales, 10 April 2026.", "title": "ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis", "url": "https://www.bbc.com/news/articles/cx24njzzkgjo", "date": "2026-04-10", "written_by": "independent", "quote": "The chatbot came back with a list of possible conditions, including hereditary spastic paraplegia." }, { "kind": "news report", "citation": "Phillips E / SWNS (2026). Cardiff woman uses Chat GPT to uncover rare condition doctor thought was anxiety. WalesOnline, 6 April 2026.", "title": "Cardiff woman uses Chat GPT to uncover rare condition doctor thought was anxiety", "url": "https://www.walesonline.co.uk/news/wales-news/cardiff-woman-uses-chat-gpt-33722339", "date": "2026-04-06", "written_by": "independent", "quote": "Phoebe underwent genetic testing which confirmed in August 2025 that she had the complex quadriplegic type of the condition" }, { "kind": "news report", "citation": "Herbert D (2026). BBC Wales, 10 April 2026 (health board statement).", "title": "ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis", "url": "https://www.bbc.com/news/articles/cx24njzzkgjo", "date": "2026-04-10", "written_by": "independent", "quote": "Cardiff and Vale University Health Board said: \"We are sorry to hear about Phoebe's experience while in our care.\"" } ] } }, "provenance": { "kind": "legacy_record", "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-0305-source-0001", "AIBD-0305-source-0002", "AIBD-0305-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": "Herbert D (2026). ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis. BBC Wales, 10 April 2026.", "title": "ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis", "url": "https://www.bbc.com/news/articles/cx24njzzkgjo", "date": "2026-04-10", "written_by": "independent", "quote": "The chatbot came back with a list of possible conditions, including hereditary spastic paraplegia." }, { "kind": "news report", "citation": "Phillips E / SWNS (2026). Cardiff woman uses Chat GPT to uncover rare condition doctor thought was anxiety. WalesOnline, 6 April 2026.", "title": "Cardiff woman uses Chat GPT to uncover rare condition doctor thought was anxiety", "url": "https://www.walesonline.co.uk/news/wales-news/cardiff-woman-uses-chat-gpt-33722339", "date": "2026-04-06", "written_by": "independent", "quote": "Phoebe underwent genetic testing which confirmed in August 2025 that she had the complex quadriplegic type of the condition" }, { "kind": "news report", "citation": "Herbert D (2026). BBC Wales, 10 April 2026 (health board statement).", "title": "ChatGPT 'uncovered woman's rare condition' after years of misdiagnosis", "url": "https://www.bbc.com/news/articles/cx24njzzkgjo", "date": "2026-04-10", "written_by": "independent", "quote": "Cardiff and Vale University Health Board said: \"We are sorry to hear about Phoebe's experience while in our care.\"" } ] } }, "provenance": { "kind": "legacy_record", "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 Cardiff teacher put her symptoms into ChatGPT after a coma; it listed hereditary spastic paraplegia, her GP called it plausible and genetic testing confirmed it in August 2025 (BBC, WalesOnline).", "account": "Phoebe Tesoriere, 23, from Cardiff, Wales, is a former special educational needs teacher. She had a limp since childhood. From 2022 she was diagnosed with epilepsy, then Todd's paralysis, and repeatedly told her symptoms were anxiety. In January 2025 she fell down stairs and spent months in hospital. In July 2025 a seizure left her in a coma (three days per BBC Wales, 48 hours per the SWNS report in WalesOnline). After discharge she entered her symptoms into ChatGPT. It returned a list of possible conditions including hereditary spastic paraplegia (HSP). She took the suggestion to her GP, who agreed it was a “plausible reason”. Genetic testing confirmed HSP, in August 2025 according to WalesOnline, which also reports the complex quadriplegic type. The first day of August is used because no day is given. There is no treatment. She uses a wheelchair, attends physiotherapy and is fundraising for a specialist chair. Cardiff and Vale University Health Board said it was “sorry to hear about Phoebe's experience while in our care”. It would not comment on an individual case. The account rests on her own statements to BBC Wales (10 April 2026) and SWNS (6 April 2026). No clinical document 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": "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 lists hereditary spastic paraplegia for Phoebe Tesoriere, 23, after four years of misdiagnosis; her GP agrees and genetic testing confirms it", "headline": "ChatGPT lists HSP for Phoebe Tesoriere; genetic testing 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": [ "d355588efc47bda5156c54087fdc76185cc4bfe5d67496bee3285c5043edec3f" ], "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": "Phoebe Tesoriere, 23, from Cardiff, Wales, is a former special educational needs teacher. She had a limp since childhood. From 2022 she was diagnosed with epilepsy, then Todd's paralysis, and repeatedly told her symptoms were anxiety. In January 2025 she fell down stairs and spent months in hospital. In July 2025 a seizure left her in a coma (three days per BBC Wales, 48 hours per the SWNS report in WalesOnline). After discharge she entered her symptoms into ChatGPT. It returned a list of possible conditions including hereditary spastic paraplegia (HSP). She took the suggestion to her GP, who agreed it was a “plausible reason”. Genetic testing confirmed HSP, in August 2025 according to WalesOnline, which also reports the complex quadriplegic type. The first day of August is used because no day is given. There is no treatment. She uses a wheelchair, attends physiotherapy and is fundraising for a specialist chair. Cardiff and Vale University Health Board said it was “sorry to hear about Phoebe's experience while in our care”. It would not comment on an individual case. The account rests on her own statements to BBC Wales (10 April 2026) and SWNS (6 April 2026). No clinical document is among the sources.", "replication": { "produced_by": [ "A patient with a long list of concrete symptoms (gait, incontinence, sensory loss, reflex changes, seizures) and no settled diagnosis", "Entry of the symptom list into a general large language model (LLM)", "A GP willing to test a patient-sourced hypothesis and access to genetic testing" ], "must_hold": [ "The true condition is among the candidates the model lists and the patient pursues it", "Clinicians confirm by genetic testing rather than accepting the suggestion" ], "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-0033", "case_url": "records/AIBD-0033.html", "reason": "The developer is headquartered in United States; the case names Italy.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Italy" }, { "label": "Case location", "value": "Monviso north face, Perotti couloir (province of Cuneo), Italy" } ], "filters": { "origin": "United States", "destination": [ "Italy" ] }, "record": { "id": "AIBD-0033", "title": "CNSAS Piemonte drone imagery analyzed with AI software, likely TEXSAR's ADIAT, locates missing surgeon Nicola Ivaldo on Monviso after ten months", "summary": "AI analysis of 2,600 drone frames flagged a red helmet in the Perotti couloir at 3,150 m; Ivaldo's body was recovered by 31 July 2025.", "date_observed": "2025-07-31", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Nicola Ivaldo was a surgeon aged 64 according to the Club Alpino Italiano's Lo Scarpone (another report says 66). He disappeared on 14 September 2024 per CAI during a solo outing on Monviso in Piedmont, Italy. Quadricottero dates his departure, when he parked his car at the Pontechianale dam, to 15 September. Searches over ten months found nothing. On 29 July 2025 the Soccorso Alpino e Speleologico Piemontese (CNSAS) flew two drones close to the north face. They collected about 2,600 high-resolution frames over 183 hectares in five hours. The frames were analyzed pixel by pixel with AI image-analysis software. Developer TEXSAR identifies it as the open-source Automated Drone Image Analysis Tool (ADIAT). The frames yielded a tiny red anomaly in the Perotti couloir: his helmet. His body was located at about 3,150 m and recovered by 31 July 2025. That date comes from TEXSAR's page (which cites Wired). It is consistent with bluewin's report of under 72 hours from the 29 July flight to the recovery. CAI gives only July 2025 and Quadricottero no date.", "category": "benefit created", "unexpected": false, "purpose": "ADIAT was built to scan drone photographs and flag color and shape anomalies for search-and-rescue teams. Locating the missing man's helmet was that intended function.", "system": "AI drone-image analysis software used by CNSAS Piemonte (identified as ADIAT by its developer, TEXSAR; CAI and CNSAS sources do not name it)", "model_version": null, "developer": "TEXSAR (Texas Search and Rescue) volunteers, per the developer's own account", "deployer": "Soccorso Alpino e Speleologico Piemontese (CNSAS Piemonte), Turin station drone team", "location": "Monviso north face, Perotti couloir (province of Cuneo), Italy", "domain": "Mountain search and rescue", "segment": "Recovery of a missing person in high-alpine terrain", "benefit_type": "Reduced Task Time", "taxonomy_note": "The time cut is the rescuers' frame review; the family's good, recovery of remains, has no type.", "registry_ids": { "oecd_aim": "2026-01-10-f4af" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "Ivaldo's family obtained recovery of his body and certainty about his fate after ten months of searching. A material benefit, not life-saving, since he had died in September 2024." }, { "group": "organizational", "depth": 2, "reason": "CNSAS Piemonte ended a search that had involved ground teams, Guardia di Finanza and fire-service helicopters for ten months. Manual analysis of the drone photos would have taken weeks (CAI) to months (CNSAS, per bluewin)." } ], "ai_role": { "status": "assessed", "statement": "The AI flagged a few red pixels, the helmet, a detail CAI describes as invisible to the naked eye. Ten months of helicopters, specialist teams and advanced technology (Quadricottero) had not resolved the search. Drone pilot Saverio Isola is quoted that it was the AI software that identified the differently colored pixels. CAI/CNSAS sources do not name the software. The ADIAT attribution comes from the developer TEXSAR's own page. So the tool identity rests on a vendor-authored source." }, "evidence": [ { "kind": "press release", "citation": "Lo Scarpone, Club Alpino Italiano (2026). Sulla BBC il 'caso Ivaldo': l'Intelligenza Artificiale a supporto del Soccorso Alpino. 18 January 2026 (CAI is the parent body of CNSAS).", "title": "Sulla BBC il 'caso Ivaldo': l'Intelligenza Artificiale a supporto del Soccorso Alpino", "url": "https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "date": "2026-01-18", "written_by": "deployer", "quote": "Due droni sono stati impiegati per voli ravvicinati alle pareti del Monviso, e in 5 ore sono stati raccolti circa 2.600 fotogrammi ad alta risoluzione." }, { "kind": "news report", "citation": "Scarato D (2025). Droni e IA trovano il corpo del medico alpinista disperso da mesi sul Monviso. Quadricottero News, 1 August 2025 (photo credited to Soccorso Alpino e Speleologico Piemonte - CNSAS).", "title": "Droni e IA trovano il corpo del medico alpinista disperso da mesi sul Monviso", "url": "https://www.quadricottero.com/2025/08/droni-e-ia-trovano-il-corpo-del-medico.html", "date": "2025-08-01", "written_by": "independent", "quote": "Il ritrovamento, avvenuto a circa 3150 metri di quota sulla parete nord della montagna, nel canale Perotti, è stato possibile grazie a un impiego mirato di droni professionali" }, { "kind": "news report", "citation": "bluewin.ch (2025). Sulle Alpi Italiane: Ecco com'è stato possibile localizzare un escursionista scomparso da un anno. 10 August 2025 (updated 12 August 2025).", "title": "Ecco com'è stato possibile localizzare un escursionista scomparso da un anno", "url": "https://www.bluewin.ch/it/attualita/escursionista-scomparso-da-un-anno-ecco-come-e-stato-localizzato-li.2817148", "date": "2025-08-10", "written_by": "independent", "quote": "Sono stati questi pixel a cambiare tutto" }, { "kind": "press release", "citation": "Texas Search and Rescue (2025). ADIAT Helps Italian SAR Team. TEXSAR website, 15 August 2025 (developer of ADIAT).", "title": "ADIAT Helps Italian SAR Team", "url": "https://www.texsar.org/2025/08/15/adiat-helps-italian-sar-team/", "date": "2025-08-15", "written_by": "makers", "quote": "It was the AI software that identified some pixels of a different color in the images taken on Tuesday." }, { "kind": "news report", "citation": "Simičević, V. (2026). A red pixel in the snow: How AI solved the mystery of a missing mountaineer. BBC Future.", "title": "A red pixel in the snow: How AI solved the mystery of a missing mountaineer", "url": "https://www.bbc.com/future/article/20260108-how-ai-solved-the-mystery-of-a-missing-mountaineer", "date": "2026-01-09", "written_by": "independent", "quote": "Three days after the search resumed, however, the body of the missing doctor was found at one of the sites identified by the AI, lying in a gully" }, { "kind": "news report", "citation": "La Stampa (2025). Trovato il corpo del medico di Albenga disperso dal 15 settembre sul Monviso. La Stampa, Cuneo.", "title": "Trovato il corpo del medico di Albenga disperso dal 15 settembre sul Monviso", "url": "https://www.lastampa.it/cuneo/2025/07/31/news/trovato_il_corpo_del_medico_di_loano_disperso_dal_15_settembre_sul_monviso-15253571/", "date": "2025-07-31", "written_by": "independent", "quote": "realizzati 2600 fotogrammi che sono stati caricati su un apposito software, in dotazione al Soccorso alpino e speleologico, che ha elaborato i dati indicando tre possibili target da verificare" }, { "kind": "news report", "citation": "Abbà, M. (2025). Il corpo di un escursionista disperso sul Monviso è stato recuperato dopo quasi un anno grazie a un sistema di AI. Wired Italia.", "title": "Il corpo di un escursionista disperso sul Monviso è stato recuperato dopo quasi un anno grazie a un sistema di AI. Che ha riconosciuto il caschetto tra migliaia di immagini", "url": "https://www.wired.it/article/escursionista-disperso-monviso-cadavere-drone-intelligenza-artificiale-soccorso-alpino/", "date": "2025-07-31", "written_by": "independent", "quote": "la salma del medico ligure di 64 anni Nicola Ivaldo è stata trovata nella mattinata di giovedì 31 luglio, a distanza di oltre dieci mesi dalla sua scomparsa" }, { "kind": "news report", "citation": "Abo Abia, M. (2025). Ritrovato il corpo di Nicola Ivaldo, disperso sul Monviso dal settembre 2024. RaiNews, TGR Piemonte.", "title": "Ritrovato il corpo di Nicola Ivaldo, disperso sul Monviso dal settembre 2024", "url": "https://www.rainews.it/tgr/piemonte/articoli/2025/07/ritrovato-il-corpo-di-nicola-ivaldo-disperso-sul-monviso-dal-settembre-2024-d14c11b3-3753-454e-98b4-d8919b1648bc.html", "date": "2025-07-31", "written_by": "independent", "quote": "Il programma, analizzando video e foto, ha individuato tre aree sensibili: zone con dettagli non naturali e incompatibili con l'ambiente circostante" }, { "kind": "press release", "citation": "Lo Scarpone (2025). Monviso, ritrovato il corpo del medico disperso da settembre 2024. Club Alpino Italiano.", "title": "Monviso, ritrovato il corpo del medico disperso da settembre 2024", "url": "https://www.loscarpone.cai.it/dettaglio/monviso-ritrovato-il-corpo-del-medico-disperso-da-settembre-2024/", "date": "2025-07-31", "written_by": "deployer", "quote": "raccolto circa 2600 fotogrammi ad alta risoluzione, poi analizzati tramite un software in dotazione al Soccorso Alpino che ha indicato tre possibili punti di interesse tramite analisi pixel-per-pixel" }, { "kind": "news report", "citation": "Ciprian, V. (2026). Quel punto rosso era il casco dell'alpinista disperso da mesi. L'AltraMontagna, il Dolomiti.", "title": "Quel punto rosso era il casco dell'alpinista disperso da mesi: \"Senza l'anomalia segnalata dall'intelligenza artificiale in una delle 2600 foto dal drone, probabilmente non sarebbe mai stato trovato\"", "url": "https://www.ildolomiti.it/altra-montagna/storie/2026/quel-punto-rosso-era-il-casco-dellalpinista-disperso-da-mesi-senza-lanomalia-segnalata-dallintelligenza-artificiale-in-una-delle-2600-foto-dal-drone-probabilmente-non-sarebbe-mai-stato-trovato", "date": "2026-01-13", "written_by": "independent", "quote": "il drone, penetrando nel canale Perotti, ha individuato i resti di un essere umano nel canale più a destra dei tre che solcano la parete nord" }, { "kind": "news report", "citation": "Rullo, F. (2025). Monviso, ritrovato dopo un anno e solo grazie ai droni il corpo di Nicola Ivaldo, medico savonese scomparso in montagna. Corriere della Sera, Torino.", "title": "Monviso, ritrovato dopo un anno e solo grazie ai droni il corpo di Nicola Ivaldo, medico savonese scomparso in montagna", "url": "https://torino.corriere.it/notizie/piemonte/25_luglio_31/monviso-ritrovato-dopo-un-anno-e-solo-grazie-ai-droni-il-corpo-di-nicola-ivaldo-medico-savonese-scomparso-in-montagna-c943f224-5e04-4320-856b-a3d4533caxlk.shtml", "date": "2025-07-31", "written_by": "independent", "quote": "ritrovati grazie all'uso di droni e a un apposito software in dotazione al soccorso alpino: erano in un canale a 3150 metri di quota" }, { "kind": "news report", "citation": "Annapurna, K. (2025). AI Solves the Mystery of Missing Italian Climber. ExplorersWeb.", "title": "AI Solves the Mystery of Missing Italian Climber", "url": "https://explorersweb.com/ai-solves-the-mystery-of-missing-italian-climber/", "date": "2025-08-07", "written_by": "independent", "quote": "an AI software analyzed these many images in a single afternoon, singling out a cluster of red pixels that turned out to be Ivaldo's helmet" }, { "kind": "news report", "citation": "Malfatti, M. (2025). Anche l'intelligenza artificiale al servizio del Soccorso Alpino. Montagna.TV.", "title": "Anche l'intelligenza artificiale al servizio del Soccorso Alpino", "url": "https://www.montagna.tv/262960/anche-lintelligenza-artificiale-al-servizio-del-soccorso-alpino/", "date": "2025-10-06", "written_by": "independent", "quote": "l'analisi automatica e dettagliata di migliaia di fotogrammi ha permesso di isolare potenziali target in un'area decisamente vasta ed impervia" }, { "kind": "news report", "citation": "Corriere della Sera Torino (2024). Disperso sul Monviso da sabato Nicola Ivaldo, 64enne chirurgo ortopedico di Loano. Corriere.it.", "title": "Disperso sul Monviso da sabato Nicola Ivaldo, 64enne chirurgo ortopedico di Loano. Lavorava ad Acqui Terme", "url": "https://torino.corriere.it/notizie/piemonte/24_settembre_17/disperso-sul-monviso-da-sabato-ha-parcheggiato-l-auto-alla-diga-di-pontechianale-poi-nessuna-traccia-89019eb1-5c94-4928-a627-187960555xlk.shtml", "date": "2024-09-17", "written_by": "independent", "quote": "presumibilmente è partito a piedi nella giornata di sabato dal parcheggio della diga di Pontechianale, nel cuneese, dove è ancora presente la sua auto" }, { "kind": "other", "citation": "OECD.AI (2026). AI-Assisted Drone Search Locates Missing Mountaineer in Italian Alps. OECD AI Incidents and Hazards Monitor.", "title": "AI-Assisted Drone Search Locates Missing Mountaineer in Italian Alps", "url": "https://oecd.ai/en/incidents/2026-01-10-f4af", "date": "2026-01-10", "written_by": "independent", "quote": "AI software analyzing drone images identified the body of missing Italian mountaineer Nicola Ivaldo in the Piedmont Alps" } ], "replication": { "produced_by": [ "Close-range high-resolution drone photography of steep faces (about 2,600 frames over 183 hectares)", "A target object (red helmet) with strong color contrast against rock and ice", "Search area narrowed by mobile-phone cell data to the Monviso and Visolotto faces" ], "must_hold": [ "Weather and terrain allow drones to fly close to the walls", "The subject or equipment remains visible and not buried by snow", "Rescuers verify AI flags, which CNSAS notes can also fire on plastic or unusual rock color" ], "echoes": [] }, "paired_incident": null, "headline": "ADIAT spots a red helmet and locates Nicola Ivaldo on Monviso", "metric": "located at about 3,150 m and recovered by 31 July 2025", "comparator": { "against": "conventional ground and air searches over the preceding months", "before": "I metodi di ricerca convenzionali sono rimasti inconcludenti per mesi", "after": "meno di 72 ore dalla prima missione del drone al recupero del corpo", "basis": "measured", "quote": "I metodi di ricerca convenzionali sono rimasti inconcludenti per mesi, mentre sono passate meno di 72 ore dalla prima missione del drone al recupero del corpo.", "url": "https://www.bluewin.ch/it/attualita/escursionista-scomparso-da-un-anno-ecco-come-e-stato-localizzato-li.2817148" }, "later": { "status": "persisted", "date": "2026-01-18", "note": "By January 2026 the CNSAS Piemonte drone lead was working with Politecnico di Torino's geomatics team on a successor tool that adds precise georeferenced coordinates to the color-anomaly detection.", "quote": "Giordan, che è anche soccorritore, sta collaborando con il team di geomatica del Politecnico di Torino per sviluppare un software capace non solo di individuare anomalie cromatiche, ma di fornire coordinate georeferenziate di estrema precisione.", "url": "https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/" }, "value": null, "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": "2026-10-20", "change": "Corrected the bluewin.ch title to the page's headline, hedged the title's ADIAT attribution to its vendor source, placed the find on the Monviso north face rather than at Pontechianale (only his starting point), removed the unsupported claim that human reviewers had missed the helmet, and noted that the sources differ on the manual-review time (weeks per CAI, months per CNSAS via bluewin), the disappearance date (14 or 15 September 2024) and the source of the 31 July 2025 recovery date.", "source": "https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged with a second write-up of the same event (11 sources and the registry id carried over); the duplicate was removed.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.bluewin.ch/it/attualita/escursionista-scomparso-da-un-anno-ecco-come-e-stato-localizzato-li.2817148; https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Task Time under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://texsar.org/; https://news.cnsas.it/piemonte/", "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: 11 independent news reports, one other independent source, 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-0033", "catalog_version": "1.0.0", "source_fingerprint": "104bc06c9ff35eb203cd8b36ceced9dbff314ff19228b999bc38a5aecf7b423a", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0033-source-0001", "AIBD-0033-source-0002", "AIBD-0033-source-0003", "AIBD-0033-source-0004", "AIBD-0033-source-0005", "AIBD-0033-source-0006", "AIBD-0033-source-0007", "AIBD-0033-source-0008", "AIBD-0033-source-0009", "AIBD-0033-source-0010", "AIBD-0033-source-0011", "AIBD-0033-source-0012", "AIBD-0033-source-0013", "AIBD-0033-source-0014", "AIBD-0033-source-0015" ], "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": "TEXSAR (Texas Search and Rescue) volunteers, per the developer's own account", "deployer": "Soccorso Alpino e Speleologico Piemontese (CNSAS Piemonte), Turin station drone team" } }, "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 flagged a few red pixels, the helmet, a detail CAI describes as invisible to the naked eye. Ten months of helicopters, specialist teams and advanced technology (Quadricottero) had not resolved the search. Drone pilot Saverio Isola is quoted that it was the AI software that identified the differently colored pixels. CAI/CNSAS sources do not name the software. The ADIAT attribution comes from the developer TEXSAR's own page. So the tool identity rests on a vendor-authored source." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The AI flagged a few red pixels, the helmet, a detail CAI describes as invisible to the naked eye. Ten months of helicopters, specialist teams and advanced technology (Quadricottero) had not resolved the search. Drone pilot Saverio Isola is quoted that it was the AI software that identified the differently colored pixels. CAI/CNSAS sources do not name the software. The ADIAT attribution comes from the developer TEXSAR's own page. So the tool identity rests on a vendor-authored source." } } }, "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": "Ivaldo's family obtained recovery of his body and certainty about his fate after ten months of searching. A material benefit, not life-saving, since he had died in September 2024." }, { "group": "organizational", "depth": 2, "reason": "CNSAS Piemonte ended a search that had involved ground teams, Guardia di Finanza and fire-service helicopters for ten months. Manual analysis of the drone photos would have taken weeks (CAI) to months (CNSAS, per bluewin)." } ] } }, "provenance": { "kind": "legacy_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, one other independent source, 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": 2, "reason": "Ivaldo's family obtained recovery of his body and certainty about his fate after ten months of searching. A material benefit, not life-saving, since he had died in September 2024." }, { "group": "organizational", "depth": 2, "reason": "CNSAS Piemonte ended a search that had involved ground teams, Guardia di Finanza and fire-service helicopters for ten months. Manual analysis of the drone photos would have taken weeks (CAI) to months (CNSAS, per bluewin)." } ] } }, "provenance": { "kind": "legacy_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": "Ivaldo's family obtained recovery of his body and certainty about his fate after ten months of searching. A material benefit, not life-saving, since he had died in September 2024." }, { "group": "organizational", "depth": 2, "reason": "CNSAS Piemonte ended a search that had involved ground teams, Guardia di Finanza and fire-service helicopters for ten months. Manual analysis of the drone photos would have taken weeks (CAI) to months (CNSAS, per bluewin)." } ] } }, "provenance": { "kind": "legacy_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": "located at about 3,150 m and recovered by 31 July 2025", "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": "2026-01-10-f4af" } } }, "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": "2026-01-18", "note": "By January 2026 the CNSAS Piemonte drone lead was working with Politecnico di Torino's geomatics team on a successor tool that adds precise georeferenced coordinates to the color-anomaly detection.", "quote": "Giordan, che è anche soccorritore, sta collaborando con il team di geomatica del Politecnico di Torino per sviluppare un software capace non solo di individuare anomalie cromatiche, ma di fornire coordinate georeferenziate di estrema precisione.", "url": "https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/" } } }, "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-0033" } }, "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 Task Time", "taxonomy_note": "The time cut is the rescuers' frame review; the family's good, recovery of remains, has no type." } }, "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": "Nicola Ivaldo was a surgeon aged 64 according to the Club Alpino Italiano's Lo Scarpone (another report says 66). He disappeared on 14 September 2024 per CAI during a solo outing on Monviso in Piedmont, Italy. Quadricottero dates his departure, when he parked his car at the Pontechianale dam, to 15 September. Searches over ten months found nothing. On 29 July 2025 the Soccorso Alpino e Speleologico Piemontese (CNSAS) flew two drones close to the north face. They collected about 2,600 high-resolution frames over 183 hectares in five hours. The frames were analyzed pixel by pixel with AI image-analysis software. Developer TEXSAR identifies it as the open-source Automated Drone Image Analysis Tool (ADIAT). The frames yielded a tiny red anomaly in the Perotti couloir: his helmet. His body was located at about 3,150 m and recovered by 31 July 2025. That date comes from TEXSAR's page (which cites Wired). It is consistent with bluewin's report of under 72 hours from the 29 July flight to the recovery. CAI gives only July 2025 and Quadricottero no date.", "benefit_type": "Reduced Task Time" } }, "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": "Ivaldo's family obtained recovery of his body and certainty about his fate after ten months of searching. A material benefit, not life-saving, since he had died in September 2024." }, { "group": "organizational", "depth": 2, "reason": "CNSAS Piemonte ended a search that had involved ground teams, Guardia di Finanza and fire-service helicopters for ten months. Manual analysis of the drone photos would have taken weeks (CAI) to months (CNSAS, per bluewin)." } ] } }, "provenance": { "kind": "legacy_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": "ADIAT was built to scan drone photographs and flag color and shape anomalies for search-and-rescue teams. Locating the missing man's helmet 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": "Monviso north face, Perotti couloir (province of Cuneo), Italy", "developer_country": "United States", "deployer_country": "Italy" } }, "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": "ADIAT was built to scan drone photographs and flag color and shape anomalies for search-and-rescue teams. Locating the missing man's helmet 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": "2025-07-31", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the bluewin.ch title to the page's headline, hedged the title's ADIAT attribution to its vendor source, placed the find on the Monviso north face rather than at Pontechianale (only his starting point), removed the unsupported claim that human reviewers had missed the helmet, and noted that the sources differ on the manual-review time (weeks per CAI, months per CNSAS via bluewin), the disappearance date (14 or 15 September 2024) and the source of the 31 July 2025 recovery date.", "source": "https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged with a second write-up of the same event (11 sources and the registry id carried over); the duplicate was removed.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.bluewin.ch/it/attualita/escursionista-scomparso-da-un-anno-ecco-come-e-stato-localizzato-li.2817148; https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Task Time under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://texsar.org/; https://news.cnsas.it/piemonte/", "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": [ "Close-range high-resolution drone photography of steep faces (about 2,600 frames over 183 hectares)", "A target object (red helmet) with strong color contrast against rock and ice", "Search area narrowed by mobile-phone cell data to the Monviso and Visolotto faces" ], "must_hold": [ "Weather and terrain allow drones to fly close to the walls", "The subject or equipment remains visible and not buried by snow", "Rescuers verify AI flags, which CNSAS notes can also fire on plastic or unusual rock color" ], "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": "Corrected the bluewin.ch title to the page's headline, hedged the title's ADIAT attribution to its vendor source, placed the find on the Monviso north face rather than at Pontechianale (only his starting point), removed the unsupported claim that human reviewers had missed the helmet, and noted that the sources differ on the manual-review time (weeks per CAI, months per CNSAS via bluewin), the disappearance date (14 or 15 September 2024) and the source of the 31 July 2025 recovery date.", "source": "https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged with a second write-up of the same event (11 sources and the registry id carried over); the duplicate was removed.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.bluewin.ch/it/attualita/escursionista-scomparso-da-un-anno-ecco-come-e-stato-localizzato-li.2817148; https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Task Time under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://texsar.org/; https://news.cnsas.it/piemonte/", "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": "Mountain search and rescue", "segment": "Recovery of a missing person in high-alpine terrain" } }, "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": "Lo Scarpone, Club Alpino Italiano (2026). Sulla BBC il 'caso Ivaldo': l'Intelligenza Artificiale a supporto del Soccorso Alpino. 18 January 2026 (CAI is the parent body of CNSAS).", "title": "Sulla BBC il 'caso Ivaldo': l'Intelligenza Artificiale a supporto del Soccorso Alpino", "url": "https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "date": "2026-01-18", "written_by": "deployer", "quote": "Due droni sono stati impiegati per voli ravvicinati alle pareti del Monviso, e in 5 ore sono stati raccolti circa 2.600 fotogrammi ad alta risoluzione." }, { "kind": "news report", "citation": "Scarato D (2025). Droni e IA trovano il corpo del medico alpinista disperso da mesi sul Monviso. Quadricottero News, 1 August 2025 (photo credited to Soccorso Alpino e Speleologico Piemonte - CNSAS).", "title": "Droni e IA trovano il corpo del medico alpinista disperso da mesi sul Monviso", "url": "https://www.quadricottero.com/2025/08/droni-e-ia-trovano-il-corpo-del-medico.html", "date": "2025-08-01", "written_by": "independent", "quote": "Il ritrovamento, avvenuto a circa 3150 metri di quota sulla parete nord della montagna, nel canale Perotti, è stato possibile grazie a un impiego mirato di droni professionali" }, { "kind": "news report", "citation": "bluewin.ch (2025). Sulle Alpi Italiane: Ecco com'è stato possibile localizzare un escursionista scomparso da un anno. 10 August 2025 (updated 12 August 2025).", "title": "Ecco com'è stato possibile localizzare un escursionista scomparso da un anno", "url": "https://www.bluewin.ch/it/attualita/escursionista-scomparso-da-un-anno-ecco-come-e-stato-localizzato-li.2817148", "date": "2025-08-10", "written_by": "independent", "quote": "Sono stati questi pixel a cambiare tutto" }, { "kind": "press release", "citation": "Texas Search and Rescue (2025). ADIAT Helps Italian SAR Team. TEXSAR website, 15 August 2025 (developer of ADIAT).", "title": "ADIAT Helps Italian SAR Team", "url": "https://www.texsar.org/2025/08/15/adiat-helps-italian-sar-team/", "date": "2025-08-15", "written_by": "makers", "quote": "It was the AI software that identified some pixels of a different color in the images taken on Tuesday." }, { "kind": "news report", "citation": "Simičević, V. (2026). A red pixel in the snow: How AI solved the mystery of a missing mountaineer. BBC Future.", "title": "A red pixel in the snow: How AI solved the mystery of a missing mountaineer", "url": "https://www.bbc.com/future/article/20260108-how-ai-solved-the-mystery-of-a-missing-mountaineer", "date": "2026-01-09", "written_by": "independent", "quote": "Three days after the search resumed, however, the body of the missing doctor was found at one of the sites identified by the AI, lying in a gully" }, { "kind": "news report", "citation": "La Stampa (2025). Trovato il corpo del medico di Albenga disperso dal 15 settembre sul Monviso. La Stampa, Cuneo.", "title": "Trovato il corpo del medico di Albenga disperso dal 15 settembre sul Monviso", "url": "https://www.lastampa.it/cuneo/2025/07/31/news/trovato_il_corpo_del_medico_di_loano_disperso_dal_15_settembre_sul_monviso-15253571/", "date": "2025-07-31", "written_by": "independent", "quote": "realizzati 2600 fotogrammi che sono stati caricati su un apposito software, in dotazione al Soccorso alpino e speleologico, che ha elaborato i dati indicando tre possibili target da verificare" }, { "kind": "news report", "citation": "Abbà, M. (2025). Il corpo di un escursionista disperso sul Monviso è stato recuperato dopo quasi un anno grazie a un sistema di AI. Wired Italia.", "title": "Il corpo di un escursionista disperso sul Monviso è stato recuperato dopo quasi un anno grazie a un sistema di AI. Che ha riconosciuto il caschetto tra migliaia di immagini", "url": "https://www.wired.it/article/escursionista-disperso-monviso-cadavere-drone-intelligenza-artificiale-soccorso-alpino/", "date": "2025-07-31", "written_by": "independent", "quote": "la salma del medico ligure di 64 anni Nicola Ivaldo è stata trovata nella mattinata di giovedì 31 luglio, a distanza di oltre dieci mesi dalla sua scomparsa" }, { "kind": "news report", "citation": "Abo Abia, M. (2025). Ritrovato il corpo di Nicola Ivaldo, disperso sul Monviso dal settembre 2024. RaiNews, TGR Piemonte.", "title": "Ritrovato il corpo di Nicola Ivaldo, disperso sul Monviso dal settembre 2024", "url": "https://www.rainews.it/tgr/piemonte/articoli/2025/07/ritrovato-il-corpo-di-nicola-ivaldo-disperso-sul-monviso-dal-settembre-2024-d14c11b3-3753-454e-98b4-d8919b1648bc.html", "date": "2025-07-31", "written_by": "independent", "quote": "Il programma, analizzando video e foto, ha individuato tre aree sensibili: zone con dettagli non naturali e incompatibili con l'ambiente circostante" }, { "kind": "press release", "citation": "Lo Scarpone (2025). Monviso, ritrovato il corpo del medico disperso da settembre 2024. Club Alpino Italiano.", "title": "Monviso, ritrovato il corpo del medico disperso da settembre 2024", "url": "https://www.loscarpone.cai.it/dettaglio/monviso-ritrovato-il-corpo-del-medico-disperso-da-settembre-2024/", "date": "2025-07-31", "written_by": "deployer", "quote": "raccolto circa 2600 fotogrammi ad alta risoluzione, poi analizzati tramite un software in dotazione al Soccorso Alpino che ha indicato tre possibili punti di interesse tramite analisi pixel-per-pixel" }, { "kind": "news report", "citation": "Ciprian, V. (2026). Quel punto rosso era il casco dell'alpinista disperso da mesi. L'AltraMontagna, il Dolomiti.", "title": "Quel punto rosso era il casco dell'alpinista disperso da mesi: \"Senza l'anomalia segnalata dall'intelligenza artificiale in una delle 2600 foto dal drone, probabilmente non sarebbe mai stato trovato\"", "url": "https://www.ildolomiti.it/altra-montagna/storie/2026/quel-punto-rosso-era-il-casco-dellalpinista-disperso-da-mesi-senza-lanomalia-segnalata-dallintelligenza-artificiale-in-una-delle-2600-foto-dal-drone-probabilmente-non-sarebbe-mai-stato-trovato", "date": "2026-01-13", "written_by": "independent", "quote": "il drone, penetrando nel canale Perotti, ha individuato i resti di un essere umano nel canale più a destra dei tre che solcano la parete nord" }, { "kind": "news report", "citation": "Rullo, F. (2025). Monviso, ritrovato dopo un anno e solo grazie ai droni il corpo di Nicola Ivaldo, medico savonese scomparso in montagna. Corriere della Sera, Torino.", "title": "Monviso, ritrovato dopo un anno e solo grazie ai droni il corpo di Nicola Ivaldo, medico savonese scomparso in montagna", "url": "https://torino.corriere.it/notizie/piemonte/25_luglio_31/monviso-ritrovato-dopo-un-anno-e-solo-grazie-ai-droni-il-corpo-di-nicola-ivaldo-medico-savonese-scomparso-in-montagna-c943f224-5e04-4320-856b-a3d4533caxlk.shtml", "date": "2025-07-31", "written_by": "independent", "quote": "ritrovati grazie all'uso di droni e a un apposito software in dotazione al soccorso alpino: erano in un canale a 3150 metri di quota" }, { "kind": "news report", "citation": "Annapurna, K. (2025). AI Solves the Mystery of Missing Italian Climber. ExplorersWeb.", "title": "AI Solves the Mystery of Missing Italian Climber", "url": "https://explorersweb.com/ai-solves-the-mystery-of-missing-italian-climber/", "date": "2025-08-07", "written_by": "independent", "quote": "an AI software analyzed these many images in a single afternoon, singling out a cluster of red pixels that turned out to be Ivaldo's helmet" }, { "kind": "news report", "citation": "Malfatti, M. (2025). Anche l'intelligenza artificiale al servizio del Soccorso Alpino. Montagna.TV.", "title": "Anche l'intelligenza artificiale al servizio del Soccorso Alpino", "url": "https://www.montagna.tv/262960/anche-lintelligenza-artificiale-al-servizio-del-soccorso-alpino/", "date": "2025-10-06", "written_by": "independent", "quote": "l'analisi automatica e dettagliata di migliaia di fotogrammi ha permesso di isolare potenziali target in un'area decisamente vasta ed impervia" }, { "kind": "news report", "citation": "Corriere della Sera Torino (2024). Disperso sul Monviso da sabato Nicola Ivaldo, 64enne chirurgo ortopedico di Loano. Corriere.it.", "title": "Disperso sul Monviso da sabato Nicola Ivaldo, 64enne chirurgo ortopedico di Loano. Lavorava ad Acqui Terme", "url": "https://torino.corriere.it/notizie/piemonte/24_settembre_17/disperso-sul-monviso-da-sabato-ha-parcheggiato-l-auto-alla-diga-di-pontechianale-poi-nessuna-traccia-89019eb1-5c94-4928-a627-187960555xlk.shtml", "date": "2024-09-17", "written_by": "independent", "quote": "presumibilmente è partito a piedi nella giornata di sabato dal parcheggio della diga di Pontechianale, nel cuneese, dove è ancora presente la sua auto" }, { "kind": "other", "citation": "OECD.AI (2026). AI-Assisted Drone Search Locates Missing Mountaineer in Italian Alps. OECD AI Incidents and Hazards Monitor.", "title": "AI-Assisted Drone Search Locates Missing Mountaineer in Italian Alps", "url": "https://oecd.ai/en/incidents/2026-01-10-f4af", "date": "2026-01-10", "written_by": "independent", "quote": "AI software analyzing drone images identified the body of missing Italian mountaineer Nicola Ivaldo in the Piedmont Alps" } ] } }, "provenance": { "kind": "legacy_record", "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-0033-source-0001", "AIBD-0033-source-0002", "AIBD-0033-source-0003", "AIBD-0033-source-0004", "AIBD-0033-source-0005", "AIBD-0033-source-0006", "AIBD-0033-source-0007", "AIBD-0033-source-0008", "AIBD-0033-source-0009", "AIBD-0033-source-0010", "AIBD-0033-source-0011", "AIBD-0033-source-0012", "AIBD-0033-source-0013", "AIBD-0033-source-0014", "AIBD-0033-source-0015" ], "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": "Lo Scarpone, Club Alpino Italiano (2026). Sulla BBC il 'caso Ivaldo': l'Intelligenza Artificiale a supporto del Soccorso Alpino. 18 January 2026 (CAI is the parent body of CNSAS).", "title": "Sulla BBC il 'caso Ivaldo': l'Intelligenza Artificiale a supporto del Soccorso Alpino", "url": "https://www.loscarpone.cai.it/dettaglio/sulla-bbc-il-caso-ivaldo-lintelligenza-artificiale-a-supporto-del-soccorso-alpino/", "date": "2026-01-18", "written_by": "deployer", "quote": "Due droni sono stati impiegati per voli ravvicinati alle pareti del Monviso, e in 5 ore sono stati raccolti circa 2.600 fotogrammi ad alta risoluzione." }, { "kind": "news report", "citation": "Scarato D (2025). Droni e IA trovano il corpo del medico alpinista disperso da mesi sul Monviso. Quadricottero News, 1 August 2025 (photo credited to Soccorso Alpino e Speleologico Piemonte - CNSAS).", "title": "Droni e IA trovano il corpo del medico alpinista disperso da mesi sul Monviso", "url": "https://www.quadricottero.com/2025/08/droni-e-ia-trovano-il-corpo-del-medico.html", "date": "2025-08-01", "written_by": "independent", "quote": "Il ritrovamento, avvenuto a circa 3150 metri di quota sulla parete nord della montagna, nel canale Perotti, è stato possibile grazie a un impiego mirato di droni professionali" }, { "kind": "news report", "citation": "bluewin.ch (2025). Sulle Alpi Italiane: Ecco com'è stato possibile localizzare un escursionista scomparso da un anno. 10 August 2025 (updated 12 August 2025).", "title": "Ecco com'è stato possibile localizzare un escursionista scomparso da un anno", "url": "https://www.bluewin.ch/it/attualita/escursionista-scomparso-da-un-anno-ecco-come-e-stato-localizzato-li.2817148", "date": "2025-08-10", "written_by": "independent", "quote": "Sono stati questi pixel a cambiare tutto" }, { "kind": "press release", "citation": "Texas Search and Rescue (2025). ADIAT Helps Italian SAR Team. TEXSAR website, 15 August 2025 (developer of ADIAT).", "title": "ADIAT Helps Italian SAR Team", "url": "https://www.texsar.org/2025/08/15/adiat-helps-italian-sar-team/", "date": "2025-08-15", "written_by": "makers", "quote": "It was the AI software that identified some pixels of a different color in the images taken on Tuesday." }, { "kind": "news report", "citation": "Simičević, V. (2026). A red pixel in the snow: How AI solved the mystery of a missing mountaineer. BBC Future.", "title": "A red pixel in the snow: How AI solved the mystery of a missing mountaineer", "url": "https://www.bbc.com/future/article/20260108-how-ai-solved-the-mystery-of-a-missing-mountaineer", "date": "2026-01-09", "written_by": "independent", "quote": "Three days after the search resumed, however, the body of the missing doctor was found at one of the sites identified by the AI, lying in a gully" }, { "kind": "news report", "citation": "La Stampa (2025). Trovato il corpo del medico di Albenga disperso dal 15 settembre sul Monviso. La Stampa, Cuneo.", "title": "Trovato il corpo del medico di Albenga disperso dal 15 settembre sul Monviso", "url": "https://www.lastampa.it/cuneo/2025/07/31/news/trovato_il_corpo_del_medico_di_loano_disperso_dal_15_settembre_sul_monviso-15253571/", "date": "2025-07-31", "written_by": "independent", "quote": "realizzati 2600 fotogrammi che sono stati caricati su un apposito software, in dotazione al Soccorso alpino e speleologico, che ha elaborato i dati indicando tre possibili target da verificare" }, { "kind": "news report", "citation": "Abbà, M. (2025). Il corpo di un escursionista disperso sul Monviso è stato recuperato dopo quasi un anno grazie a un sistema di AI. Wired Italia.", "title": "Il corpo di un escursionista disperso sul Monviso è stato recuperato dopo quasi un anno grazie a un sistema di AI. Che ha riconosciuto il caschetto tra migliaia di immagini", "url": "https://www.wired.it/article/escursionista-disperso-monviso-cadavere-drone-intelligenza-artificiale-soccorso-alpino/", "date": "2025-07-31", "written_by": "independent", "quote": "la salma del medico ligure di 64 anni Nicola Ivaldo è stata trovata nella mattinata di giovedì 31 luglio, a distanza di oltre dieci mesi dalla sua scomparsa" }, { "kind": "news report", "citation": "Abo Abia, M. (2025). Ritrovato il corpo di Nicola Ivaldo, disperso sul Monviso dal settembre 2024. RaiNews, TGR Piemonte.", "title": "Ritrovato il corpo di Nicola Ivaldo, disperso sul Monviso dal settembre 2024", "url": "https://www.rainews.it/tgr/piemonte/articoli/2025/07/ritrovato-il-corpo-di-nicola-ivaldo-disperso-sul-monviso-dal-settembre-2024-d14c11b3-3753-454e-98b4-d8919b1648bc.html", "date": "2025-07-31", "written_by": "independent", "quote": "Il programma, analizzando video e foto, ha individuato tre aree sensibili: zone con dettagli non naturali e incompatibili con l'ambiente circostante" }, { "kind": "press release", "citation": "Lo Scarpone (2025). Monviso, ritrovato il corpo del medico disperso da settembre 2024. Club Alpino Italiano.", "title": "Monviso, ritrovato il corpo del medico disperso da settembre 2024", "url": "https://www.loscarpone.cai.it/dettaglio/monviso-ritrovato-il-corpo-del-medico-disperso-da-settembre-2024/", "date": "2025-07-31", "written_by": "deployer", "quote": "raccolto circa 2600 fotogrammi ad alta risoluzione, poi analizzati tramite un software in dotazione al Soccorso Alpino che ha indicato tre possibili punti di interesse tramite analisi pixel-per-pixel" }, { "kind": "news report", "citation": "Ciprian, V. (2026). Quel punto rosso era il casco dell'alpinista disperso da mesi. L'AltraMontagna, il Dolomiti.", "title": "Quel punto rosso era il casco dell'alpinista disperso da mesi: \"Senza l'anomalia segnalata dall'intelligenza artificiale in una delle 2600 foto dal drone, probabilmente non sarebbe mai stato trovato\"", "url": "https://www.ildolomiti.it/altra-montagna/storie/2026/quel-punto-rosso-era-il-casco-dellalpinista-disperso-da-mesi-senza-lanomalia-segnalata-dallintelligenza-artificiale-in-una-delle-2600-foto-dal-drone-probabilmente-non-sarebbe-mai-stato-trovato", "date": "2026-01-13", "written_by": "independent", "quote": "il drone, penetrando nel canale Perotti, ha individuato i resti di un essere umano nel canale più a destra dei tre che solcano la parete nord" }, { "kind": "news report", "citation": "Rullo, F. (2025). Monviso, ritrovato dopo un anno e solo grazie ai droni il corpo di Nicola Ivaldo, medico savonese scomparso in montagna. Corriere della Sera, Torino.", "title": "Monviso, ritrovato dopo un anno e solo grazie ai droni il corpo di Nicola Ivaldo, medico savonese scomparso in montagna", "url": "https://torino.corriere.it/notizie/piemonte/25_luglio_31/monviso-ritrovato-dopo-un-anno-e-solo-grazie-ai-droni-il-corpo-di-nicola-ivaldo-medico-savonese-scomparso-in-montagna-c943f224-5e04-4320-856b-a3d4533caxlk.shtml", "date": "2025-07-31", "written_by": "independent", "quote": "ritrovati grazie all'uso di droni e a un apposito software in dotazione al soccorso alpino: erano in un canale a 3150 metri di quota" }, { "kind": "news report", "citation": "Annapurna, K. (2025). AI Solves the Mystery of Missing Italian Climber. ExplorersWeb.", "title": "AI Solves the Mystery of Missing Italian Climber", "url": "https://explorersweb.com/ai-solves-the-mystery-of-missing-italian-climber/", "date": "2025-08-07", "written_by": "independent", "quote": "an AI software analyzed these many images in a single afternoon, singling out a cluster of red pixels that turned out to be Ivaldo's helmet" }, { "kind": "news report", "citation": "Malfatti, M. (2025). Anche l'intelligenza artificiale al servizio del Soccorso Alpino. Montagna.TV.", "title": "Anche l'intelligenza artificiale al servizio del Soccorso Alpino", "url": "https://www.montagna.tv/262960/anche-lintelligenza-artificiale-al-servizio-del-soccorso-alpino/", "date": "2025-10-06", "written_by": "independent", "quote": "l'analisi automatica e dettagliata di migliaia di fotogrammi ha permesso di isolare potenziali target in un'area decisamente vasta ed impervia" }, { "kind": "news report", "citation": "Corriere della Sera Torino (2024). Disperso sul Monviso da sabato Nicola Ivaldo, 64enne chirurgo ortopedico di Loano. Corriere.it.", "title": "Disperso sul Monviso da sabato Nicola Ivaldo, 64enne chirurgo ortopedico di Loano. Lavorava ad Acqui Terme", "url": "https://torino.corriere.it/notizie/piemonte/24_settembre_17/disperso-sul-monviso-da-sabato-ha-parcheggiato-l-auto-alla-diga-di-pontechianale-poi-nessuna-traccia-89019eb1-5c94-4928-a627-187960555xlk.shtml", "date": "2024-09-17", "written_by": "independent", "quote": "presumibilmente è partito a piedi nella giornata di sabato dal parcheggio della diga di Pontechianale, nel cuneese, dove è ancora presente la sua auto" }, { "kind": "other", "citation": "OECD.AI (2026). AI-Assisted Drone Search Locates Missing Mountaineer in Italian Alps. OECD AI Incidents and Hazards Monitor.", "title": "AI-Assisted Drone Search Locates Missing Mountaineer in Italian Alps", "url": "https://oecd.ai/en/incidents/2026-01-10-f4af", "date": "2026-01-10", "written_by": "independent", "quote": "AI software analyzing drone images identified the body of missing Italian mountaineer Nicola Ivaldo in the Piedmont Alps" } ] } }, "provenance": { "kind": "legacy_record", "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 analysis of 2,600 drone frames flagged a red helmet in the Perotti couloir at 3,150 m; Ivaldo's body was recovered by 31 July 2025.", "account": "Nicola Ivaldo was a surgeon aged 64 according to the Club Alpino Italiano's Lo Scarpone (another report says 66). He disappeared on 14 September 2024 per CAI during a solo outing on Monviso in Piedmont, Italy. Quadricottero dates his departure, when he parked his car at the Pontechianale dam, to 15 September. Searches over ten months found nothing. On 29 July 2025 the Soccorso Alpino e Speleologico Piemontese (CNSAS) flew two drones close to the north face. They collected about 2,600 high-resolution frames over 183 hectares in five hours. The frames were analyzed pixel by pixel with AI image-analysis software. Developer TEXSAR identifies it as the open-source Automated Drone Image Analysis Tool (ADIAT). The frames yielded a tiny red anomaly in the Perotti couloir: his helmet. His body was located at about 3,150 m and recovered by 31 July 2025. That date comes from TEXSAR's page (which cites Wired). It is consistent with bluewin's report of under 72 hours from the 29 July flight to the recovery. CAI gives only July 2025 and Quadricottero no 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": "AI drone-image analysis software used by CNSAS Piemonte (identified as ADIAT by its developer, TEXSAR; CAI and CNSAS sources do not name 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": "TEXSAR (Texas Search and Rescue) volunteers, per the developer's own account" } }, "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": "CNSAS Piemonte drone imagery analyzed with AI software, likely TEXSAR's ADIAT, locates missing surgeon Nicola Ivaldo on Monviso after ten months", "headline": "ADIAT spots a red helmet and locates Nicola Ivaldo on Monviso" } }, "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": [ "104bc06c9ff35eb203cd8b36ceced9dbff314ff19228b999bc38a5aecf7b423a" ], "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": "Nicola Ivaldo was a surgeon aged 64 according to the Club Alpino Italiano's Lo Scarpone (another report says 66). He disappeared on 14 September 2024 per CAI during a solo outing on Monviso in Piedmont, Italy. Quadricottero dates his departure, when he parked his car at the Pontechianale dam, to 15 September. Searches over ten months found nothing. On 29 July 2025 the Soccorso Alpino e Speleologico Piemontese (CNSAS) flew two drones close to the north face. They collected about 2,600 high-resolution frames over 183 hectares in five hours. The frames were analyzed pixel by pixel with AI image-analysis software. Developer TEXSAR identifies it as the open-source Automated Drone Image Analysis Tool (ADIAT). The frames yielded a tiny red anomaly in the Perotti couloir: his helmet. His body was located at about 3,150 m and recovered by 31 July 2025. That date comes from TEXSAR's page (which cites Wired). It is consistent with bluewin's report of under 72 hours from the 29 July flight to the recovery. CAI gives only July 2025 and Quadricottero no date.", "replication": { "produced_by": [ "Close-range high-resolution drone photography of steep faces (about 2,600 frames over 183 hectares)", "A target object (red helmet) with strong color contrast against rock and ice", "Search area narrowed by mobile-phone cell data to the Monviso and Visolotto faces" ], "must_hold": [ "Weather and terrain allow drones to fly close to the walls", "The subject or equipment remains visible and not buried by snow", "Rescuers verify AI flags, which CNSAS notes can also fire on plastic or unusual rock color" ], "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-0349", "case_url": "records/AIBD-0349.html", "reason": "The developer is headquartered in United States; the case names Turkey, Greece, Romania, Bulgaria.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Turkey, Greece, Romania, Bulgaria" }, { "label": "Case location", "value": "Sea of Marmara / Istanbul region, Türkiye (alerts also in Greece, Romania and Bulgaria)" } ], "filters": { "origin": "United States", "destination": [ "Turkey", "Greece", "Romania", "Bulgaria" ] }, "record": { "id": "AIBD-0349", "title": "Google's Android Earthquake Alerts detects the Mw 6.2 Marmara Ereğlisi earthquake 5.31 s after origin and alerts over 16 million users on 23 April 2025; evaluation is Google-authored", "summary": "AEA detected the 23 April 2025 Mw 6.2 Marmara Ereğlisi quake in 5.31 s and alerted 16 million+ users (median 56 s warning for weak shaking); Google-authored evaluation; inland Take Action shortfall.", "date_observed": "2025-04-23", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 23 April 2025 the Mw 6.2 Marmara Ereğlisi earthquake struck offshore in the Sea of Marmara, Türkiye. Initial reports cited by the evaluation put shaking in Istanbul at MMI V. At least 359 people were injured, mostly from panic. Google's Android Earthquake Alerts (AEA) system uses the accelerometers of online Android phones. An on-phone detection algorithm flags sudden changes in acceleration. The system detected the event 5.31 seconds after its origin. It issued a first “Be Aware” alert 0.43 seconds later on an initial magnitude estimate of Mw 4.83. “Take Action” alerts followed as the estimate rose. Alerts reached more than 16 million Android users in Türkiye, Greece, Romania and Bulgaria. Warning times for weak shaking (MMI III) reached up to 150 seconds with a median of 56 seconds. The authors report 90% true positives and 99% precision. The final magnitude estimate saturated at Mw 6.04, so the “Take Action” polygon was too small. Inland populations that felt MMI V shaking received only the “Be Aware” warning before stronger shaking arrived. The evaluation (Nature Communications, 11 August 2026) and the system description (Science, 17 July 2025) are written by Google employees. So is the user-feedback data. The same system underestimated the 6 February 2023 Türkiye earthquakes. The BBC reported on 27 July 2025 that only 469 “Take Action” alerts were sent for the M7.8 event. 10 million people could have received one.", "category": "protective response", "unexpected": false, "purpose": "Crowd-sourced earthquake detection and early warning delivered to Android phones in countries without dense seismic networks. The alerts were the intended use.", "system": "Android Earthquake Alerts (AEA) system", "model_version": null, "developer": "Google", "deployer": "Google (alerts delivered directly to Android users; launched in Türkiye in June 2021)", "location": "Sea of Marmara / Istanbul region, Türkiye (alerts also in Greece, Romania and Bulgaria)", "domain": "Earthquake early warning / public safety", "segment": "Consumer operating-system safety feature", "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "depth": null, "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ], "ai_role": { "status": "assessed", "statement": "The system itself generated the detection and the alerts. A few hundred phones triggered on the P wave. The system detected the event 5.31 s after origin and issued alerts within 0.43 s. Magnitude estimates were updated over the next 12 s. All performance figures and user-response data come from Google-authored papers (Mousavi et al. 2026; Allen et al. 2025). That is said plainly here. The only independent cited source, the BBC, concerns the 2023 underestimation. The read papers describe the detection as an on-phone algorithm that analyzes acceleration time series for sudden changes. They do not use the terms machine learning or artificial intelligence. Whether the system uses AI is not established by the sources." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., Barski, A., Bosch, R., Thiruverahan, N., Cho, Y., Gadh, T., Malkos, S., Spooner, B., Wimpey, G., Shi, E., Stogaitis, M. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Authors affiliated with Google LLC and Google Germany GmbH.", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake", "url": "https://doi.org/10.1038/s41467-026-76413-7", "date": "2026-08-11", "written_by": "makers", "quote": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, abstract, warning times.", "title": "Performance and user response of android's smartphone-based alerts (warning times)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Methods, detection and alerting.", "title": "Performance and user response of android's smartphone-based alerts (detection method)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "An on-phone detection algorithm analyzes acceleration time series for sudden changes indicative of P- or S-wave arrivals." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Results, alert performance.", "title": "Performance and user response of android's smartphone-based alerts (Take Action shortfall)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Consequently, inland populations who experienced moderate ground motions (MMI V) did not receive a “Take Action” alert before the arrival of the stronger shaking." }, { "kind": "peer-reviewed study", "citation": "Allen, R. M., Barski, A., Berman, M., Bosch, R., Cho, Y., Jiang, X. S., Lee, Y. L., Malkos, S., Mousavi, S. M., Robertson, P., Spooner, B., Stogaitis, M., Thiruverahan, N., Wimpey, G. (2025). Global earthquake detection and warning using Android phones. Science, 17 July 2025. doi:10.1126/science.ads4779 (Google-authored).", "title": "Global earthquake detection and warning using Android phones", "url": "https://europepmc.org/abstract/MED/40674485", "date": "2025-07-17", "written_by": "makers", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Google failed to warn 10 million of Turkey earthquake severity. BBC News, 27 July 2025.", "title": "Google failed to warn 10 million of Turkey earthquake severity", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "Instead, only 469 \"Take Action\" warnings were sent out for the first 7.8 magnitude quake." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Same article, magnitude estimate.", "title": "Google failed to warn 10 million of Turkey earthquake severity (magnitude estimate)", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "For the first earthquake, the system estimated the shaking at between 4.5 and 4.9 on the moment magnitude scale (MMS) when it was actually a 7.8." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., et al. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Full text as served by the Europe PMC REST API (PMC13558587).", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "date": "2026-08-11", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Dense network of online Android phones (about 66,000 used for characterization) around an offshore epicentre", "On-phone trigger on P-wave acceleration and server-side magnitude estimation updated over 12 s", "Two-tier alerting (Be Aware, Take Action) delivered through the operating system", "A population with high earthquake awareness after the 2023 Kahramanmaraş disaster, per the authors" ], "must_hold": [ "Enough stationary, charging phones near the epicentre to trigger early", "Magnitude estimates that do not saturate below the true magnitude. This failed here at Mw 6.04 and failed badly in 2023.", "Alerts that outrun S-wave arrival at the populated distance", "Users who recognize and act on the alert" ], "echoes": [ { "date": "2023-02-06", "setting": "Mw 7.8 and Mw 7.5 earthquakes, south-east Türkiye: magnitude estimated at 4.5–4.9; 469 Take Action alerts sent for the first quake when 10 million people could have received one (BBC)", "model_version": null, "outcome": "did not recur", "source": "https://www.bbc.com/news/articles/c77v2kx304go" }, { "date": null, "setting": "Global operation over three years: alerts in 98 countries, about 60 events and 18 million alerts per month; 85% of alert recipients felt shaking (Science 2025, Google-authored)", "model_version": null, "outcome": "recurred", "source": "https://europepmc.org/abstract/MED/40674485" } ] }, "paired_incident": null, "headline": "Google's AEA alerts 16 million+ users to Marmara Ereğlisi quake", "metric": "alerted 16 million+ users (median 56 s warning for weak shaking)", "comparator": { "against": "local seismic networks (AFAD stations)", "before": null, "after": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users.", "basis": "measured", "quote": "A key finding was the earlier detection of seismic signals by smartphone accelerometers compared to the wavefield’s arrival at local seismic networks in this specific geography.", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML" }, "later": { "status": "persisted", "date": "2025-09-16", "note": "Google reports the system still operating in Türkiye after the event, with more than 116 million alerts issued for 203 detected events since June 2021 as of 16 September 2025.", "quote": "Since AEA’s launch in Türkiye in June 2021, more than 116 million alerts have been issued for 203 detected events in the country (as of Sep 16, 2025)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and four peer-reviewed studies from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0349", "catalog_version": "1.0.0", "source_fingerprint": "5d5b66e01eda69fa53abd5912285d70540631339d1048ba27dde67b5a1cf8ba7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0349-source-0001", "AIBD-0349-source-0002", "AIBD-0349-source-0003", "AIBD-0349-source-0004", "AIBD-0349-source-0005", "AIBD-0349-source-0006", "AIBD-0349-source-0007", "AIBD-0349-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Google", "deployer": "Google (alerts delivered directly to Android users; launched in Türkiye in June 2021)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The system itself generated the detection and the alerts. A few hundred phones triggered on the P wave. The system detected the event 5.31 s after origin and issued alerts within 0.43 s. Magnitude estimates were updated over the next 12 s. All performance figures and user-response data come from Google-authored papers (Mousavi et al. 2026; Allen et al. 2025). That is said plainly here. The only independent cited source, the BBC, concerns the 2023 underestimation. The read papers describe the detection as an on-phone algorithm that analyzes acceleration time series for sudden changes. They do not use the terms machine learning or artificial intelligence. Whether the system uses AI is not established by the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Android Earthquake Alerts detected the earthquake from phone accelerometers and issued warnings. The authors report detection 5.31 seconds after the earthquake began and an alert within a further 0.43 seconds.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The system itself generated the detection and the alerts. A few hundred phones triggered on the P wave. The system detected the event 5.31 s after origin and issued alerts within 0.43 s. Magnitude estimates were updated over the next 12 s. All performance figures and user-response data come from Google-authored papers (Mousavi et al. 2026; Allen et al. 2025). That is said plainly here. The only independent cited source, the BBC, concerns the 2023 underestimation. The read papers describe the detection as an on-phone algorithm that analyzes acceleration time series for sudden changes. They do not use the terms machine learning or artificial intelligence. Whether the system uses AI is not established by the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and four peer-reviewed studies from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "alerted 16 million+ users (median 56 s warning for weak shaking)", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Google-authored papers supply the performance figures and user responses. The sources describe an algorithm that detects changes in acceleration but do not establish that it uses machine learning. The independent BBC report concerns a different earthquake.", "editor_note": "" } }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2025-09-16", "note": "Google reports the system still operating in Türkiye after the event, with more than 116 million alerts issued for 203 detected events since June 2021 as of 16 September 2025.", "quote": "Since AEA’s launch in Türkiye in June 2021, more than 116 million alerts have been issued for 203 detected events in the country (as of Sep 16, 2025)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0349" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 23 April 2025 the Mw 6.2 Marmara Ereğlisi earthquake struck offshore in the Sea of Marmara, Türkiye. Initial reports cited by the evaluation put shaking in Istanbul at MMI V. At least 359 people were injured, mostly from panic. Google's Android Earthquake Alerts (AEA) system uses the accelerometers of online Android phones. An on-phone detection algorithm flags sudden changes in acceleration. The system detected the event 5.31 seconds after its origin. It issued a first “Be Aware” alert 0.43 seconds later on an initial magnitude estimate of Mw 4.83. “Take Action” alerts followed as the estimate rose. Alerts reached more than 16 million Android users in Türkiye, Greece, Romania and Bulgaria. Warning times for weak shaking (MMI III) reached up to 150 seconds with a median of 56 seconds. The authors report 90% true positives and 99% precision. The final magnitude estimate saturated at Mw 6.04, so the “Take Action” polygon was too small. Inland populations that felt MMI V shaking received only the “Be Aware” warning before stronger shaking arrived. The evaluation (Nature Communications, 11 August 2026) and the system description (Science, 17 July 2025) are written by Google employees. So is the user-feedback data. The same system underestimated the 6 February 2023 Türkiye earthquakes. The BBC reported on 27 July 2025 that only 469 “Take Action” alerts were sent for the M7.8 event. 10 million people could have received one.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Crowd-sourced earthquake detection and early warning delivered to Android phones in countries without dense seismic networks. The alerts were the intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Sea of Marmara / Istanbul region, Türkiye (alerts also in Greece, Romania and Bulgaria)", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Crowd-sourced earthquake detection and early warning delivered to Android phones in countries without dense seismic networks. The alerts were the intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-04-23", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Dense network of online Android phones (about 66,000 used for characterization) around an offshore epicentre", "On-phone trigger on P-wave acceleration and server-side magnitude estimation updated over 12 s", "Two-tier alerting (Be Aware, Take Action) delivered through the operating system", "A population with high earthquake awareness after the 2023 Kahramanmaraş disaster, per the authors" ], "must_hold": [ "Enough stationary, charging phones near the epicentre to trigger early", "Magnitude estimates that do not saturate below the true magnitude. This failed here at Mw 6.04 and failed badly in 2023.", "Alerts that outrun S-wave arrival at the populated distance", "Users who recognize and act on the alert" ], "echoes": [ { "date": "2023-02-06", "setting": "Mw 7.8 and Mw 7.5 earthquakes, south-east Türkiye: magnitude estimated at 4.5–4.9; 469 Take Action alerts sent for the first quake when 10 million people could have received one (BBC)", "outcome": "did not recur", "source": "https://www.bbc.com/news/articles/c77v2kx304go" }, { "setting": "Global operation over three years: alerts in 98 countries, about 60 events and 18 million alerts per month; 85% of alert recipients felt shaking (Science 2025, Google-authored)", "outcome": "recurred", "source": "https://europepmc.org/abstract/MED/40674485" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Dense network of online Android phones (about 66,000 used for characterization) around an offshore epicentre", "On-phone trigger on P-wave acceleration and server-side magnitude estimation updated over 12 s", "Two-tier alerting (Be Aware, Take Action) delivered through the operating system", "A population with high earthquake awareness after the 2023 Kahramanmaraş disaster, per the authors" ], "must_hold": [ "Enough stationary, charging phones near the epicentre to trigger early", "Magnitude estimates that do not saturate below the true magnitude. This failed here at Mw 6.04 and failed badly in 2023.", "Alerts that outrun S-wave arrival at the populated distance", "Users who recognize and act on the alert" ], "echoes": [ { "date": "2023-02-06", "setting": "Mw 7.8 and Mw 7.5 earthquakes, south-east Türkiye: magnitude estimated at 4.5–4.9; 469 Take Action alerts sent for the first quake when 10 million people could have received one (BBC)", "outcome": "did not recur", "source": "https://www.bbc.com/news/articles/c77v2kx304go" }, { "setting": "Global operation over three years: alerts in 98 countries, about 60 events and 18 million alerts per month; 85% of alert recipients felt shaking (Science 2025, Google-authored)", "outcome": "recurred", "source": "https://europepmc.org/abstract/MED/40674485" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Earthquake early warning / public safety", "segment": "Consumer operating-system safety feature" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., Barski, A., Bosch, R., Thiruverahan, N., Cho, Y., Gadh, T., Malkos, S., Spooner, B., Wimpey, G., Shi, E., Stogaitis, M. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Authors affiliated with Google LLC and Google Germany GmbH.", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake", "url": "https://doi.org/10.1038/s41467-026-76413-7", "date": "2026-08-11", "written_by": "makers", "quote": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, abstract, warning times.", "title": "Performance and user response of android's smartphone-based alerts (warning times)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Methods, detection and alerting.", "title": "Performance and user response of android's smartphone-based alerts (detection method)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "An on-phone detection algorithm analyzes acceleration time series for sudden changes indicative of P- or S-wave arrivals." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Results, alert performance.", "title": "Performance and user response of android's smartphone-based alerts (Take Action shortfall)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Consequently, inland populations who experienced moderate ground motions (MMI V) did not receive a “Take Action” alert before the arrival of the stronger shaking." }, { "kind": "peer-reviewed study", "citation": "Allen, R. M., Barski, A., Berman, M., Bosch, R., Cho, Y., Jiang, X. S., Lee, Y. L., Malkos, S., Mousavi, S. M., Robertson, P., Spooner, B., Stogaitis, M., Thiruverahan, N., Wimpey, G. (2025). Global earthquake detection and warning using Android phones. Science, 17 July 2025. doi:10.1126/science.ads4779 (Google-authored).", "title": "Global earthquake detection and warning using Android phones", "url": "https://europepmc.org/abstract/MED/40674485", "date": "2025-07-17", "written_by": "makers", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Google failed to warn 10 million of Turkey earthquake severity. BBC News, 27 July 2025.", "title": "Google failed to warn 10 million of Turkey earthquake severity", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "Instead, only 469 \"Take Action\" warnings were sent out for the first 7.8 magnitude quake." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Same article, magnitude estimate.", "title": "Google failed to warn 10 million of Turkey earthquake severity (magnitude estimate)", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "For the first earthquake, the system estimated the shaking at between 4.5 and 4.9 on the moment magnitude scale (MMS) when it was actually a 7.8." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., et al. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Full text as served by the Europe PMC REST API (PMC13558587).", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "date": "2026-08-11", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0349-source-0001", "AIBD-0349-source-0002", "AIBD-0349-source-0003", "AIBD-0349-source-0004", "AIBD-0349-source-0005", "AIBD-0349-source-0006", "AIBD-0349-source-0007", "AIBD-0349-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., Barski, A., Bosch, R., Thiruverahan, N., Cho, Y., Gadh, T., Malkos, S., Spooner, B., Wimpey, G., Shi, E., Stogaitis, M. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Authors affiliated with Google LLC and Google Germany GmbH.", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake", "url": "https://doi.org/10.1038/s41467-026-76413-7", "date": "2026-08-11", "written_by": "makers", "quote": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, abstract, warning times.", "title": "Performance and user response of android's smartphone-based alerts (warning times)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Methods, detection and alerting.", "title": "Performance and user response of android's smartphone-based alerts (detection method)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "An on-phone detection algorithm analyzes acceleration time series for sudden changes indicative of P- or S-wave arrivals." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Results, alert performance.", "title": "Performance and user response of android's smartphone-based alerts (Take Action shortfall)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Consequently, inland populations who experienced moderate ground motions (MMI V) did not receive a “Take Action” alert before the arrival of the stronger shaking." }, { "kind": "peer-reviewed study", "citation": "Allen, R. M., Barski, A., Berman, M., Bosch, R., Cho, Y., Jiang, X. S., Lee, Y. L., Malkos, S., Mousavi, S. M., Robertson, P., Spooner, B., Stogaitis, M., Thiruverahan, N., Wimpey, G. (2025). Global earthquake detection and warning using Android phones. Science, 17 July 2025. doi:10.1126/science.ads4779 (Google-authored).", "title": "Global earthquake detection and warning using Android phones", "url": "https://europepmc.org/abstract/MED/40674485", "date": "2025-07-17", "written_by": "makers", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Google failed to warn 10 million of Turkey earthquake severity. BBC News, 27 July 2025.", "title": "Google failed to warn 10 million of Turkey earthquake severity", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "Instead, only 469 \"Take Action\" warnings were sent out for the first 7.8 magnitude quake." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Same article, magnitude estimate.", "title": "Google failed to warn 10 million of Turkey earthquake severity (magnitude estimate)", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "For the first earthquake, the system estimated the shaking at between 4.5 and 4.9 on the moment magnitude scale (MMS) when it was actually a 7.8." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., et al. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Full text as served by the Europe PMC REST API (PMC13558587).", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "date": "2026-08-11", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "AEA detected the 23 April 2025 Mw 6.2 Marmara Ereğlisi quake in 5.31 s and alerted 16 million+ users (median 56 s warning for weak shaking); Google-authored evaluation; inland Take Action shortfall.", "account": "On 23 April 2025 the Mw 6.2 Marmara Ereğlisi earthquake struck offshore in the Sea of Marmara, Türkiye. Initial reports cited by the evaluation put shaking in Istanbul at MMI V. At least 359 people were injured, mostly from panic. Google's Android Earthquake Alerts (AEA) system uses the accelerometers of online Android phones. An on-phone detection algorithm flags sudden changes in acceleration. The system detected the event 5.31 seconds after its origin. It issued a first “Be Aware” alert 0.43 seconds later on an initial magnitude estimate of Mw 4.83. “Take Action” alerts followed as the estimate rose. Alerts reached more than 16 million Android users in Türkiye, Greece, Romania and Bulgaria. Warning times for weak shaking (MMI III) reached up to 150 seconds with a median of 56 seconds. The authors report 90% true positives and 99% precision. The final magnitude estimate saturated at Mw 6.04, so the “Take Action” polygon was too small. Inland populations that felt MMI V shaking received only the “Be Aware” warning before stronger shaking arrived. The evaluation (Nature Communications, 11 August 2026) and the system description (Science, 17 July 2025) are written by Google employees. So is the user-feedback data. The same system underestimated the 6 February 2023 Türkiye earthquakes. The BBC reported on 27 July 2025 that only 469 “Take Action” alerts were sent for the M7.8 event. 10 million people could have received one." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Android Earthquake Alerts (AEA) system" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Google's Android Earthquake Alerts detects the Mw 6.2 Marmara Ereğlisi earthquake 5.31 s after origin and alerts over 16 million users on 23 April 2025; evaluation is Google-authored", "headline": "Google's AEA alerts 16 million+ users to Marmara Ereğlisi quake" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "5d5b66e01eda69fa53abd5912285d70540631339d1048ba27dde67b5a1cf8ba7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 23 April 2025 the Mw 6.2 Marmara Ereğlisi earthquake struck offshore in the Sea of Marmara, Türkiye. Initial reports cited by the evaluation put shaking in Istanbul at MMI V. At least 359 people were injured, mostly from panic. Google's Android Earthquake Alerts (AEA) system uses the accelerometers of online Android phones. An on-phone detection algorithm flags sudden changes in acceleration. The system detected the event 5.31 seconds after its origin. It issued a first “Be Aware” alert 0.43 seconds later on an initial magnitude estimate of Mw 4.83. “Take Action” alerts followed as the estimate rose. Alerts reached more than 16 million Android users in Türkiye, Greece, Romania and Bulgaria. Warning times for weak shaking (MMI III) reached up to 150 seconds with a median of 56 seconds. The authors report 90% true positives and 99% precision. The final magnitude estimate saturated at Mw 6.04, so the “Take Action” polygon was too small. Inland populations that felt MMI V shaking received only the “Be Aware” warning before stronger shaking arrived. The evaluation (Nature Communications, 11 August 2026) and the system description (Science, 17 July 2025) are written by Google employees. So is the user-feedback data. The same system underestimated the 6 February 2023 Türkiye earthquakes. The BBC reported on 27 July 2025 that only 469 “Take Action” alerts were sent for the M7.8 event. 10 million people could have received one.", "replication": { "produced_by": [ "Dense network of online Android phones (about 66,000 used for characterization) around an offshore epicentre", "On-phone trigger on P-wave acceleration and server-side magnitude estimation updated over 12 s", "Two-tier alerting (Be Aware, Take Action) delivered through the operating system", "A population with high earthquake awareness after the 2023 Kahramanmaraş disaster, per the authors" ], "must_hold": [ "Enough stationary, charging phones near the epicentre to trigger early", "Magnitude estimates that do not saturate below the true magnitude. This failed here at Mw 6.04 and failed badly in 2023.", "Alerts that outrun S-wave arrival at the populated distance", "Users who recognize and act on the alert" ], "echoes": [ { "date": "2023-02-06", "setting": "Mw 7.8 and Mw 7.5 earthquakes, south-east Türkiye: magnitude estimated at 4.5–4.9; 469 Take Action alerts sent for the first quake when 10 million people could have received one (BBC)", "outcome": "did not recur", "source": "https://www.bbc.com/news/articles/c77v2kx304go" }, { "setting": "Global operation over three years: alerts in 98 countries, about 60 events and 18 million alerts per month; 85% of alert recipients felt shaking (Science 2025, Google-authored)", "outcome": "recurred", "source": "https://europepmc.org/abstract/MED/40674485" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0381", "case_url": "records/AIBD-0381.html", "reason": "The developer is headquartered in United States; the case names Turkey.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Turkey" }, { "label": "Case location", "value": "Tokat, Turkey" } ], "filters": { "origin": "United States", "destination": [ "Turkey" ] }, "record": { "id": "AIBD-0381", "title": "School bus driver Mehmet Can Duran in Tokat has ChatGPT draft his objection to a 2,400 TL traffic fine; the Tokat Civil Court of Peace annuls it within three weeks for lack of evidence", "summary": "A Tokat school bus driver used ChatGPT to write his objection to a 2,400 TL fine for dropping a student off outside a stop; the court annulled it within three weeks for lack of evidence.", "date_observed": "2025-02-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Mehmet Can Duran, a school bus driver in Tokat, Turkey, was fined 2,400 TL by an honorary traffic inspector (fahri trafik müfettişi). He allegedly let a student off outside the designated stop. Webtekno says he had stopped at a pedestrian crossing to let people cross (Webtekno, 7 February 2025). Not wanting to pay a lawyer, he had an objection petition written by artificial intelligence. Webtekno identifies it as ChatGPT. Akşam's İHA report names no tool (Akşam, 6 February 2025). The petition stressed that the inspector had submitted no image or document supporting the fine. The Tokat Civil Court of Peace (Sulh Hukuk Mahkemesi) found for Duran and annulled the fine within three weeks (Akşam; Webtekno). The reports, published February 6 and 7, 2025, do not give the dates of the fine or the ruling.", "category": "benefit created", "unexpected": false, "purpose": "A general-purpose chatbot. Drafting a legal objection on request is within what it was built to do, so this benefit was expected use.", "system": "ChatGPT (per Webtekno; Akşam/İHA says only 'yapay zeka', artificial intelligence)", "model_version": null, "developer": "OpenAI (per Webtekno's identification of ChatGPT)", "deployer": null, "location": "Tokat, Turkey", "domain": "Legal services / administrative appeals", "segment": "Traffic fine objections by self-represented drivers", "benefit_type": "Reduced Task Cost", "taxonomy_note": "The good is a legal petition produced without an attorney's fee and a fine annulled; the annulment itself rested on the court's finding that the inspector had no evidence.", "registry_ids": { "oecd_aim": "2025-02-06-4cc2" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "Duran's 2,400 TL fine was annulled on an objection he filed without paying a lawyer. A one-off measurable result, not a lasting change." } ], "ai_role": { "status": "assessed", "statement": "ChatGPT, per Webtekno, drafted the objection that pointed to the absence of any image or document behind the fine. The court annulled the fine on that ground. The decisive fact was the inspector's missing evidence, which Duran could have pleaded himself. What the system contributed was a petition in legal form at no fee. Neither report quotes the petition or the ruling." }, "evidence": [ { "kind": "news report", "citation": "Bulut, B. (2025). Bir Vatandaş, ChatGPT'ye Yazdırdığı Dilekçe ile Trafik Cezasını İptal Ettirdi. Webtekno, February 7, 2025.", "title": "Bir Vatandaş, ChatGPT'ye Yazdırdığı Dilekçe ile Trafik Cezasını İptal Ettirdi", "url": "https://www.webtekno.com/chatgpt-dilekce-trafik-cezasi-h155464.html", "date": "2025-02-07", "written_by": "independent", "quote": "Dilekçede fahri trafik müfettişinin cezaya dair herhangi bir görsel ya da belge sunmadığı vurgulanınca" }, { "kind": "news report", "citation": "Akşam / İHA (2025). Yapay zekaya yazdırdığı dilekçe ile trafik cezasını iptal ettirdi. Akşam, February 6, 2025.", "title": "Yapay zekaya yazdırdığı dilekçe ile trafik cezasını iptal ettirdi", "url": "https://www.aksam.com.tr/guncel/yapay-zekaya-yazdirdigi-dilekce-ile-trafik-cezasini-iptal-ettirdi/haber-1544041", "date": "2025-02-06", "written_by": "independent", "quote": "haklı bularak cezayı iptal etti" }, { "kind": "registry label", "citation": "OECD.AI (2025). School Bus Driver Cancels Traffic Fine with AI-Written Appeal. OECD AI Incidents and Hazards Monitor, 2025-02-06.", "title": "School Bus Driver Cancels Traffic Fine with AI-Written Appeal", "url": "https://oecd.ai/en/incidents/2025-02-06-4cc2", "date": "2025-02-06", "written_by": "independent", "quote": "The AI-generated petition highlighted missing evidence, and the Tokat Civil Court ruled in his favor, annulling the fine." } ], "replication": { "produced_by": [ "A fine issued without supporting evidence that an objection could expose", "A chatbot able to produce a petition in the form the court expects", "A court that accepts a self-filed written objection" ], "must_hold": [ "The objection's factual claim must be true and checkable by the court", "The filer must still meet the court's deadlines and form requirements" ], "echoes": [] }, "paired_incident": null, "headline": "AI-drafted objection gets a Tokat bus driver's traffic fine annulled", "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/2025-02-06-4cc2", "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 system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0381", "catalog_version": "1.0.0", "source_fingerprint": "c65adc379764743d3eddf75d6df2a54967ffc54157a587225c58329cf59b55f4", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0381-source-0001", "AIBD-0381-source-0002", "AIBD-0381-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": "OpenAI (per Webtekno's identification of ChatGPT)" } }, "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, per Webtekno, drafted the objection that pointed to the absence of any image or document behind the fine. The court annulled the fine on that ground. The decisive fact was the inspector's missing evidence, which Duran could have pleaded himself. What the system contributed was a petition in legal form at no fee. Neither report quotes the petition or the ruling." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "ChatGPT, per Webtekno, drafted the objection that pointed to the absence of any image or document behind the fine. The court annulled the fine on that ground. The decisive fact was the inspector's missing evidence, which Duran could have pleaded himself. What the system contributed was a petition in legal form at no fee. Neither report quotes the petition or the ruling." } } }, "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": "Duran's 2,400 TL fine was annulled on an objection he filed without paying a lawyer. A one-off measurable result, not a lasting change." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Duran's 2,400 TL fine was annulled on an objection he filed without paying a lawyer. A one-off measurable result, not a lasting change." } ] } }, "provenance": { "kind": "legacy_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": "Duran's 2,400 TL fine was annulled on an objection he filed without paying a lawyer. A one-off measurable result, not a lasting change." } ] } }, "provenance": { "kind": "legacy_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": "2025-02-06-4cc2" } } }, "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-0381" } }, "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 Task Cost", "taxonomy_note": "The good is a legal petition produced without an attorney's fee and a fine annulled; the annulment itself rested on the court's finding that the inspector had no evidence." } }, "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": "Mehmet Can Duran, a school bus driver in Tokat, Turkey, was fined 2,400 TL by an honorary traffic inspector (fahri trafik müfettişi). He allegedly let a student off outside the designated stop. Webtekno says he had stopped at a pedestrian crossing to let people cross (Webtekno, 7 February 2025). Not wanting to pay a lawyer, he had an objection petition written by artificial intelligence. Webtekno identifies it as ChatGPT. Akşam's İHA report names no tool (Akşam, 6 February 2025). The petition stressed that the inspector had submitted no image or document supporting the fine. The Tokat Civil Court of Peace (Sulh Hukuk Mahkemesi) found for Duran and annulled the fine within three weeks (Akşam; Webtekno). The reports, published February 6 and 7, 2025, do not give the dates of the fine or the ruling.", "benefit_type": "Reduced Task Cost" } }, "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": "Duran's 2,400 TL fine was annulled on an objection he filed without paying a lawyer. A one-off measurable result, not a lasting change." } ] } }, "provenance": { "kind": "legacy_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 general-purpose chatbot. Drafting a legal objection on request is within what it was built to do, so this benefit was expected 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": "Tokat, Turkey", "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": "A general-purpose chatbot. Drafting a legal objection on request is within what it was built to do, so this benefit was expected 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": "2025-02-06", "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-02-06-4cc2", "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 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 fine issued without supporting evidence that an objection could expose", "A chatbot able to produce a petition in the form the court expects", "A court that accepts a self-filed written objection" ], "must_hold": [ "The objection's factual claim must be true and checkable by the court", "The filer must still meet the court's deadlines and form requirements" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-02-06-4cc2", "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 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": "Legal services / administrative appeals", "segment": "Traffic fine objections by self-represented drivers" } }, "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": "Bulut, B. (2025). Bir Vatandaş, ChatGPT'ye Yazdırdığı Dilekçe ile Trafik Cezasını İptal Ettirdi. Webtekno, February 7, 2025.", "title": "Bir Vatandaş, ChatGPT'ye Yazdırdığı Dilekçe ile Trafik Cezasını İptal Ettirdi", "url": "https://www.webtekno.com/chatgpt-dilekce-trafik-cezasi-h155464.html", "date": "2025-02-07", "written_by": "independent", "quote": "Dilekçede fahri trafik müfettişinin cezaya dair herhangi bir görsel ya da belge sunmadığı vurgulanınca" }, { "kind": "news report", "citation": "Akşam / İHA (2025). Yapay zekaya yazdırdığı dilekçe ile trafik cezasını iptal ettirdi. Akşam, February 6, 2025.", "title": "Yapay zekaya yazdırdığı dilekçe ile trafik cezasını iptal ettirdi", "url": "https://www.aksam.com.tr/guncel/yapay-zekaya-yazdirdigi-dilekce-ile-trafik-cezasini-iptal-ettirdi/haber-1544041", "date": "2025-02-06", "written_by": "independent", "quote": "haklı bularak cezayı iptal etti" }, { "kind": "registry label", "citation": "OECD.AI (2025). School Bus Driver Cancels Traffic Fine with AI-Written Appeal. OECD AI Incidents and Hazards Monitor, 2025-02-06.", "title": "School Bus Driver Cancels Traffic Fine with AI-Written Appeal", "url": "https://oecd.ai/en/incidents/2025-02-06-4cc2", "date": "2025-02-06", "written_by": "independent", "quote": "The AI-generated petition highlighted missing evidence, and the Tokat Civil Court ruled in his favor, annulling the fine." } ] } }, "provenance": { "kind": "legacy_record", "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-0381-source-0001", "AIBD-0381-source-0002", "AIBD-0381-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": "Bulut, B. (2025). Bir Vatandaş, ChatGPT'ye Yazdırdığı Dilekçe ile Trafik Cezasını İptal Ettirdi. Webtekno, February 7, 2025.", "title": "Bir Vatandaş, ChatGPT'ye Yazdırdığı Dilekçe ile Trafik Cezasını İptal Ettirdi", "url": "https://www.webtekno.com/chatgpt-dilekce-trafik-cezasi-h155464.html", "date": "2025-02-07", "written_by": "independent", "quote": "Dilekçede fahri trafik müfettişinin cezaya dair herhangi bir görsel ya da belge sunmadığı vurgulanınca" }, { "kind": "news report", "citation": "Akşam / İHA (2025). Yapay zekaya yazdırdığı dilekçe ile trafik cezasını iptal ettirdi. Akşam, February 6, 2025.", "title": "Yapay zekaya yazdırdığı dilekçe ile trafik cezasını iptal ettirdi", "url": "https://www.aksam.com.tr/guncel/yapay-zekaya-yazdirdigi-dilekce-ile-trafik-cezasini-iptal-ettirdi/haber-1544041", "date": "2025-02-06", "written_by": "independent", "quote": "haklı bularak cezayı iptal etti" }, { "kind": "registry label", "citation": "OECD.AI (2025). School Bus Driver Cancels Traffic Fine with AI-Written Appeal. OECD AI Incidents and Hazards Monitor, 2025-02-06.", "title": "School Bus Driver Cancels Traffic Fine with AI-Written Appeal", "url": "https://oecd.ai/en/incidents/2025-02-06-4cc2", "date": "2025-02-06", "written_by": "independent", "quote": "The AI-generated petition highlighted missing evidence, and the Tokat Civil Court ruled in his favor, annulling the fine." } ] } }, "provenance": { "kind": "legacy_record", "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 Tokat school bus driver used ChatGPT to write his objection to a 2,400 TL fine for dropping a student off outside a stop; the court annulled it within three weeks for lack of evidence.", "account": "Mehmet Can Duran, a school bus driver in Tokat, Turkey, was fined 2,400 TL by an honorary traffic inspector (fahri trafik müfettişi). He allegedly let a student off outside the designated stop. Webtekno says he had stopped at a pedestrian crossing to let people cross (Webtekno, 7 February 2025). Not wanting to pay a lawyer, he had an objection petition written by artificial intelligence. Webtekno identifies it as ChatGPT. Akşam's İHA report names no tool (Akşam, 6 February 2025). The petition stressed that the inspector had submitted no image or document supporting the fine. The Tokat Civil Court of Peace (Sulh Hukuk Mahkemesi) found for Duran and annulled the fine within three weeks (Akşam; Webtekno). The reports, published February 6 and 7, 2025, do not give the dates of the fine or the ruling." } }, "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 (per Webtekno; Akşam/İHA says only 'yapay zeka', artificial intelligence)" } }, "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 (per Webtekno's identification of ChatGPT)" } }, "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": "School bus driver Mehmet Can Duran in Tokat has ChatGPT draft his objection to a 2,400 TL traffic fine; the Tokat Civil Court of Peace annuls it within three weeks for lack of evidence", "headline": "AI-drafted objection gets a Tokat bus driver's traffic fine annulled" } }, "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": [ "c65adc379764743d3eddf75d6df2a54967ffc54157a587225c58329cf59b55f4" ], "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": "Mehmet Can Duran, a school bus driver in Tokat, Turkey, was fined 2,400 TL by an honorary traffic inspector (fahri trafik müfettişi). He allegedly let a student off outside the designated stop. Webtekno says he had stopped at a pedestrian crossing to let people cross (Webtekno, 7 February 2025). Not wanting to pay a lawyer, he had an objection petition written by artificial intelligence. Webtekno identifies it as ChatGPT. Akşam's İHA report names no tool (Akşam, 6 February 2025). The petition stressed that the inspector had submitted no image or document supporting the fine. The Tokat Civil Court of Peace (Sulh Hukuk Mahkemesi) found for Duran and annulled the fine within three weeks (Akşam; Webtekno). The reports, published February 6 and 7, 2025, do not give the dates of the fine or the ruling.", "replication": { "produced_by": [ "A fine issued without supporting evidence that an objection could expose", "A chatbot able to produce a petition in the form the court expects", "A court that accepts a self-filed written objection" ], "must_hold": [ "The objection's factual claim must be true and checkable by the court", "The filer must still meet the court's deadlines and form requirements" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0350", "case_url": "records/AIBD-0350.html", "reason": "The developer is headquartered in United States; the case names Argentina.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Argentina" }, { "label": "Case location", "value": "Argentine exclusive economic zone off Caleta Olivia, Santa Cruz, Argentina" } ], "filters": { "origin": "United States", "destination": [ "Argentina" ] }, "record": { "id": "AIBD-0350", "title": "Skylight speed-range and machine-learning fishing alerts may have led Argentina's Prefectura Naval to intercept the Vanuatu-flagged XINRUN 579 on 5 January 2025, and a fine may have been paid", "summary": "PNA intercepted XINRUN 579 fishing illegally in Argentina's EEZ on 5 January 2025; Skylight says its speed-range and ML fishing alerts prompted the response and a first electronic fine was paid.", "date_observed": "2025-01-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Sunday 5 January 2025 the Prefectura Naval Argentina (PNA) detected the Vanuatu-flagged trawler XinRun 579. It was in Argentina's exclusive economic zone off Caleta Olivia, Santa Cruz, sailing at under 6 knots. Local rules treat that as indicating trawling. Coastguard vessel GC-25 Azopardo intercepted it. The captain was formally notified of an infraction under Law 24.922, opening administrative proceedings (Agenda Malvinas, 8 January 2025, citing official information). Skylight, the free AI maritime-monitoring platform of the Allen Institute for AI, published its own account on 31 March 2025. A rule-based Speed Range Event first alerted the authorities. A Fishing Event alert from its machine-learning model came 40 minutes later. PNA analysts redirected a nearby coastguard vessel. It led to Argentina's first international electronic fine using AI-powered evidence, with the fine and operational costs paid. It quotes the EICEMAR coordinator and the PNA GIS division lead crediting Skylight. The independent news report attributes the detection to the PNA's Sistema Guardacostas and does not mention Skylight or AI. The PNA release Skylight links to is not found live or in the Internet Archive. An NVIDIA Developer blog (3 March 2025) also reports the interdiction and fine.", "category": "protective response", "unexpected": false, "purpose": "Real-time detection of suspicious vessel behavior and fishing activity to support maritime enforcement against illegal, unreported and unregulated fishing. The interception was the intended use.", "system": "Skylight maritime-monitoring platform (Speed Range Event rule and machine-learning Fishing Event detection)", "model_version": "Atlantes", "developer": "Allen Institute for AI (Ai2), Skylight", "deployer": "Prefectura Naval Argentina (Dirección de Tráfico Marítimo, Fluvial y Lacustre; EICEMAR)", "location": "Argentine exclusive economic zone off Caleta Olivia, Santa Cruz, Argentina", "domain": "Fisheries enforcement / illegal, unreported and unregulated fishing", "segment": "National coastguard using a free non-profit AI platform", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": "An interception and fine for illegal fishing protect a fishery; no ecological gain is measured and no enforcement type exists.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 2, "reason": "The PNA intercepted the vessel within hours and, per Skylight, issued its first international electronic fine with the fine and costs paid. The independent report confirms the interception and administrative proceedings." }, { "group": "societal", "depth": 1, "reason": "One vessel among hundreds operating at the edge of Argentina's EEZ each season; the fishery-level effect is not quantified." } ], "ai_role": { "status": "assessed", "statement": "Skylight's own account describes a two-stage AI contribution. A rule-based Speed Range Event flagged the vessel at under 6 knots inside an area of interest. 40 minutes later a Fishing Event from its machine-learning model, which detects longlining, trawling and squid jigging, reinforced the suspicion. PNA analysts then redirected a coastguard vessel. A retired PNA captain is quoted saying Skylight contributed directly to the decision. This is the maker's account, with the deployer's officers quoted inside it. The independent report (Agenda Malvinas, citing Pescare and official information) attributes the detection to the PNA's Sistema Guardacostas and does not name Skylight or AI. So the attribution to the AI output is not independently confirmed. The PNA release Skylight links to is not among the sources." }, "evidence": [ { "kind": "news report", "citation": "Agenda Malvinas (2025). Prefectura detectó un buque asiático pescando ilegalmente en aguas argentinas. 8 January 2025 (source line: Pescare).", "title": "Prefectura detectó un buque asiático pescando ilegalmente en aguas argentinas", "url": "https://agendamalvinas.com.ar/noticia/prefectura-detecto-un-buque-asiatico-pescando-ilegalmente-en-aguas-argentinas", "date": "2025-01-08", "written_by": "independent", "quote": "la Prefectura Naval Argentina detectó este domingo 5 de enero, al buque pesquero XinRun 579, de bandera de Vanuatu, realizando pesca ilegal en la Zona Económica Exclusiva Argentina" }, { "kind": "news report", "citation": "Agenda Malvinas (2025). Same article, detection and interception sentences.", "title": "Prefectura detectó un buque asiático pescando ilegalmente (interception)", "url": "https://agendamalvinas.com.ar/noticia/prefectura-detecto-un-buque-asiatico-pescando-ilegalmente-en-aguas-argentinas", "date": "2025-01-08", "written_by": "independent", "quote": "Tras la intercepción del Guardacostas GC-25 Azopardo, se notificó formalmente al capitán del XinRun 579 sobre la infracción" }, { "kind": "press release", "citation": "Skylight (2025). Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing. skylight.global, 31 March 2025 (maker's account).", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "Just 40 minutes later, a Fishing Event alert provided additional intelligence, reinforcing suspicions that the vessel could be engaged in illegal fishing activity." }, { "kind": "press release", "citation": "Skylight (2025). Same article, description of the detection stages.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (machine-learning model)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "then by a machine learning model that detects several types of fishing behavior like long lining, trawling, and squid jigging" }, { "kind": "press release", "citation": "Skylight (2025). Same article, quotation attributed to Retired Captain Almada, Coordinator of EICEMAR, Prefectura Naval Argentina.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (PNA quotation)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "Skylight contributed directly to the Coast Guard's decision-making by delivering immediate and reliable insights." }, { "kind": "press release", "citation": "Skylight (2025). Same article, outcome sentence.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (fine)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "The case led to Argentina’s first international electronic fine using AI-powered evidence." }, { "kind": "press release", "citation": "Wolfberg, E. (2025). AI Model Offers Conservationists New Tools to Protect Fisheries, Wildlife at Scale. NVIDIA Developer blog, 3 March 2025 (technology-partner blog, not the system's maker).", "title": "AI Model Offers Conservationists New Tools to Protect Fisheries, Wildlife at Scale", "url": "https://developer.nvidia.com/blog/ai-model-offers-conservationists-new-tools-to-protect-fisheries-wildlife-at-scale/", "date": "2025-03-03", "written_by": "independent", "quote": "Argentina's Coast Guard interdicted—and later fined—a vessel illegally fishing in its exclusive economic zone after Skylight alerted it to the vessel's suspicious behavior" }, { "kind": "press release", "citation": "Skylight (2026). Year in Review: Skylight Opens to the Public, Introduces Shippy, and Helps Partners Act at Sea (Impact Report June 2025-May 2026).", "title": "Skylight Impact Report 2025-2026", "url": "https://www.skylight.global/news/impact-report-2025-2026", "date": null, "written_by": "makers", "quote": "Skylight surfaces signals. The inspections, fines, and decisions belong to the agencies that made them." } ], "replication": { "produced_by": [ "A vessel transmitting AIS inside a monitored area of interest", "A speed-range rule tuned to the local legal definition of trawling speed", "A machine-learning fishing-behavior model producing a second alert within 40 minutes", "PNA analysts integrating Skylight with the Sistema Guardacostas and a coastguard vessel within reach" ], "must_hold": [ "AIS transmissions not switched off or spoofed", "A patrol asset close enough to intercept before the vessel leaves the EEZ", "A legal route to sanction (Law 24.922 and the electronic fine procedure)", "Analyst confirmation of each alert before dispatch" ], "echoes": [ { "date": null, "setting": "Gabon, ANPN marine parks: Skylight alert on GUO JI 838 entering a restricted MPA led to a patrol being sent; no outcome stated (Skylight's account, undated)", "model_version": null, "outcome": "unknown", "source": "https://www.skylight.global/news/stories-from-the-field-protecting-the-biggest-marine-national-park-system-in-africa" }, { "date": null, "setting": "Skylight impact report June 2025-May 2026: aggregated partner results of 21+ vessels interdicted and fines issued, US$915,000+ in fines, without case-level attribution (maker's figures)", "model_version": null, "outcome": "recurred", "source": "https://www.skylight.global/news/impact-report-2025-2026" } ] }, "paired_incident": null, "headline": "Skylight alerts may have led PNA to intercept XINRUN 579", "metric": null, "comparator": { "against": "PNA's capability before Skylight's real-time fishing detection", "before": null, "after": "a Speed Range Event alert, then a Fishing Event alert 40 minutes later", "basis": "assumed", "quote": "This kind of swift and decisive assistance wouldn't have been possible just a few months ago, before Skylight’s latest advancements in real-time fishing detection.", "url": "https://www.skylight.global/news/argentina-success-story" }, "later": { "status": "litigated", "date": "2025-03-31", "note": "The administrative proceeding ended with the vessel paying the fine and the authorities' operational costs, as Skylight reported on 31 March 2025 (the payment date itself is not given; NVIDIA's 3 March 2025 blog already said the vessel was later fined).", "quote": "The vessel paid both the fine and the operational costs incurred by authorities–underscoring how smarter, faster detection is helping enforcement teams respond quickly and build stronger, more actionable cases against those who exploit the ocean.", "url": "https://www.skylight.global/news/argentina-success-story" }, "value": null, "developer_country": "United States", "deployer_country": "Argentina", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.skylight.global/news/argentina-success-story; https://www.skylight.global/news/argentina-success-story; https://developer.nvidia.com/blog/ai-model-offers-conservationists-new-tools-to-protect-fisheries-wildlife-at-scale/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://allenai.org/about; https://en.wikipedia.org/wiki/Argentine_Naval_Prefecture", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, one independent press release and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0350", "catalog_version": "1.0.0", "source_fingerprint": "30e9f88a9087fef78c19cfc9c9322d9ab4b3a849e0f3dd14333df59dc2954154", "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-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" ], "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": "Allen Institute for AI (Ai2), Skylight", "deployer": "Prefectura Naval Argentina (Dirección de Tráfico Marítimo, Fluvial y Lacustre; EICEMAR)" } }, "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": "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." } } }, "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": "A speed rule in Skylight first flagged the vessel. Its machine-learning model then detected a pattern consistent with fishing. According to Skylight, coast guard analysts used those alerts when redirecting a patrol vessel.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "Skylight's own account describes a two-stage AI contribution. 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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.", "benefit_type": "Improved Environmental Sustainability" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary 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. 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The interception 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": "2025-01-05", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.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.", "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). 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). 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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-0095", "case_url": "records/AIBD-0095.html", "reason": "The developer is headquartered in United States; the case names Nigeria.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Nigeria" }, { "label": "Case location", "value": "Ibaji, Kogi State, Nigeria" } ], "filters": { "origin": "United States", "destination": [ "Nigeria" ] }, "record": { "id": "AIBD-0095", "title": "GiveDirectly paid anticipatory cash in Kogi State, Nigeria, on Google Flood Hub forecast triggers (August to October 2024)", "summary": "Google Flood Hub forecast triggers paid $105 to 4,600 people in Ibaji, Kogi State, mostly before floods peaked on 21 October 2024", "date_observed": "2024-08-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In 2024 GiveDirectly ran an anticipatory cash program in six wards of Ibaji, Kogi State, Nigeria. JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation appeared.", "category": "protective response", "unexpected": false, "purpose": "Trigger and target anticipatory cash transfers to flood-prone communities before river flooding.", "system": "Google Flood Hub AI river-flood forecasts accessed via API, with JBA Global Resilience hydrology analysis and a 20%-of-community-area trigger rule", "model_version": null, "developer": "Google (Flood Hub); trigger design by GiveDirectly with JBA Global Resilience", "deployer": "GiveDirectly", "location": "Ibaji, Kogi State, Nigeria", "domain": "Humanitarian anticipatory action and cash transfers", "segment": "NGO cash assistance", "benefit_type": "Reduced Risk", "taxonomy_note": "Cash paid before a forecast flood is read as reducing potential for harm; Trustible's description stresses legal harm.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "$105 to over 4,600 individuals, followed by a $210 transfer (recipient count not stated). Implementer survey reports weekly income doubling and fewer food shortages (self-reported, not independently verified)." }, { "group": "organizational", "depth": 1, "reason": "GiveDirectly gained a tested trigger pipeline and documented lessons. It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ], "ai_role": { "status": "assessed", "statement": "Decisive for timing and geography: GiveDirectly told The New Humanitarian that satellite/AI flood modeling determined which communities were paid and when. Individual eligibility was decided by non-AI verification (ward residence, Smile ID checks, call-center calls). The investigation identifies that as the main source of exclusions. GiveDirectly's own page attributes the late payments to identity checks in two wards." }, "evidence": [ { "kind": "press release", "citation": "GiveDirectly (2025). How AI helped send cash just before floods hit in Nigeria. GiveDirectly (implementer report; page updated 2026).", "title": "How AI helped send cash just before floods hit in Nigeria", "url": "https://www.givedirectly.org/flood-forecast-ai/", "date": "2025-06-27", "written_by": "deployer", "quote": "We sent early payments to 3,250 people between August 29 and October 6" }, { "kind": "news report", "citation": "Ayeni, D. (2026). GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria. The New Humanitarian, investigation, 31 August 2026 (live page returns 403).", "title": "GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria", "url": "https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "date": "2026-08-31", "written_by": "independent", "quote": "Would-be aid recipients said they were excluded for submitting personal data that did not perfectly match government or bank records" }, { "kind": "news report", "citation": "Cheng, K.-C. (2024). Could AI save Nigerians from devastating floods? Al Jazeera.", "title": "Could AI save Nigerians from devastating floods?", "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "date": "2024-09-21", "written_by": "independent", "quote": "Omali received his first transfer of 177,866 naira on August 31 this year." } ], "replication": { "produced_by": [ "Google Flood Hub forecasts via API", "20%-of-area trigger rule defined by GiveDirectly and JBA", "USSD pre-enrollment, Smile ID verification and call-center checks", "Cash transfers using recipients' self-reported bank details. GiveDirectly and the IRC received a $4.6 million Google.org grant in 2024 for AI-assisted anticipatory cash (The New Humanitarian). It is not stated to have funded the Kogi transfers" ], "must_hold": [ "Forecast skill on the Niger and Benue floodplain", "Phone access, charging and matching ID records among the poorest", "Funds released and verification completed before the hazard" ], "echoes": [ { "date": "2022-10-01", "setting": "GiveDirectly's 2022 Kogi State response: communities chosen before the season by layering historical flood data (Floodbase, Fathom), granular poverty maps and crop and village density derived from satellite imagery; people enrolled mid-September 2022 and paid once October 2022 flooding began (month only known; first day used)", "model_version": null, "outcome": "unknown", "source": "https://www.givedirectly.org/flood-pilots/" }, { "date": null, "setting": "GiveDirectly anticipatory programs in Kenya, Mozambique and a planned Bangladesh RCT (Jamuna basin)", "model_version": null, "outcome": "unknown", "source": "The New Humanitarian (2026), https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "severity": null, "group": "societal", "basis": "Google-built hazard alert system underestimated the earthquake and failed to warn residents; Flood Hub triggers from the same developer decided who received anticipatory cash." }, "headline": "Google Flood Hub triggers pay $105 to 4,600 people in Kogi", "metric": "weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%", "comparator": { "against": "recipients' own situation before the transfers (GiveDirectly follow-up survey; no control group)", "before": "weekly income ₦4,500; 97% facing food shortages", "after": "weekly income ₦10,000; 7% facing food shortages", "basis": "measured", "quote": "Weekly income more than doubled, from ₦4,500 to ₦10,000 🍲 Food security improved, with 97% facing food shortages before and just 7% after", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "later": { "status": "scaled", "date": "2025-06-27", "note": "GiveDirectly's implementer report (published 27 June 2025, revised July 2026 to add the tradeoffs of remote phone-based targeting) says it is extending AI-triggered anticipatory cash with Google Research to new countries and other hazards.", "quote": "GiveDirectly continues to work with Google Research to scale the use of AI for anticipatory action to new countries and explore anticipatory cash transfers for other hazards, such as droughts.", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "value": { "amount": "$105", "what": "anticipatory cash transfer per recipient before the flood peak (a second $210 transfer followed)", "who": "over 4,600 flood-exposed residents of Ibaji, Kogi State", "estimate": false, "quote": "Once forecasted flood thresholds were met, we sent $105 to over 4,600 individuals before the flood peak.", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Re-aligned the account, ratings, AI-role statement, what produced it and the 2022 echo with the GiveDirectly and New Humanitarian texts (JBA pulled forecasts by API and set thresholds while GiveDirectly monitored daily, the $210 recipient count is not stated, the page was updated in July 2026 before the 31 August 2026 investigation, payments used bank details rather than mobile money and the Google.org grant is not tied to Kogi, satellite imagery informed crop and village density rather than poverty maps, late payments are the page's identity checks), confirmed the Ayeni byline against the Wayback snapshot of 22 September 2026, and dropped the word “State” from the headline to bring it under the 64-character limit.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-pilots/; https://web.archive.org/web/20260922092208/https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; 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. 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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[].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": "GiveDirectly’s survey reported weekly income rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%." } }, "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": "GiveDirectly surveyed recipients about weekly income and food shortages." } }, "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": "Weekly income and the share of recipients reporting food shortages." } }, "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 income and food figures are self-reported and not independently verified. 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-0346", "case_url": "records/AIBD-0346.html", "reason": "The developer is headquartered in United States; the case names Turkey.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Turkey" }, { "label": "Case location", "value": "Adıyaman and four cities in southeast Türkiye including Kahramanmaraş (Marash)" } ], "filters": { "origin": "United States", "destination": [ "Turkey" ] }, "record": { "id": "AIBD-0346", "title": "xView2 machine-learning damage maps may have been used by UN INSARAG teams in Adıyaman and by AFAD, the World Bank, IFRC and WFP after the 6 February 2023 Türkiye earthquakes", "summary": "MIT Technology Review (20 February 2023), quoting DIU: xView2 used by two UN INSARAG teams in Adıyaman and by AFAD, World Bank, IFRC and WFP; Microsoft reports 3,849 damaged buildings mapped for AFAD.", "date_observed": "2023-02-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "MIT Technology Review reported on 20 February 2023 on xView2, an open-source building-damage assessment project. It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. No responder statement describing a decision taken on xView2 output is among the sources.", "category": "protective response", "unexpected": false, "purpose": "Rapid building-damage assessment from pre- and post-event satellite imagery for humanitarian assistance and disaster response. This use was that purpose.", "system": "xView2 building damage assessment models (semantic segmentation on satellite imagery, trained on the xBD dataset)", "model_version": null, "developer": "US Defense Innovation Unit (DIU) with Carnegie Mellon University Software Engineering Institute; research partners Microsoft AI for Good and UC Berkeley", "deployer": "UN INSARAG (search and rescue ground teams in Adıyaman); AFAD, World Bank, IFRC and WFP (per the developer's account); Microsoft AI for Good delivered estimates to AFAD (per Microsoft)", "location": "Adıyaman and four cities in southeast Türkiye including Kahramanmaraş (Marash)", "domain": "Disaster response / post-earthquake damage assessment", "segment": "Humanitarian and civil-protection organizations", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The responder-use claim rests on the developer's account relayed by one news item and Microsoft's own report.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual rescue or beneficiary is attributed to the maps in any cited source." }, { "group": "organizational", "depth": 2, "reason": "Responders are reported to have received building-level damage maps in hours instead of weeks and to have found damaged areas they were unaware of. The reports come from the developer and a World Bank specialist, not the responding agencies." }, { "group": "societal", "depth": 1, "reason": "No downstream effect on rescues, shelter siting or reconstruction is documented. The maps covered part of the affected zone after imagery became usable on 9 February." } ], "ai_role": { "status": "assessed", "statement": "The AI produced the damage classifications that responders are said to have used. Which decisions changed is not documented. Sources: the developer (DIU's Gupta) and the World Bank specialist, quoted in one news report, and Microsoft's own report of delivering estimates to AFAD. World Bank's GRADE report and DLR's account do not corroborate operational use of AI products." }, "evidence": [ { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). How AI can actually be helpful in disaster response. MIT Technology Review, 20 Feb 2023.", "title": "How AI can actually be helpful in disaster response", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "xView2 has been used by at least two different ground teams of search and rescue personnel from the UN’s International Search and Rescue Advisory Group in Adiyaman" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, organisations named by DIU's Ritwik Gupta.", "title": "How AI can actually be helpful in disaster response (organisations)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "noting Turkey’s Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, World Bank specialist on assessment speed.", "title": "How AI can actually be helpful in disaster response (World Bank)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "Atishay Abbhi, a disaster risk management specialist at the World Bank, tells me that this same degree of assessment would typically take weeks and now takes hours or minutes." }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, method description.", "title": "How AI can actually be helpful in disaster response (method)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity" }, { "kind": "report", "citation": "Robinson, C., Gupta, R., Fobi Nsutezo, S., Pound, E., Ortiz, A., Rosa, M., White, K., Dodhia, R., Zolli, A., Birge, C., Lavista Ferres, J. M. (2023). Turkey Earthquake Report. Microsoft AI for Good Research Lab, February 2023.", "title": "Turkey Earthquake Report", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "date": null, "written_by": "makers", "quote": "partnered with Turkey's Ministry of Interior Disaster and Emergency Management Presidency (AFAD) to deliver building level damage estimates over four cities in southeast Turkey" }, { "kind": "report", "citation": "Robinson, C. et al. (2023). Same report, results.", "title": "Turkey Earthquake Report (results)", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "date": null, "written_by": "makers", "quote": "3,849 buildings were damaged/destroyed across the four cities" }, { "kind": "preprint", "citation": "Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv:1911.09296.", "title": "xBD: A Dataset for Assessing Building Damage from Satellite Imagery", "url": "https://arxiv.org/abs/1911.09296", "date": "2019-11-21", "written_by": "makers", "quote": "Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life." }, { "kind": "news report", "citation": "Direct Relief (2025). After Disaster, AI Models Support Rapid Understanding of Health Facility Damage. 10 Dec 2025 (describes a Direct Relief/Stanford research study in PLOS Digital Health; does not mention xView2).", "title": "After Disaster, AI Models Support Rapid Understanding of Health Facility Damage", "url": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/", "date": "2025-12-10", "written_by": "independent", "quote": "Two of the most prominent models in use during the 2023 earthquake response came from Microsoft AI for Good and from Google." } ], "replication": { "produced_by": [ "Usable high-resolution post-event satellite imagery (first available 9 February 2023, three days after the quake)", "A pre-trained open-source damage model (xBD/xView2) run by the developers on request of responding organizations", "Direct contact between DIU/Microsoft researchers and responders (UN INSARAG teams, AFAD)" ], "must_hold": [ "Cloud-free daytime imagery with a satellite overhead. The article notes far fewer images in remote or less developed areas such as Syria", "Responder trust in an off-site AI product, which the developer describes as hard to obtain", "Documentation by the responding agency of what the maps changed, which no cited source provides" ], "echoes": [ { "date": null, "setting": "Earlier deployments by the California National Guard and the Australian Geospatial-Intelligence Organization for wildfires and after flooding in Nepal, per the same MIT Technology Review article; no outcomes documented", "model_version": null, "outcome": "unknown", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, { "date": "2025-12-10", "setting": "Research study by Direct Relief and Stanford assessing health-facility damage with Microsoft AI for Good and Google models for the 2023 earthquakes (not an operational use)", "model_version": null, "outcome": "unknown", "source": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/" } ] }, "paired_incident": null, "headline": "UN INSARAG teams in Adıyaman may have used xView2 damage maps", "metric": "estimating 3,849 buildings damaged or destroyed", "comparator": { "against": "conventional damage assessment", "before": "weeks", "after": "hours or minutes", "basis": "assumed", "quote": "this same degree of assessment would typically take weeks and now takes hours or minutes", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, "later": { "status": "unknown", "date": null, "note": "Direct Relief's 10 December 2025 article describes a retrospective research study of the Microsoft and Google 2023 models that found they disagreed in significant ways; no later operational use of xView2 was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "Switzerland", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Defense_Innovation_Unit; https://en.wikipedia.org/wiki/International_Search_and_Rescue_Advisory_Group", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UN INSARAG.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, the makers' report and the makers' preprint. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0346", "catalog_version": "1.0.0", "source_fingerprint": "0552f28b520cecd7eb1ac0a7189af9fb84ad3f2ef360d27b88bb995479c5f0d7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0346-source-0001", "AIBD-0346-source-0002", "AIBD-0346-source-0003", "AIBD-0346-source-0004", "AIBD-0346-source-0005", "AIBD-0346-source-0006", "AIBD-0346-source-0007", "AIBD-0346-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "US Defense Innovation Unit (DIU) with Carnegie Mellon University Software Engineering Institute; research partners Microsoft AI for Good and UC Berkeley", "deployer": "UN INSARAG (search and rescue ground teams in Adıyaman); AFAD, World Bank, IFRC and WFP (per the developer's account); Microsoft AI for Good delivered estimates to AFAD (per Microsoft)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI produced the damage classifications that responders are said to have used. Which decisions changed is not documented. Sources: the developer (DIU's Gupta) and the World Bank specialist, quoted in one news report, and Microsoft's own report of delivering estimates to AFAD. World Bank's GRADE report and DLR's account do not corroborate operational use of AI products." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The AI produced the damage classifications that responders are said to have used. Which decisions changed is not documented. Sources: the developer (DIU's Gupta) and the World Bank specialist, quoted in one news report, and Microsoft's own report of delivering estimates to AFAD. World Bank's GRADE report and DLR's account do not corroborate operational use of AI products." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual rescue or beneficiary is attributed to the maps in any cited source." }, { "group": "organizational", "depth": 2, "reason": "Responders are reported to have received building-level damage maps in hours instead of weeks and to have found damaged areas they were unaware of. 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": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Satna, India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "record": { "id": "AIBD-0368", "title": "Instagram self-harm alert to Madhya Pradesh cyber police leads Satna officers to a 17-year-old within about an hour; she is counseled and sent home (3 November 2022)", "summary": "3 November 2022, about 5 pm: Instagram emailed MP cyber police a self-harm alert; Bhopal and Indore teams traced a 17-year-old to Satna in about an hour; she was counseled and sent home.", "date_observed": "2022-11-03", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 3 November 2022 at about 5 pm, Madhya Pradesh's state cyber police received an alert emailed by Instagram's US office. It said a user in the state had suffered a body injury. The Indore cyber-crime unit shared the “self-harm” alert with the cyber headquarters in Bhopal (ANI, 5 November 2022; Times of India, 4 November 2022). The girl, 17, had uploaded two videos. In one, a packet of poison lay beside her as she acted as if drunk. In the other she appeared to cut a vein (ANI). The Times of India notes a small cut on her wrist. State Cyber SP Vaibhav Shrivastava said: “After an effort of one hour, we traced the location of the girl”. She was in Satna district, whose police were informed (ANI). Satna SP Ashutosh Gupta said officers located her and called her in with her mother. She said she only wanted to scare her boyfriend and was sent home after counseling (Times of India).", "category": "protective response", "unexpected": false, "purpose": "Instagram's self-harm detection was built to flag content indicating self-harm or suicide risk and route alerts to agencies that can intervene. An alert that brought police to a girl posting self-harm videos was that purpose.", "system": "Instagram self-harm content detection and alert routing", "model_version": null, "developer": "Meta Platforms (Instagram)", "deployer": "Instagram; alert acted on by the Madhya Pradesh State Cyber Police (Bhopal headquarters and Indore unit) and Satna Police", "location": "Satna, India", "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police", "benefit_type": "Improved Physical Health", "taxonomy_note": "The good is a welfare check and counseling for a teen flagged for self-harm, with only a minor injury documented.", "registry_ids": { "oecd_aim": "2022-11-04-76c5" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "A 17-year-old had posted videos with poison and an apparent cut. She was reached and counseled within about an hour of the alert and sent home with a small cut on her wrist. By her own account she wanted to scare her boyfriend. An averted suicide rests on the police's framing and is not established." } ], "ai_role": { "status": "assessed", "statement": "Police say the alert came by email from Instagram's US office after the videos were posted. An ASP described the platform's AI algorithms as flagging non-graphic self-harm content such as scars, search words and hashtags. No source says whether a classifier or a user report started the alert, or whether Instagram staff reviewed it. Everything after the email was human work. Bhopal and Indore technical teams traced the location in about an hour. Satna police found and counseled her." }, "evidence": [ { "kind": "news report", "citation": "TNN (2022). Instagram alert helps cops prevent a suicide in Madhya Pradesh. The Times of India (Bhopal), 4 November 2022.", "title": "Instagram alert helps cops prevent a suicide in Madhya Pradesh", "url": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms", "date": "2022-11-04", "written_by": "independent", "quote": "She told us that she only wanted to scare her boyfriend as he was not talking to her for five days. She was sent home after a session of counseling" }, { "kind": "news report", "citation": "ANI (2022). Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna. Asian News International, 5 November 2022.", "title": "Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna", "url": "https://www.aninews.in/news/national/general-news/madhya-pradesh-instagrams-timely-alert-to-cyber-police-helps-save-minor-girls-life-in-satna20221105161736", "date": "2022-11-05", "written_by": "independent", "quote": "After an effort of one hour, we traced the location of the girl. We find that she is a resident of the Satna district of Madhya Pradesh" }, { "kind": "news report", "citation": "Team Latestly (2022). Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life. LatestLY, 4 November 2022.", "title": "Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life", "url": "https://www.latestly.com/india/news/madhya-pradesh-instagram-timely-alert-to-indore-cyber-cops-saves-satna-girl-from-ending-life-4417111.html", "date": "2022-11-04", "written_by": "independent", "quote": "AI algorithms on Instagram don't allow any graphic images of self-harm, and any non-graphic, self-harm related content, such as scars, or search words and hashtags, trigger alerts" }, { "kind": "registry label", "citation": "OECD.AI (2022). Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh. OECD AI Incidents and Hazards Monitor, entry 2022-11-04-76c5.", "title": "Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh", "url": "https://oecd.ai/en/incidents/2022-11-04-76c5", "date": null, "written_by": "independent", "quote": "Instagram's AI system detected self-harm content posted by a 17-year-old girl in Satna, Madhya Pradesh, and sent an alert to cyber police." } ], "replication": { "produced_by": [ "Instagram's self-harm detection and alert routing, which per the police flags non-graphic self-harm content and emails state cyber police with account information", "A state cyber police unit (Bhopal headquarters and Indore unit) able to trace the account's location from the emailed information, here in about an hour", "District police able to reach the person and hold a counseling session with a parent present" ], "must_hold": [ "The account's IP or other details resolve to a reachable district", "The person is found before any act. Here she was found with a small cut on her wrist", "A standing channel exists between Instagram and the state cyber police. This was the unit's second such alert" ], "echoes": [ { "date": null, "setting": "Chhattisgarh, earlier: per ASP Vaibhav Srivastava, a previous Instagram “suicide” alert to the Madhya Pradesh cyber cell led to a girl being traced to Chhattisgarh and saved by local police", "model_version": null, "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms" } ] }, "paired_incident": null, "headline": "Instagram self-harm alert brings Satna police to a 17-year-old", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": "United States", "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-11-04-76c5", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. 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This was the unit's second such alert" ], "echoes": [ { "setting": "Chhattisgarh, earlier: per ASP Vaibhav Srivastava, a previous Instagram “suicide” alert to the Madhya Pradesh cyber cell led to a girl being traced to Chhattisgarh and saved by local police", "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-11-04-76c5", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "TNN (2022). Instagram alert helps cops prevent a suicide in Madhya Pradesh. The Times of India (Bhopal), 4 November 2022.", "title": "Instagram alert helps cops prevent a suicide in Madhya Pradesh", "url": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms", "date": "2022-11-04", "written_by": "independent", "quote": "She told us that she only wanted to scare her boyfriend as he was not talking to her for five days. She was sent home after a session of counseling" }, { "kind": "news report", "citation": "ANI (2022). Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna. Asian News International, 5 November 2022.", "title": "Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna", "url": "https://www.aninews.in/news/national/general-news/madhya-pradesh-instagrams-timely-alert-to-cyber-police-helps-save-minor-girls-life-in-satna20221105161736", "date": "2022-11-05", "written_by": "independent", "quote": "After an effort of one hour, we traced the location of the girl. We find that she is a resident of the Satna district of Madhya Pradesh" }, { "kind": "news report", "citation": "Team Latestly (2022). Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life. LatestLY, 4 November 2022.", "title": "Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life", "url": "https://www.latestly.com/india/news/madhya-pradesh-instagram-timely-alert-to-indore-cyber-cops-saves-satna-girl-from-ending-life-4417111.html", "date": "2022-11-04", "written_by": "independent", "quote": "AI algorithms on Instagram don't allow any graphic images of self-harm, and any non-graphic, self-harm related content, such as scars, or search words and hashtags, trigger alerts" }, { "kind": "registry label", "citation": "OECD.AI (2022). Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh. OECD AI Incidents and Hazards Monitor, entry 2022-11-04-76c5.", "title": "Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh", "url": "https://oecd.ai/en/incidents/2022-11-04-76c5", "written_by": "independent", "quote": "Instagram's AI system detected self-harm content posted by a 17-year-old girl in Satna, Madhya Pradesh, and sent an alert to cyber police." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0368-source-0001", "AIBD-0368-source-0002", "AIBD-0368-source-0003", "AIBD-0368-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "TNN (2022). Instagram alert helps cops prevent a suicide in Madhya Pradesh. The Times of India (Bhopal), 4 November 2022.", "title": "Instagram alert helps cops prevent a suicide in Madhya Pradesh", "url": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms", "date": "2022-11-04", "written_by": "independent", "quote": "She told us that she only wanted to scare her boyfriend as he was not talking to her for five days. She was sent home after a session of counseling" }, { "kind": "news report", "citation": "ANI (2022). Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna. Asian News International, 5 November 2022.", "title": "Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna", "url": "https://www.aninews.in/news/national/general-news/madhya-pradesh-instagrams-timely-alert-to-cyber-police-helps-save-minor-girls-life-in-satna20221105161736", "date": "2022-11-05", "written_by": "independent", "quote": "After an effort of one hour, we traced the location of the girl. We find that she is a resident of the Satna district of Madhya Pradesh" }, { "kind": "news report", "citation": "Team Latestly (2022). Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life. LatestLY, 4 November 2022.", "title": "Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life", "url": "https://www.latestly.com/india/news/madhya-pradesh-instagram-timely-alert-to-indore-cyber-cops-saves-satna-girl-from-ending-life-4417111.html", "date": "2022-11-04", "written_by": "independent", "quote": "AI algorithms on Instagram don't allow any graphic images of self-harm, and any non-graphic, self-harm related content, such as scars, or search words and hashtags, trigger alerts" }, { "kind": "registry label", "citation": "OECD.AI (2022). Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh. OECD AI Incidents and Hazards Monitor, entry 2022-11-04-76c5.", "title": "Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh", "url": "https://oecd.ai/en/incidents/2022-11-04-76c5", "written_by": "independent", "quote": "Instagram's AI system detected self-harm content posted by a 17-year-old girl in Satna, Madhya Pradesh, and sent an alert to cyber police." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "3 November 2022, about 5 pm: Instagram emailed MP cyber police a self-harm alert; Bhopal and Indore teams traced a 17-year-old to Satna in about an hour; she was counseled and sent home.", "account": "On 3 November 2022 at about 5 pm, Madhya Pradesh's state cyber police received an alert emailed by Instagram's US office. It said a user in the state had suffered a body injury. The Indore cyber-crime unit shared the “self-harm” alert with the cyber headquarters in Bhopal (ANI, 5 November 2022; Times of India, 4 November 2022). The girl, 17, had uploaded two videos. In one, a packet of poison lay beside her as she acted as if drunk. In the other she appeared to cut a vein (ANI). The Times of India notes a small cut on her wrist. State Cyber SP Vaibhav Shrivastava said: “After an effort of one hour, we traced the location of the girl”. She was in Satna district, whose police were informed (ANI). Satna SP Ashutosh Gupta said officers located her and called her in with her mother. She said she only wanted to scare her boyfriend and was sent home after counseling (Times of India)." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Instagram self-harm content detection and alert routing" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Meta Platforms (Instagram)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Instagram self-harm alert to Madhya Pradesh cyber police leads Satna officers to a 17-year-old within about an hour; she is counseled and sent home (3 November 2022)", "headline": "Instagram self-harm alert brings Satna police to a 17-year-old" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "df724059f4e078b0baf3ddb1b276f72659cf7cc9167fb308345002ee63116233" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 3 November 2022 at about 5 pm, Madhya Pradesh's state cyber police received an alert emailed by Instagram's US office. It said a user in the state had suffered a body injury. The Indore cyber-crime unit shared the “self-harm” alert with the cyber headquarters in Bhopal (ANI, 5 November 2022; Times of India, 4 November 2022). The girl, 17, had uploaded two videos. In one, a packet of poison lay beside her as she acted as if drunk. In the other she appeared to cut a vein (ANI). The Times of India notes a small cut on her wrist. State Cyber SP Vaibhav Shrivastava said: “After an effort of one hour, we traced the location of the girl”. She was in Satna district, whose police were informed (ANI). Satna SP Ashutosh Gupta said officers located her and called her in with her mother. She said she only wanted to scare her boyfriend and was sent home after counseling (Times of India).", "replication": { "produced_by": [ "Instagram's self-harm detection and alert routing, which per the police flags non-graphic self-harm content and emails state cyber police with account information", "A state cyber police unit (Bhopal headquarters and Indore unit) able to trace the account's location from the emailed information, here in about an hour", "District police able to reach the person and hold a counseling session with a parent present" ], "must_hold": [ "The account's IP or other details resolve to a reachable district", "The person is found before any act. Here she was found with a small cut on her wrist", "A standing channel exists between Instagram and the state cyber police. This was the unit's second such alert" ], "echoes": [ { "setting": "Chhattisgarh, earlier: per ASP Vaibhav Srivastava, a previous Instagram “suicide” alert to the Madhya Pradesh cyber cell led to a girl being traced to Chhattisgarh and saved by local police", "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0367", "case_url": "records/AIBD-0367.html", "reason": "The developer is headquartered in United States; the case names Germany.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Germany" }, { "label": "Case location", "value": "Siegen, Germany" } ], "filters": { "origin": "United States", "destination": [ "Germany" ] }, "record": { "id": "AIBD-0367", "title": "The Palantir-based DAR software identifies the author of an emailed amok threat against Siegen's city administration in fractions of a second and 51 of about 100 participants in a Duisburg shooting (28 October 2022)", "summary": "NRW Interior Ministry, 28 Oct 2022: Palantir-based DAR resolved an amok-threat email against Siegen's city administration in seconds and identified 51 of about 100 people at a Duisburg clan shooting.", "date_observed": "2022-10-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 28 October 2022 North Rhine-Westphalia's (NRW) Interior Ministry said DAR, the police's Palantir software, was central in several recent cases (WELT; RP Online). In October someone had emailed the Siegen city administration announcing an amok attack. The threat was over the city's “topless” permission for swimming pools. The ministry said the email address revealed the suspect via Palantir in fractions of a second. A shooting in Duisburg-Hamborn between a Turkish-Arab clan and the Hells Angels injured four people. A Duisburg investigator said police quickly identified 51 of the roughly 100 participants, about 20 minutes each (RP Online; WELT). 49 of them were assigned to rival groups. Interior Minister Herbert Reul addressed the Landtag's interior committee on 27 October. He said DAR links the weapons register, residents' registration, the police case system and the wanted-persons portal. The ministry puts the 2019–2025 cost at about 39 million euros. The opposition attacks it on data-protection and cost grounds (WELT).", "category": "benefit created", "unexpected": false, "purpose": "DAR was procured to let North Rhine-Westphalia police search and link entries across police and administrative databases during investigations. Identifying suspects from an email address or from a nickname and phone number was that purpose.", "system": "DAR (Datenbankübergreifende Recherche und Analyse), Palantir software used by North Rhine-Westphalia police", "model_version": null, "developer": "Palantir Technologies", "deployer": "North Rhine-Westphalia police, under the NRW Ministry of the Interior (contract since 2019)", "location": "Siegen, Germany", "domain": "Policing / criminal investigation", "segment": "Cross-database analysis for state police", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2022-10-28-8649" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "NRW police resolved an amok-threat email address to a person in fractions of a second. They identified 51 of about 100 shooting participants at about 20 minutes each, per the ministry and a Duisburg investigator. No comparison with conventional methods and no charges or convictions are reported." }, { "group": "societal", "depth": null, "reason": "Siegen's administration and public had the author of a threatened amok attack identified. The sources do not say whether the threat was credible, whether the person was charged, or what harm was averted." } ], "ai_role": { "status": "assessed", "statement": "The reports describe DAR as software that links entries across the weapons register, residents' registration data, the police case system and the wanted-persons portal. None mentions machine learning. The minister himself describes plain linking, while the OECD AI Incidents Monitor calls it an AI system. On the ministry's account the software's contribution was speed of identification. That was an email address in fractions of a second and about 20 minutes per shooting participant. Whether manual queries would have reached the same people more slowly is not addressed. The claims come from the deploying ministry and an unnamed investigator." }, "evidence": [ { "kind": "news report", "citation": "WELT (2022). Ministerium: «Palantir» klärte unter anderem Amokdrohung auf. WELT, 28 October 2022.", "title": "Ministerium: «Palantir» klärte unter anderem Amokdrohung auf", "url": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die E-Mail-Adresse habe via «Palantir» in Sekundenbruchteilen die verdächtige Person enthüllt, hieß es aus dem Innenministerium." }, { "kind": "news report", "citation": "Corman T, Triesch A (2022). Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf. RP Online, 28 October 2022.", "title": "Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf", "url": "https://rp-online.de/nrw/panorama/duesseldorf-palantir-klaert-amokdrohung-und-schiesserei-in-duisburg-auf_aid-79054087", "date": "2022-10-28", "written_by": "independent", "quote": "Wie ein Duisburger Ermittler sagte, konnte man schnell 51 der rund 100 Beteiligten identifizieren." }, { "kind": "news report", "citation": "Westfalenpost (2022). Siegen: Amokdrohung gegen Stadtverwaltung. WP.de, 28 October 2022.", "title": "Siegen: Amokdrohung gegen Stadtverwaltung", "url": "https://www.wp.de/staedte/siegerland/article236775413/Siegen-Amokdrohung-gegen-Stadtverwaltung.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die Stadtverwaltung gerät in den Fokus eines Verdächtigen. Er schickt eine Amokdrohung." }, { "kind": "registry label", "citation": "OECD.AI (2022). AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations. OECD AI Incidents and Hazards Monitor, entry 2022-10-28-8649.", "title": "AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations", "url": "https://oecd.ai/en/incidents/2022-10-28-8649", "date": null, "written_by": "independent", "quote": "a mass shooting in Duisburg and an emailed amok threat to Siegen city administration" } ], "replication": { "produced_by": [ "Police and administrative databases (weapons register, residents' registration, case-processing system, wanted-persons portal) linked in one Palantir-based search", "An identifier from the case, such as an email address, a nickname or a phone number, that matches an entry somewhere in the linked data" ], "must_hold": [ "The person already appears in one of the linked databases. The system links records and does not generate new ones", "Investigators have lawful access to the linked registers. The opposition contests the arrangement on data-protection grounds", "Licensing continues. The ministry puts the 2019–2025 cost at about 39 million euros" ], "echoes": [ { "date": null, "setting": "August 2022, North Rhine-Westphalia: per the ministry, DAR led from one burglar to a whole gang", "model_version": null, "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" }, { "date": null, "setting": "August 2022, North Rhine-Westphalia: DAR identified a man suspected of abusing a 13-year-old girl from only a nickname and a phone number with fake owner data", "model_version": null, "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" } ] }, "paired_incident": null, "headline": "Palantir-based DAR identifies the author of a Siegen amok threat", "metric": "51 of the roughly 100 participants", "comparator": null, "later": null, "value": null, "developer_country": "United States", "deployer_country": "Germany", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-10-28-8649", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Palantir_Technologies; https://de.wikipedia.org/wiki/Polizei_Nordrhein-Westfalen", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0367", "catalog_version": "1.0.0", "source_fingerprint": "f8bb557bc7f642dcd2c31905ee08cd079662a321cced3883eaae0125429f4a68", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0367-source-0001", "AIBD-0367-source-0002", "AIBD-0367-source-0003", "AIBD-0367-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Palantir Technologies", "deployer": "North Rhine-Westphalia police, under the NRW Ministry of the Interior (contract since 2019)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The reports describe DAR as software that links entries across the weapons register, residents' registration data, the police case system and the wanted-persons portal. None mentions machine learning. The minister himself describes plain linking, while the OECD AI Incidents Monitor calls it an AI system. On the ministry's account the software's contribution was speed of identification. That was an email address in fractions of a second and about 20 minutes per shooting participant. Whether manual queries would have reached the same people more slowly is not addressed. 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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 28 October 2022 North Rhine-Westphalia's (NRW) Interior Ministry said DAR, the police's Palantir software, was central in several recent cases (WELT; RP Online). In October someone had emailed the Siegen city administration announcing an amok attack. The threat was over the city's “topless” permission for swimming pools. The ministry said the email address revealed the suspect via Palantir in fractions of a second. A shooting in Duisburg-Hamborn between a Turkish-Arab clan and the Hells Angels injured four people. A Duisburg investigator said police quickly identified 51 of the roughly 100 participants, about 20 minutes each (RP Online; WELT). 49 of them were assigned to rival groups. Interior Minister Herbert Reul addressed the Landtag's interior committee on 27 October. He said DAR links the weapons register, residents' registration, the police case system and the wanted-persons portal. The ministry puts the 2019–2025 cost at about 39 million euros. The opposition attacks it on data-protection and cost grounds (WELT).", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "NRW police resolved an amok-threat email address to a person in fractions of a second. They identified 51 of about 100 shooting participants at about 20 minutes each, per the ministry and a Duisburg investigator. No comparison with conventional methods and no charges or convictions are reported." }, { "group": "societal", "reason": "Siegen's administration and public had the author of a threatened amok attack identified. The sources do not say whether the threat was credible, whether the person was charged, or what harm was averted." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "DAR was procured to let North Rhine-Westphalia police search and link entries across police and administrative databases during investigations. Identifying suspects from an email address or from a nickname and phone number was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Siegen, Germany", "developer_country": "United States", "deployer_country": "Germany" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "DAR was procured to let North Rhine-Westphalia police search and link entries across police and administrative databases during investigations. Identifying suspects from an email address or from a nickname and phone number was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-10-28", "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-10-28-8649", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Palantir_Technologies; https://de.wikipedia.org/wiki/Polizei_Nordrhein-Westfalen", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Police and administrative databases (weapons register, residents' registration, case-processing system, wanted-persons portal) linked in one Palantir-based search", "An identifier from the case, such as an email address, a nickname or a phone number, that matches an entry somewhere in the linked data" ], "must_hold": [ "The person already appears in one of the linked databases. The system links records and does not generate new ones", "Investigators have lawful access to the linked registers. The opposition contests the arrangement on data-protection grounds", "Licensing continues. The ministry puts the 2019–2025 cost at about 39 million euros" ], "echoes": [ { "setting": "August 2022, North Rhine-Westphalia: per the ministry, DAR led from one burglar to a whole gang", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" }, { "setting": "August 2022, North Rhine-Westphalia: DAR identified a man suspected of abusing a 13-year-old girl from only a nickname and a phone number with fake owner data", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Police and administrative databases (weapons register, residents' registration, case-processing system, wanted-persons portal) linked in one Palantir-based search", "An identifier from the case, such as an email address, a nickname or a phone number, that matches an entry somewhere in the linked data" ], "must_hold": [ "The person already appears in one of the linked databases. The system links records and does not generate new ones", "Investigators have lawful access to the linked registers. The opposition contests the arrangement on data-protection grounds", "Licensing continues. The ministry puts the 2019–2025 cost at about 39 million euros" ], "echoes": [ { "setting": "August 2022, North Rhine-Westphalia: per the ministry, DAR led from one burglar to a whole gang", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" }, { "setting": "August 2022, North Rhine-Westphalia: DAR identified a man suspected of abusing a 13-year-old girl from only a nickname and a phone number with fake owner data", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-10-28-8649", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Palantir_Technologies; https://de.wikipedia.org/wiki/Polizei_Nordrhein-Westfalen", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / criminal investigation", "segment": "Cross-database analysis for state police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "WELT (2022). Ministerium: «Palantir» klärte unter anderem Amokdrohung auf. WELT, 28 October 2022.", "title": "Ministerium: «Palantir» klärte unter anderem Amokdrohung auf", "url": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die E-Mail-Adresse habe via «Palantir» in Sekundenbruchteilen die verdächtige Person enthüllt, hieß es aus dem Innenministerium." }, { "kind": "news report", "citation": "Corman T, Triesch A (2022). Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf. RP Online, 28 October 2022.", "title": "Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf", "url": "https://rp-online.de/nrw/panorama/duesseldorf-palantir-klaert-amokdrohung-und-schiesserei-in-duisburg-auf_aid-79054087", "date": "2022-10-28", "written_by": "independent", "quote": "Wie ein Duisburger Ermittler sagte, konnte man schnell 51 der rund 100 Beteiligten identifizieren." }, { "kind": "news report", "citation": "Westfalenpost (2022). Siegen: Amokdrohung gegen Stadtverwaltung. WP.de, 28 October 2022.", "title": "Siegen: Amokdrohung gegen Stadtverwaltung", "url": "https://www.wp.de/staedte/siegerland/article236775413/Siegen-Amokdrohung-gegen-Stadtverwaltung.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die Stadtverwaltung gerät in den Fokus eines Verdächtigen. Er schickt eine Amokdrohung." }, { "kind": "registry label", "citation": "OECD.AI (2022). AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations. OECD AI Incidents and Hazards Monitor, entry 2022-10-28-8649.", "title": "AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations", "url": "https://oecd.ai/en/incidents/2022-10-28-8649", "written_by": "independent", "quote": "a mass shooting in Duisburg and an emailed amok threat to Siegen city administration" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0367-source-0001", "AIBD-0367-source-0002", "AIBD-0367-source-0003", "AIBD-0367-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "WELT (2022). Ministerium: «Palantir» klärte unter anderem Amokdrohung auf. WELT, 28 October 2022.", "title": "Ministerium: «Palantir» klärte unter anderem Amokdrohung auf", "url": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die E-Mail-Adresse habe via «Palantir» in Sekundenbruchteilen die verdächtige Person enthüllt, hieß es aus dem Innenministerium." }, { "kind": "news report", "citation": "Corman T, Triesch A (2022). Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf. RP Online, 28 October 2022.", "title": "Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf", "url": "https://rp-online.de/nrw/panorama/duesseldorf-palantir-klaert-amokdrohung-und-schiesserei-in-duisburg-auf_aid-79054087", "date": "2022-10-28", "written_by": "independent", "quote": "Wie ein Duisburger Ermittler sagte, konnte man schnell 51 der rund 100 Beteiligten identifizieren." }, { "kind": "news report", "citation": "Westfalenpost (2022). Siegen: Amokdrohung gegen Stadtverwaltung. WP.de, 28 October 2022.", "title": "Siegen: Amokdrohung gegen Stadtverwaltung", "url": "https://www.wp.de/staedte/siegerland/article236775413/Siegen-Amokdrohung-gegen-Stadtverwaltung.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die Stadtverwaltung gerät in den Fokus eines Verdächtigen. Er schickt eine Amokdrohung." }, { "kind": "registry label", "citation": "OECD.AI (2022). AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations. OECD AI Incidents and Hazards Monitor, entry 2022-10-28-8649.", "title": "AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations", "url": "https://oecd.ai/en/incidents/2022-10-28-8649", "written_by": "independent", "quote": "a mass shooting in Duisburg and an emailed amok threat to Siegen city administration" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "NRW Interior Ministry, 28 Oct 2022: Palantir-based DAR resolved an amok-threat email against Siegen's city administration in seconds and identified 51 of about 100 people at a Duisburg clan shooting.", "account": "On 28 October 2022 North Rhine-Westphalia's (NRW) Interior Ministry said DAR, the police's Palantir software, was central in several recent cases (WELT; RP Online). In October someone had emailed the Siegen city administration announcing an amok attack. The threat was over the city's “topless” permission for swimming pools. The ministry said the email address revealed the suspect via Palantir in fractions of a second. A shooting in Duisburg-Hamborn between a Turkish-Arab clan and the Hells Angels injured four people. A Duisburg investigator said police quickly identified 51 of the roughly 100 participants, about 20 minutes each (RP Online; WELT). 49 of them were assigned to rival groups. Interior Minister Herbert Reul addressed the Landtag's interior committee on 27 October. He said DAR links the weapons register, residents' registration, the police case system and the wanted-persons portal. The ministry puts the 2019–2025 cost at about 39 million euros. The opposition attacks it on data-protection and cost grounds (WELT)." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "DAR (Datenbankübergreifende Recherche und Analyse), Palantir software used by North Rhine-Westphalia police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Palantir Technologies" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "The Palantir-based DAR software identifies the author of an emailed amok threat against Siegen's city administration in fractions of a second and 51 of about 100 participants in a Duisburg shooting (28 October 2022)", "headline": "Palantir-based DAR identifies the author of a Siegen amok threat" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f8bb557bc7f642dcd2c31905ee08cd079662a321cced3883eaae0125429f4a68" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 28 October 2022 North Rhine-Westphalia's (NRW) Interior Ministry said DAR, the police's Palantir software, was central in several recent cases (WELT; RP Online). In October someone had emailed the Siegen city administration announcing an amok attack. The threat was over the city's “topless” permission for swimming pools. The ministry said the email address revealed the suspect via Palantir in fractions of a second. A shooting in Duisburg-Hamborn between a Turkish-Arab clan and the Hells Angels injured four people. A Duisburg investigator said police quickly identified 51 of the roughly 100 participants, about 20 minutes each (RP Online; WELT). 49 of them were assigned to rival groups. Interior Minister Herbert Reul addressed the Landtag's interior committee on 27 October. He said DAR links the weapons register, residents' registration, the police case system and the wanted-persons portal. The ministry puts the 2019–2025 cost at about 39 million euros. The opposition attacks it on data-protection and cost grounds (WELT).", "replication": { "produced_by": [ "Police and administrative databases (weapons register, residents' registration, case-processing system, wanted-persons portal) linked in one Palantir-based search", "An identifier from the case, such as an email address, a nickname or a phone number, that matches an entry somewhere in the linked data" ], "must_hold": [ "The person already appears in one of the linked databases. The system links records and does not generate new ones", "Investigators have lawful access to the linked registers. The opposition contests the arrangement on data-protection grounds", "Licensing continues. The ministry puts the 2019–2025 cost at about 39 million euros" ], "echoes": [ { "setting": "August 2022, North Rhine-Westphalia: per the ministry, DAR led from one burglar to a whole gang", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" }, { "setting": "August 2022, North Rhine-Westphalia: DAR identified a man suspected of abusing a 13-year-old girl from only a nickname and a phone number with fake owner data", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0275", "case_url": "records/AIBD-0275.html", "reason": "The developer is headquartered in United States; the case names Thailand.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Thailand" }, { "label": "Case location", "value": "Thailand (nine primary-care sites)" } ], "filters": { "origin": "United States", "destination": [ "Thailand" ] }, "record": { "id": "AIBD-0275", "title": "Google deep-learning DR screening in Thailand's national program: 7,651 patients, specialist-level accuracy", "summary": "At nine primary-care sites (Dec 2018-Mar 2020) Google's deep-learning system graded fundus photos in real time, 94.7% accuracy for vision-threatening DR, 2,412 referrals; a fifth of images rejected.", "date_observed": "2022-03-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Ruamviboonsuk et al. (Lancet Digital Health, 7 March 2022) report a prospective interventional cohort study funded by Google and Rajavithi Hospital with Google Health co-authors. It deployed a deep-learning system at nine primary-care sites of Thailand's national diabetic retinopathy screening program between 12 December 2018 and 29 March 2020. Of 7,940 patients screened, 7,651 were analyzed and 2,412 (31.5%) were referred for retinopathy, macular edema, ungradable images or low acuity. For vision-threatening retinopathy the system's accuracy was 94.7%, sensitivity 91.4% and specificity 95.4%. The retina-specialist over-readers scored 93.5%, 84.8% and 95.5%. MIT Technology Review (27 April 2020) reported Google's own field study of the deployment in 11 clinics. Over a fifth of images were rejected, nurses were frustrated by rejections and slow internet, and when it worked the system sped screening. The Nation Thailand (10 February 2025) reported Google and Perceptra planning AI screening for about 1 million people in Thailand over a decade. It cited over 600,000 patients screened with the model globally. The paper's abstract is among the sources.", "category": "benefit created", "unexpected": false, "purpose": "Real-time automated grading of fundus photographs for diabetic retinopathy in a national screening program", "system": "Google Health deep-learning diabetic retinopathy screening system", "model_version": "ARDA", "developer": "Google (Google Health)", "deployer": "Thailand national diabetic retinopathy screening program (Ministry of Public Health guidelines) with Rajavithi Hospital, Bangkok", "location": "Thailand (nine primary-care sites)", "domain": "Health; ophthalmology", "segment": "People with diabetes due for retinopathy screening", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "7,651 patients received real-time grading with specialist-level accuracy and 2,412 were referred." }, { "group": "organizational", "depth": 1, "reason": "Real-time results in primary care; field reporting notes speed gains when the system worked and rejections and connectivity problems when it did not." }, { "group": "societal", "depth": null, "reason": "No population-level vision outcome reported." } ], "ai_role": { "status": "assessed", "statement": "The deep-learning grades and referral recommendations were delivered in real time with specialist over-reading as a safety net. Accuracy figures come from the vendor-funded, vendor co-authored prospective study. An independent news account of Google's field study records substantial image rejection and nurse frustration." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Ruamviboonsuk P, Tiwari R, Sayres R, et al. Lancet Digit Health 4(4):e235-e244 (2022)", "title": "Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study", "url": "https://doi.org/10.1016/s2589-7500(22)00017-6", "date": "2022-03-07", "written_by": "makers", "quote": "A deep-learning system can deliver real-time diabetic retinopathy detection capability similar to retina specialists in community-based screening settings." }, { "kind": "news report", "citation": "Heaven WD. MIT Technology Review, 27 Apr 2020", "title": "Google's medical AI was super accurate in a lab. Real life was a different story.", "url": "https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/", "date": "2020-04-27", "written_by": "independent", "quote": "Nurses felt frustrated, especially when they believed the rejected scans showed no signs of disease and the follow-up appointments were unnecessary." }, { "kind": "news report", "citation": "The Nation Thailand, 10 Feb 2025", "title": "Google, Perceptra offer AI-powered diabetic retinopathy screening", "url": "https://www.nationthailand.com/business/tech/40046121", "date": "2025-02-10", "written_by": "independent", "quote": "From initial research to the first patient screening in Thailand, we are committed to translating the potential of AI into meaningful change." }, { "kind": "other", "citation": "Nature Medicine 32:3525-3527 (23 Sep 2026). Comment by Google and partner-site authors on scaling deep-learning diabetic retinopathy screening across India, Thailand and Australia.", "title": "Nature Medicine comment on scaling a deep learning diabetic retinopathy tool across India, Thailand and Australia", "url": "https://www.nature.com/articles/s41591-026-04643-9", "date": "2026-09-23", "written_by": "makers", "quote": null }, { "kind": "other", "citation": "AI Weekly (6 Oct 2026). Google ARDA eye AI tops a million patients in three countries.", "title": "Google ARDA eye AI tops a million patients in three countries", "url": "https://aiweekly.co/alerts/google-arda-eye-ai-tops-a-million-patients-in-three-countries", "date": "2026-10-06", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Fundus photography at primary-care sites", "Real-time deep-learning grading with referral recommendations", "Retina-specialist over-reading as a safety mechanism" ], "must_hold": [ "Image quality sufficient for grading (a fifth of images were rejected in the 2020 field study)", "Reliable connectivity", "Referral pathways able to absorb referrals" ], "echoes": [ { "date": "2025-02-10", "setting": "Google-Perceptra program to screen about 1 million people in Thailand over a decade (announcement)", "model_version": null, "outcome": "unknown", "source": "https://www.nationthailand.com/business/tech/40046121" }, { "date": "2022-10-15", "setting": "Same Thai community-based national screening program, 4,852 participants and eight non-mydriatic camera types: the deep-learning algorithm showed better sensitivity, specificity and kappa than human graders (Srinivasan et al., Life)", "model_version": null, "outcome": "recurred", "source": "https://doi.org/10.3390/life12101610" } ] }, "paired_incident": null, "headline": "Google DR screening in Thailand matches specialists at 94.7%", "metric": "94.7% accuracy for vision-threatening DR, 2,412 referrals", "comparator": { "against": "regional retina specialist over-readers, measured against an adjudicated reference standard from fellowship-trained retina specialists", "before": "an accuracy of 93·5 (91·7-95·0; p=0·17), a sensitivity of 84·8% (79·4-90·0; p=0·024), and specificity of 95·5% (94·1-96·7; p=0·98)", "after": "an accuracy of 94·7% (95% CI 93·0-96·2), sensitivity of 91·4% (87·1-95·0), and specificity of 95·4% (94·1-96·7)", "basis": "measured", "quote": "For vision-threatening diabetic retinopathy, the deep-learning system had an accuracy of 94·7% (95% CI 93·0-96·2), sensitivity of 91·4% (87·1-95·0), and specificity of 95·4% (94·1-96·7). The retina specialist over-readers had an accuracy of 93·5 (91·7-95·0; p=0·17), a sensitivity of 84·8% (79·4-90·0; p=0·024), and specificity of 95·5% (94·1-96·7; p=0·98).", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=TITLE:%22Real-time%20diabetic%20retinopathy%20screening%20by%20deep%20learning%20in%20a%20multisite%20national%20screening%20programme%22&resultType=core&format=json" }, "later": { "status": "scaled", "date": "2026-09-23", "note": "A Nature Medicine comment of 23 September 2026 by the Google and partner-site authors describes scaling the same deep-learning screening tool across India, Thailand and Australia; an October 2026 AI Weekly alert summarizing it says the model, ARDA, has now screened more than one million patients, with Perceptra licensed to run screening in Thailand.", "quote": "Scaling a successful deep learning tool across three highly distinct settings – in India, Thailand and Australia — offers cross-cutting insights that may inform the expansion of healthcare artificial intelligence globally.", "url": "https://www.nature.com/articles/s41591-026-04643-9" }, "value": null, "developer_country": "United States", "deployer_country": "Thailand", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged a second write-up of the same study: added the 2022 Life replication in the same Thai program.", "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, the model version from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=TITLE:%22Real-time%20diabetic%20retinopathy%20screening%20by%20deep%20learning%20in%20a%20multisite%20national%20screening%20programme%22&resultType=core&format=json; https://www.nature.com/articles/s41591-026-04643-9; https://aiweekly.co/alerts/google-arda-eye-ai-tops-a-million-patients-in-three-countries", "editor": "AIBD" }, { "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/Ministry_of_Public_Health_(Thailand)", "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: two independent news reports, one other independent source, the makers' peer-reviewed study and the makers' other. 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(Lancet Digital Health, 7 March 2022) report a prospective interventional cohort study funded by Google and Rajavithi Hospital with Google Health co-authors. It deployed a deep-learning system at nine primary-care sites of Thailand's national diabetic retinopathy screening program between 12 December 2018 and 29 March 2020. Of 7,940 patients screened, 7,651 were analyzed and 2,412 (31.5%) were referred for retinopathy, macular edema, ungradable images or low acuity. For vision-threatening retinopathy the system's accuracy was 94.7%, sensitivity 91.4% and specificity 95.4%. The retina-specialist over-readers scored 93.5%, 84.8% and 95.5%. MIT Technology Review (27 April 2020) reported Google's own field study of the deployment in 11 clinics. Over a fifth of images were rejected, nurses were frustrated by rejections and slow internet, and when it worked the system sped screening. 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(Lancet Digital Health, 7 March 2022) report a prospective interventional cohort study funded by Google and Rajavithi Hospital with Google Health co-authors. It deployed a deep-learning system at nine primary-care sites of Thailand's national diabetic retinopathy screening program between 12 December 2018 and 29 March 2020. Of 7,940 patients screened, 7,651 were analyzed and 2,412 (31.5%) were referred for retinopathy, macular edema, ungradable images or low acuity. For vision-threatening retinopathy the system's accuracy was 94.7%, sensitivity 91.4% and specificity 95.4%. The retina-specialist over-readers scored 93.5%, 84.8% and 95.5%. MIT Technology Review (27 April 2020) reported Google's own field study of the deployment in 11 clinics. Over a fifth of images were rejected, nurses were frustrated by rejections and slow internet, and when it worked the system sped screening. The Nation Thailand (10 February 2025) reported Google and Perceptra planning AI screening for about 1 million people in Thailand over a decade. It cited over 600,000 patients screened with the model globally. The paper's abstract is among the sources.", "replication": { "produced_by": [ "Fundus photography at primary-care sites", "Real-time deep-learning grading with referral recommendations", "Retina-specialist over-reading as a safety mechanism" ], "must_hold": [ "Image quality sufficient for grading (a fifth of images were rejected in the 2020 field study)", "Reliable connectivity", "Referral pathways able to absorb referrals" ], "echoes": [ { "date": "2025-02-10", "setting": "Google-Perceptra program to screen about 1 million people in Thailand over a decade (announcement)", "outcome": "unknown", "source": "https://www.nationthailand.com/business/tech/40046121" }, { "date": "2022-10-15", "setting": "Same Thai community-based national screening program, 4,852 participants and eight non-mydriatic camera types: the deep-learning algorithm showed better sensitivity, specificity and kappa than human graders (Srinivasan et al., Life)", "outcome": "recurred", "source": "https://doi.org/10.3390/life12101610" } ] } } }, "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-0364", "case_url": "records/AIBD-0364.html", "reason": "The developer is headquartered in United States; the case names Belgium.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Belgium" }, { "label": "Case location", "value": "Houthalen-Helchteren, Belgium" } ], "filters": { "origin": "United States", "destination": [ "Belgium" ] }, "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. 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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." } ] } }, "provenance": { "kind": "legacy_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": "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." } }, "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": "Houthalen-Helchteren, Belgium", "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": "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." } }, "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": "2020-05-30", "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.", "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 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": [ "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/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.", "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": "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[].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-0322", "case_url": "records/AIBD-0322.html", "reason": "The developer is headquartered in United States; the case names Kenya.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Kenya" }, { "label": "Case location", "value": "Asing'e, Busia County, Kenya" } ], "filters": { "origin": "United States", "destination": [ "Kenya" ] }, "record": { "id": "AIBD-0322", "title": "PlantVillage Nuru helps Busia County cassava farmer Josephine Arisat select clean cuttings; her yields may have risen from 0.5 kg to 2–5 kg per plant", "summary": "PlantVillage's own 23 Mar 2020 post: after 18 months using Nuru, introduced via Self Help Africa and Ugunja Resource Center, Arisat reports 2.0–5.0 kg per plant against 0.5 kg; no independent source.", "date_observed": "2020-03-23", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A PlantVillage (Pennsylvania State University) blog post dated 23 March 2020, profiles Josephine Arisat of Asing'e location, Busia County. The CGIAR Platform for Big Data in Agriculture's project page links it as a “Success story from a cassava farmer in Busia County, Kenya”. She has grown cassava since 1995 and had been using the PlantVillage Nuru smartphone app “for a period of One and a half years”. Organizations “such as SHA and Ugunja Resource Center” introduced it. She is quoted: “Initially, I used to harvest rotten cassava tubers whereas their leaves looked healthy. I never knew that Cassava leaves can showcase diseases”. With Nuru she can scan leaves for cassava mosaic disease, cassava green mite or cassava brown streak disease and get management advice. The post states that before Nuru she “would harvest just 0.5kg per Cassava plant”. Using the app to select healthy cuttings to replant, “she harvests between 2.0–5.0 kg per Cassava plant”. It also records that she switched in 2016 to certified seed (MM06 and Mygeria varieties) and joined an aggregation selling to True Trade in 2019. She recommends one-meter spacing. The post does not separate any of these from the app's effect. Per CGIAR's page, Nuru was tested in Busia County “in partnership with Self Help Africa, a charity helping 28,000 farmers in the cassava value chain”. It says over 100 lead farmers there received smartphones at the start of 2019. Mrisho et al. (Frontiers in Plant Science, 2020), co-authored by PlantVillage and IITA developers, measured Nuru's symptom-recognition accuracy. It was 65% in 2020 versus 40–58% for extension agents and 18–31% for farmers, including 50 farmers from Busia County. That paper reports no individual outcomes. Business Insider (10 August 2022) independently quotes a Kilifi County farmer, Caroline Dama Kitsao, on Nuru. “The levels of pests and diseases in our farms have reduced, and our crops are more healthy”, she said. It quotes PlantVillage's David Hughes claiming yield increases “between 130% to 530%”. No peer-reviewed or agency source documents Arisat's case. Separate records cover Nuru's accuracy study and FAO fall armyworm use.", "category": "benefit created", "unexpected": false, "purpose": "Nuru is built to recognize cassava disease and pest symptoms on leaves offline and give management advice. Selecting clean cuttings from diagnosed plants is that intended use.", "system": "PlantVillage Nuru (offline TensorFlow convolutional neural network for cassava disease and pest symptoms on a smartphone)", "model_version": null, "developer": "PlantVillage, Pennsylvania State University, with the International Institute of Tropical Agriculture (IITA)", "deployer": "Self Help Africa and Ugunja Resource Center (lead-farmer program in Busia County)", "location": "Asing'e, Busia County, Kenya", "domain": "Agriculture / crop disease diagnosis", "segment": "Smallholder cassava", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The documented outcome is a higher cassava yield, and the type names the diagnostic information that produced it.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "Arisat's reported rise from 0.5 kg to 2.0 to 5.0 kg per plant is a measurable improvement in her harvest. It comes only from the developer's blog, with no independent source." } ], "ai_role": { "status": "unassessed", "statement": "Only the maker's account exists for this case. PlantVillage's post attributes the yield change, in the farmer's words, to Nuru's leaf-scanning diagnosis guiding removal of diseased plants and selection of clean cuttings. The independent evidence concerns the model's accuracy (Mrisho et al. 2020, developer co-authored) and another farmer's self-report in Business Insider, not Arisat. So the AI's share of her outcome cannot be assessed." }, "evidence": [ { "kind": "press release", "citation": "PlantVillage (2020). Successful stories of cassava farmers in Busia County. PlantVillage blog, Pennsylvania State University, 23 March 2020 (developer-authored; linked from the CGIAR Big Data Inspire Challenge project page).", "title": "Successful stories of cassava farmers in Busia County", "url": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county", "date": "2020-03-23", "written_by": "makers", "quote": "Before using PlantVillage Nuru smartphone application, Josephine would harvest just 0.5kg per Cassava plant." }, { "kind": "press release", "citation": "PlantVillage (2020), same post.", "title": "Successful stories of cassava farmers in Busia County", "url": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county", "date": "2020-03-23", "written_by": "makers", "quote": "she harvests between 2.0-5.0 kg per Cassava plant." }, { "kind": "press release", "citation": "CGIAR Platform for Big Data in Agriculture (undated). PlantVillage Nuru: Pest and disease monitoring using artificial intelligence. Inspire Challenge 2017 pilot and 2019 scale-up project page (partner-written; links the Arisat post as a success story).", "title": "PlantVillage Nuru: Pest and disease monitoring using artificial intelligence", "url": "https://bigdata.cgiar.org/inspire/inspire-challenge-2017/pest-and-disease-monitoring-by-using-artificial-intelligence/", "date": null, "written_by": "makers", "quote": "In Busia County, Kenya, PlantVillage is working with over 100 Lead Farmers who received smartphones at the beginning of 2019." }, { "kind": "peer-reviewed study", "citation": "Mrisho LM, Mbilinyi NA, Ndalahwa M, Ramcharan AM, Kehs AK, McCloskey PC, Murithi H, Hughes DP, Legg JP (2020). Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD. Frontiers in Plant Science 11:590889 (PMC7775399; developer co-authored).", "title": "Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7775399/", "date": "2020-12-17", "written_by": "makers", "quote": "Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40-58%) and farmers (18-31%)." }, { "kind": "news report", "citation": "Business Insider (2022). US state university developed an AI assistant for African farmers. 10 August 2022.", "title": "US state university developed an AI assistant for African farmers", "url": "https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8", "date": "2022-08-10", "written_by": "independent", "quote": "The levels of pests and diseases in our farms have reduced, and our crops are more healthy." } ], "replication": { "produced_by": [ "A smartphone with Nuru supplied through a lead-farmer scheme (Self Help Africa, Ugunja Resource Center) with training", "Use of the diagnosis to uproot diseased plants and select clean cuttings for replanting" ], "must_hold": [ "Symptoms are visible on leaves. Mrisho et al. report accuracy rising when six leaves per plant are scanned", "The app's effect is separable from certified seed, spacing and market changes, which the post does not attempt" ], "echoes": [ { "date": "2022-08-10", "setting": "Kilifi County cassava and maize farmer Caroline Dama Kitsao, who joined PlantVillage in 2020, quoted by Business Insider (self-report)", "model_version": null, "outcome": "recurred", "source": "https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8" } ] }, "paired_incident": null, "headline": "Nuru may have lifted cassava yield from 0.5 kg to 2–5 kg a plant", "metric": "2.0–5.0 kg per plant against 0.5 kg", "comparator": { "against": "the same farmer's harvests before she used the Nuru app", "before": "0.5kg per Cassava plant", "after": "between 2.0-5.0 kg per Cassava plant", "basis": "assumed", "quote": "Before using PlantVillage Nuru smartphone application, Josephine would harvest just 0.5kg per Cassava plant. With an AI assistant in her hand helping her to select healthy cassava cuttings to replant, she harvests between 2.0-5.0 kg per Cassava plant.", "url": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county" }, "later": { "status": "scaled", "date": "2022-08-10", "note": "By August 2022 Business Insider reported Nuru extended to eight more countries, with PlantVillage's David Hughes claiming cassava yield increases of 130% to 530%, and CGIAR's project page records 240 phones distributed to Bungoma, Kilifi and Tanzania in April 2021.", "quote": "PlantVillage has expanded some of its projects, including Nuru, to Uganda, Tanzania, Ethiopia, Mozambique, Malawi, Burkina Faso, Nepal, and Honduras.", "url": "https://web.archive.org/web/20221002175306/https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8" }, "value": null, "developer_country": "United States", "deployer_country": "Kenya", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Depth rated 2 for personal 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, what happened since from the cited sources.", "source": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county; https://web.archive.org/web/20221002175306/https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8", "editor": "AIBD" }, { "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://selfhelpafrica.org/ie/where-we-work/kenya/", "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 Self Help Africa.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, the makers' peer-reviewed study and two press releases from the makers. 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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": "2.0–5.0 kg per plant against 0.5 kg", "editor_note": "" } }, "evaluations[].reproduction_materials": 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Nuru, to Uganda, Tanzania, Ethiopia, Mozambique, Malawi, Burkina Faso, Nepal, and Honduras.", "url": "https://web.archive.org/web/20221002175306/https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8" } } }, "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-0322" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The documented outcome is a higher cassava yield, and the type names the diagnostic information that produced it." } }, "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 PlantVillage (Pennsylvania State University) blog post dated 23 March 2020, profiles Josephine Arisat of Asing'e location, Busia County. The CGIAR Platform for Big Data in Agriculture's project page links it as a “Success story from a cassava farmer in Busia County, Kenya”. She has grown cassava since 1995 and had been using the PlantVillage Nuru smartphone app “for a period of One and a half years”. Organizations “such as SHA and Ugunja Resource Center” introduced it. She is quoted: “Initially, I used to harvest rotten cassava tubers whereas their leaves looked healthy. I never knew that Cassava leaves can showcase diseases”. With Nuru she can scan leaves for cassava mosaic disease, cassava green mite or cassava brown streak disease and get management advice. The post states that before Nuru she “would harvest just 0.5kg per Cassava plant”. Using the app to select healthy cuttings to replant, “she harvests between 2.0–5.0 kg per Cassava plant”. It also records that she switched in 2016 to certified seed (MM06 and Mygeria varieties) and joined an aggregation selling to True Trade in 2019. She recommends one-meter spacing. The post does not separate any of these from the app's effect. Per CGIAR's page, Nuru was tested in Busia County “in partnership with Self Help Africa, a charity helping 28,000 farmers in the cassava value chain”. It says over 100 lead farmers there received smartphones at the start of 2019. Mrisho et al. (Frontiers in Plant Science, 2020), co-authored by PlantVillage and IITA developers, measured Nuru's symptom-recognition accuracy. It was 65% in 2020 versus 40–58% for extension agents and 18–31% for farmers, including 50 farmers from Busia County. That paper reports no individual outcomes. Business Insider (10 August 2022) independently quotes a Kilifi County farmer, Caroline Dama Kitsao, on Nuru. “The levels of pests and diseases in our farms have reduced, and our crops are more healthy”, she said. It quotes PlantVillage's David Hughes claiming yield increases “between 130% to 530%”. No peer-reviewed or agency source documents Arisat's case. Separate records cover Nuru's accuracy study and FAO fall armyworm use.", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Arisat's reported rise from 0.5 kg to 2.0 to 5.0 kg per plant is a measurable improvement in her harvest. It comes only from the developer's blog, with no independent source." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Nuru is built to recognize cassava disease and pest symptoms on leaves offline and give management advice. Selecting clean cuttings from diagnosed plants 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": "Asing'e, Busia County, Kenya", "developer_country": "United States", "deployer_country": "Kenya" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Nuru is built to recognize cassava disease and pest symptoms on leaves offline and give management advice. Selecting clean cuttings from diagnosed plants 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": "2020-03-23", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Depth rated 2 for personal 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 what the benefit is measured against, what happened since from the cited sources.", "source": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county; https://web.archive.org/web/20221002175306/https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8", "editor": "AIBD" }, { "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://selfhelpafrica.org/ie/where-we-work/kenya/", "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 Self Help Africa.", "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 smartphone with Nuru supplied through a lead-farmer scheme (Self Help Africa, Ugunja Resource Center) with training", "Use of the diagnosis to uproot diseased plants and select clean cuttings for replanting" ], "must_hold": [ "Symptoms are visible on leaves. Mrisho et al. report accuracy rising when six leaves per plant are scanned", "The app's effect is separable from certified seed, spacing and market changes, which the post does not attempt" ], "echoes": [ { "date": "2022-08-10", "setting": "Kilifi County cassava and maize farmer Caroline Dama Kitsao, who joined PlantVillage in 2020, quoted by Business Insider (self-report)", "outcome": "recurred", "source": "https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8" } ] } } }, "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 smartphone with Nuru supplied through a lead-farmer scheme (Self Help Africa, Ugunja Resource Center) with training", "Use of the diagnosis to uproot diseased plants and select clean cuttings for replanting" ], "must_hold": [ "Symptoms are visible on leaves. Mrisho et al. report accuracy rising when six leaves per plant are scanned", "The app's effect is separable from certified seed, spacing and market changes, which the post does not attempt" ], "echoes": [ { "date": "2022-08-10", "setting": "Kilifi County cassava and maize farmer Caroline Dama Kitsao, who joined PlantVillage in 2020, quoted by Business Insider (self-report)", "outcome": "recurred", "source": "https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8" } ] } } }, "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 2 for personal 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 what the benefit is measured against, what happened since from the cited sources.", "source": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county; https://web.archive.org/web/20221002175306/https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8", "editor": "AIBD" }, { "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://selfhelpafrica.org/ie/where-we-work/kenya/", "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 Self Help Africa.", "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 / crop disease diagnosis", "segment": "Smallholder cassava" } }, "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": "PlantVillage (2020). Successful stories of cassava farmers in Busia County. PlantVillage blog, Pennsylvania State University, 23 March 2020 (developer-authored; linked from the CGIAR Big Data Inspire Challenge project page).", "title": "Successful stories of cassava farmers in Busia County", "url": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county", "date": "2020-03-23", "written_by": "makers", "quote": "Before using PlantVillage Nuru smartphone application, Josephine would harvest just 0.5kg per Cassava plant." }, { "kind": "press release", "citation": "PlantVillage (2020), same post.", "title": "Successful stories of cassava farmers in Busia County", "url": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county", "date": "2020-03-23", "written_by": "makers", "quote": "she harvests between 2.0-5.0 kg per Cassava plant." }, { "kind": "press release", "citation": "CGIAR Platform for Big Data in Agriculture (undated). PlantVillage Nuru: Pest and disease monitoring using artificial intelligence. Inspire Challenge 2017 pilot and 2019 scale-up project page (partner-written; links the Arisat post as a success story).", "title": "PlantVillage Nuru: Pest and disease monitoring using artificial intelligence", "url": "https://bigdata.cgiar.org/inspire/inspire-challenge-2017/pest-and-disease-monitoring-by-using-artificial-intelligence/", "written_by": "makers", "quote": "In Busia County, Kenya, PlantVillage is working with over 100 Lead Farmers who received smartphones at the beginning of 2019." }, { "kind": "peer-reviewed study", "citation": "Mrisho LM, Mbilinyi NA, Ndalahwa M, Ramcharan AM, Kehs AK, McCloskey PC, Murithi H, Hughes DP, Legg JP (2020). Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD. Frontiers in Plant Science 11:590889 (PMC7775399; developer co-authored).", "title": "Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7775399/", "date": "2020-12-17", "written_by": "makers", "quote": "Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40-58%) and farmers (18-31%)." }, { "kind": "news report", "citation": "Business Insider (2022). US state university developed an AI assistant for African farmers. 10 August 2022.", "title": "US state university developed an AI assistant for African farmers", "url": "https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8", "date": "2022-08-10", "written_by": "independent", "quote": "The levels of pests and diseases in our farms have reduced, and our crops are more healthy." } ] } }, "provenance": { "kind": "legacy_record", "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-0322-source-0001", "AIBD-0322-source-0002", "AIBD-0322-source-0003", "AIBD-0322-source-0004", "AIBD-0322-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": "PlantVillage (2020). Successful stories of cassava farmers in Busia County. PlantVillage blog, Pennsylvania State University, 23 March 2020 (developer-authored; linked from the CGIAR Big Data Inspire Challenge project page).", "title": "Successful stories of cassava farmers in Busia County", "url": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county", "date": "2020-03-23", "written_by": "makers", "quote": "Before using PlantVillage Nuru smartphone application, Josephine would harvest just 0.5kg per Cassava plant." }, { "kind": "press release", "citation": "PlantVillage (2020), same post.", "title": "Successful stories of cassava farmers in Busia County", "url": "https://plantvillage.psu.edu/blogposts/71-successful-stories-of-cassava-farmers-in-busia-county", "date": "2020-03-23", "written_by": "makers", "quote": "she harvests between 2.0-5.0 kg per Cassava plant." }, { "kind": "press release", "citation": "CGIAR Platform for Big Data in Agriculture (undated). 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Frontiers in Plant Science 11:590889 (PMC7775399; developer co-authored).", "title": "Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7775399/", "date": "2020-12-17", "written_by": "makers", "quote": "Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40-58%) and farmers (18-31%)." }, { "kind": "news report", "citation": "Business Insider (2022). US state university developed an AI assistant for African farmers. 10 August 2022.", "title": "US state university developed an AI assistant for African farmers", "url": "https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8", "date": "2022-08-10", "written_by": "independent", "quote": "The levels of pests and diseases in our farms have reduced, and our crops are more healthy." } ] } }, "provenance": { "kind": "legacy_record", "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": "PlantVillage's own 23 Mar 2020 post: after 18 months using Nuru, introduced via Self Help Africa and Ugunja Resource Center, Arisat reports 2.0–5.0 kg per plant against 0.5 kg; no independent source.", "account": "A PlantVillage (Pennsylvania State University) blog post dated 23 March 2020, profiles Josephine Arisat of Asing'e location, Busia County. The CGIAR Platform for Big Data in Agriculture's project page links it as a “Success story from a cassava farmer in Busia County, Kenya”. She has grown cassava since 1995 and had been using the PlantVillage Nuru smartphone app “for a period of One and a half years”. Organizations “such as SHA and Ugunja Resource Center” introduced it. She is quoted: “Initially, I used to harvest rotten cassava tubers whereas their leaves looked healthy. I never knew that Cassava leaves can showcase diseases”. With Nuru she can scan leaves for cassava mosaic disease, cassava green mite or cassava brown streak disease and get management advice. The post states that before Nuru she “would harvest just 0.5kg per Cassava plant”. Using the app to select healthy cuttings to replant, “she harvests between 2.0–5.0 kg per Cassava plant”. It also records that she switched in 2016 to certified seed (MM06 and Mygeria varieties) and joined an aggregation selling to True Trade in 2019. She recommends one-meter spacing. The post does not separate any of these from the app's effect. Per CGIAR's page, Nuru was tested in Busia County “in partnership with Self Help Africa, a charity helping 28,000 farmers in the cassava value chain”. It says over 100 lead farmers there received smartphones at the start of 2019. Mrisho et al. (Frontiers in Plant Science, 2020), co-authored by PlantVillage and IITA developers, measured Nuru's symptom-recognition accuracy. It was 65% in 2020 versus 40–58% for extension agents and 18–31% for farmers, including 50 farmers from Busia County. That paper reports no individual outcomes. Business Insider (10 August 2022) independently quotes a Kilifi County farmer, Caroline Dama Kitsao, on Nuru. “The levels of pests and diseases in our farms have reduced, and our crops are more healthy”, she said. It quotes PlantVillage's David Hughes claiming yield increases “between 130% to 530%”. No peer-reviewed or agency source documents Arisat's case. Separate records cover Nuru's accuracy study and FAO fall armyworm use." } }, "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": "PlantVillage Nuru (offline TensorFlow convolutional neural network for cassava disease and pest symptoms on a smartphone)" } }, "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": "PlantVillage, Pennsylvania State University, with the International Institute of Tropical Agriculture (IITA)" } }, "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": "PlantVillage Nuru helps Busia County cassava farmer Josephine Arisat select clean cuttings; her yields may have risen from 0.5 kg to 2–5 kg per plant", "headline": "Nuru may have lifted cassava yield from 0.5 kg to 2–5 kg a plant" } }, "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": [ "78586b05c4ff5ef3d2e0a49c22ce8058798b8291e04bffa7cc718fa6336d9411" ], "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 PlantVillage (Pennsylvania State University) blog post dated 23 March 2020, profiles Josephine Arisat of Asing'e location, Busia County. The CGIAR Platform for Big Data in Agriculture's project page links it as a “Success story from a cassava farmer in Busia County, Kenya”. She has grown cassava since 1995 and had been using the PlantVillage Nuru smartphone app “for a period of One and a half years”. Organizations “such as SHA and Ugunja Resource Center” introduced it. She is quoted: “Initially, I used to harvest rotten cassava tubers whereas their leaves looked healthy. I never knew that Cassava leaves can showcase diseases”. With Nuru she can scan leaves for cassava mosaic disease, cassava green mite or cassava brown streak disease and get management advice. The post states that before Nuru she “would harvest just 0.5kg per Cassava plant”. Using the app to select healthy cuttings to replant, “she harvests between 2.0–5.0 kg per Cassava plant”. It also records that she switched in 2016 to certified seed (MM06 and Mygeria varieties) and joined an aggregation selling to True Trade in 2019. She recommends one-meter spacing. The post does not separate any of these from the app's effect. Per CGIAR's page, Nuru was tested in Busia County “in partnership with Self Help Africa, a charity helping 28,000 farmers in the cassava value chain”. It says over 100 lead farmers there received smartphones at the start of 2019. Mrisho et al. (Frontiers in Plant Science, 2020), co-authored by PlantVillage and IITA developers, measured Nuru's symptom-recognition accuracy. It was 65% in 2020 versus 40–58% for extension agents and 18–31% for farmers, including 50 farmers from Busia County. That paper reports no individual outcomes. Business Insider (10 August 2022) independently quotes a Kilifi County farmer, Caroline Dama Kitsao, on Nuru. “The levels of pests and diseases in our farms have reduced, and our crops are more healthy”, she said. It quotes PlantVillage's David Hughes claiming yield increases “between 130% to 530%”. No peer-reviewed or agency source documents Arisat's case. Separate records cover Nuru's accuracy study and FAO fall armyworm use.", "replication": { "produced_by": [ "A smartphone with Nuru supplied through a lead-farmer scheme (Self Help Africa, Ugunja Resource Center) with training", "Use of the diagnosis to uproot diseased plants and select clean cuttings for replanting" ], "must_hold": [ "Symptoms are visible on leaves. Mrisho et al. report accuracy rising when six leaves per plant are scanned", "The app's effect is separable from certified seed, spacing and market changes, which the post does not attempt" ], "echoes": [ { "date": "2022-08-10", "setting": "Kilifi County cassava and maize farmer Caroline Dama Kitsao, who joined PlantVillage in 2020, quoted by Business Insider (self-report)", "outcome": "recurred", "source": "https://www.businessinsider.com/us-state-university-developed-an-ai-assistant-for-african-farmers-2022-8" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 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-0130", "case_url": "records/AIBD-0130.html", "reason": "The developer is headquartered in United States; the case names Greece.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Greece" }, { "label": "Case location", "value": "Kasos, Greece" } ], "filters": { "origin": "United States", "destination": [ "Greece" ] }, "record": { "id": "AIBD-0130", "title": "Google's Android Earthquake Alerts reached phones in Crete and the Dodecanese seconds before the 14 May 2025 M6.0 (Greek press 6.1) Kasos quake", "summary": "Android users in Crete and the Dodecanese got Google's quake alert seconds before the M6.0 (Greek press 6.1) Kasos quake of 14 May 2025.", "date_observed": "2025-05-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "At 01:51 local time on Wednesday 14 May 2025 (22:51 UTC on 13 May) a magnitude 6.0 (USGS; Greek outlets 6.1) earthquake struck. It struck 20 km south-south-east of Fry on Kasos, between Kasos and Karpathos, at 76 km depth. USGS logged 931 felt reports and a green alert level. Newsbomb, GizChina Greece, in.gr and Alphafreepress reported on 14 and 15 May on Google's Android Earthquake Alerts System. In Europe it detects quakes from phone accelerometers rather than seismographs. It notified users in Crete, the Dodecanese and farther away seconds before the shaking. Notice came by message and an audible signal similar to the 112 alert. None of the reports names a recipient, gives a lead time in seconds, or mentions damage or injuries.", "category": "protective response", "unexpected": false, "purpose": "Built to detect earthquakes from the accelerometers of Android phones and combine the signals on Google's servers. It alerts users in the expected shaking zone ahead of the slower seismic waves. It covers quakes of magnitude 4.5 and above. This alert was that intended function.", "system": "Android Earthquake Alerts System (crowdsourced phone-accelerometer detection; alerts for magnitude 4.5 and above)", "model_version": null, "developer": "Google", "deployer": "Google", "location": "Kasos, Greece", "domain": "Disaster risk reduction / earthquake early warning", "segment": "Consumer smartphones (Android)", "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented.", "registry_ids": { "oecd_aim": "2025-05-14-779c" }, "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ], "ai_role": { "status": "assessed", "statement": "If the reports are right, the alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake. Alerts were then pushed ahead of the slower waves, as Google's 2020 description and the 2025 Science paper set out. Google's own pages do not use the words AI or machine learning. The Science abstract speaks of smartphone-based earthquake detection algorithms. The OECD Monitor classifies the system as AI doing event detection. That alerts arrived seconds before shaking rests on unattributed statements in four Greek outlets published within a day of the quake. None quotes a recipient, a seismologist or Google. No Google per-event confirmation was found." }, "evidence": [ { "kind": "news report", "citation": "Newsbomb (2025). «Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς. Newsbomb.gr, 14 May 2025, 23:10.", "title": "«Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys", "date": "2025-05-14", "written_by": "independent", "quote": "Πολλοί κάτοικοι κοντινών περιοχών, όπως εκείνοι στην Κρήτη, στα Δωδεκάνησα αλλά και πιο μακριά ενημερώθηκαν έγκαιρα και έλαβαν ειδοποίηση στο κινητό τους λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση" }, { "kind": "news report", "citation": "GizChina Greece (2025). Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό. GizChina.gr, 14 May 2025.", "title": "Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό", "url": "https://www.gizchina.gr/?p=363710", "date": "2025-05-14", "written_by": "independent", "quote": "αρκετοί κάτοικοι των Δωδεκανήσων, αλλά και ευρύτερων περιοχών, έλαβαν ειδοποιήσεις στο κινητό τους, δευτερόλεπτα πριν το κύμα του σεισμού φτάσει σε αυτούς" }, { "kind": "news report", "citation": "in.gr (2025). Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό. in.gr, 15 May 2025, 11:02.", "title": "Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό", "url": "https://www.in.gr/2025/05/15/in-science/technology/pos-tha-energopoiisete-tin-efarmogi-tis-google-pou-proeidopoiei-gia-seismo", "date": "2025-05-15", "written_by": "independent", "quote": "Μια υπηρεσία έγκαιρης προειδοποίησης για σεισμούς που κρύβεται στο λειτουργικό σύστημα Android ήταν αυτή που ενημέρωσε πολλούς χρήστες για τον σεισμό των 6,1 βαθμών" }, { "kind": "news report", "citation": "Alphafreepress (2025). Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android. Alphafreepress.gr, 14 May 2025, 20:42.", "title": "Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android", "url": "https://www.alphafreepress.gr/2025/05/14/ellada/kasos-seismos-61-rixter-pos-leitourgei-to-systima-proeidopoiisis-sta-android", "date": "2025-05-14", "written_by": "independent", "quote": "το σύστημα σεισμικής ειδοποίησης της Google καταφέρνει να στείλει ενημέρωση στον πολίτη πριν φτάσει το κύμα της δόνησης στην περιοχή του" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 6.0 - 20 km SSE of Fry, Greece (event us7000pz2p, 2025-05-13 22:51:15 UTC, depth 76 km). USGS Earthquake Hazards Program.", "title": "M 6.0 - 20 km SSE of Fry, Greece", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pz2p", "date": "2025-05-13", "written_by": "independent", "quote": "M 6.0 - 20 km SSE of Fry, Greece" }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "date": null, "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259. PMID 40674485.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "We show how smartphone-based earthquake detection algorithms can be implemented at scale and improved through postevent analysis." } ], "replication": { "produced_by": [ "A magnitude 6.0 (USGS) quake above the system's 4.5 alert threshold, centered offshore south of Kasos at about 76 km depth. A push alert could therefore outrun the slower waves to Crete and the Dodecanese.", "Enough Android phones near the epicentre with the Earthquake Alerts setting on to trigger Google's server-side detection. In Europe the system uses the accelerometers in phones instead of seismographs (in.gr).", "Receiving phones with location (GPS) enabled and an internet connection, which Alphafreepress lists as requirements for the alerts" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports that only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. The Greek reports document no protective action by anyone who received the alert.", "The phone must have the Earthquake Alerts setting enabled (Alphafreepress: Settings > Safety & Emergency > Earthquake Alerts). The feature must be supported in the region." ], "echoes": [] }, "paired_incident": null, "headline": "Google alerts Crete phones seconds before the M6.0 Kasos quake", "metric": null, "comparator": { "against": "the arrival of the seismic shaking (no warning)", "before": null, "after": "λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση", "basis": "assumed", "quote": "Πολλοί κάτοικοι κοντινών περιοχών, όπως εκείνοι στην Κρήτη, στα Δωδεκάνησα αλλά και πιο μακριά ενημερώθηκαν έγκαιρα και έλαβαν ειδοποίηση στο κινητό τους λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys" }, "later": { "status": "persisted", "date": "2025-07-17", "note": "Two months later Google's team published its Science evaluation of the Android system: over three years it delivered about 18 million alerts a month in 98 countries, and 36% of users who gave feedback received the alert before shaking (the paper does not discuss the Kasos event).", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month. User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively.", "url": "https://europepmc.org/article/MED/40674485" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Dated to 14 May 2025 local time: USGS gives the origin as 2025–05–13T22:51:15 UTC, M6.0 mww, 76 km depth, 20 km SSE of Fry, and in.gr places it in the early hours of Wednesday; Newsbomb's text says the early hours of Tuesday 13 May, which contradicts both and is not followed. Greek outlets report 6.1; USGS 6.0. Filed as protective response with prevention unproven as the classifier proposed. Of the Monitor's eight Greek sources, Newsbomb, GizChina Greece, in.gr and Alphafreepress state that users were alerted before the shaking (Newsbomb names Crete, the Dodecanese and farther away, by message and an audible signal like the 112 alert); Parapolitika is an explainer without that claim; eleftherostypos.gr and reader.gr returned HTTP 403 and ant1news.gr a TLS error, so they were not read. No Google statement about this event was found; web search was unavailable in this session, so sourcing is limited to the Monitor's list, USGS, Google's own pages and the Science abstract.", "source": "https://oecd.ai/en/incidents/2025-05-14-779c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys; https://europepmc.org/article/MED/40674485", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports, one independent dataset, the makers' peer-reviewed study and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0130", "catalog_version": "1.0.0", "source_fingerprint": "c434c4947e2cacd4aa85a380b8afca94ec0bad41d6ad884073cdbbb28fd816a4", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0130-source-0001", "AIBD-0130-source-0002", "AIBD-0130-source-0003", "AIBD-0130-source-0004", "AIBD-0130-source-0005", "AIBD-0130-source-0006", "AIBD-0130-source-0007", "AIBD-0130-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Google", "deployer": "Google" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "If the reports are right, the alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake. Alerts were then pushed ahead of the slower waves, as Google's 2020 description and the 2025 Science paper set out. Google's own pages do not use the words AI or machine learning. The Science abstract speaks of smartphone-based earthquake detection algorithms. The OECD Monitor classifies the system as AI doing event detection. That alerts arrived seconds before shaking rests on unattributed statements in four Greek outlets published within a day of the quake. None quotes a recipient, a seismologist or Google. No Google per-event confirmation was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Android Earthquake Alerts combined signals from phone accelerometers and sent warnings. Greek news reports said that some alerts arrived before the shaking.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "If the reports are right, the alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake. Alerts were then pushed ahead of the slower waves, as Google's 2020 description and the 2025 Science paper set out. Google's own pages do not use the words AI or machine learning. The Science abstract speaks of smartphone-based earthquake detection algorithms. The OECD Monitor classifies the system as AI doing event detection. That alerts arrived seconds before shaking rests on unattributed statements in four Greek outlets published within a day of the quake. None quotes a recipient, a seismologist or Google. No Google per-event confirmation was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: four independent news reports, one independent dataset, the makers' peer-reviewed study and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The reports quote no recipient or Google representative and provide no confirmation for this particular earthquake. Google’s cited descriptions establish an automated detection system but do not identify a learned model.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-05-14-779c" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2025-07-17", "note": "Two months later Google's team published its Science evaluation of the Android system: over three years it delivered about 18 million alerts a month in 98 countries, and 36% of users who gave feedback received the alert before shaking (the paper does not discuss the Kasos event).", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month. User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively.", "url": "https://europepmc.org/article/MED/40674485" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0130" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At 01:51 local time on Wednesday 14 May 2025 (22:51 UTC on 13 May) a magnitude 6.0 (USGS; Greek outlets 6.1) earthquake struck. It struck 20 km south-south-east of Fry on Kasos, between Kasos and Karpathos, at 76 km depth. USGS logged 931 felt reports and a green alert level. Newsbomb, GizChina Greece, in.gr and Alphafreepress reported on 14 and 15 May on Google's Android Earthquake Alerts System. In Europe it detects quakes from phone accelerometers rather than seismographs. It notified users in Crete, the Dodecanese and farther away seconds before the shaking. Notice came by message and an audible signal similar to the 112 alert. None of the reports names a recipient, gives a lead time in seconds, or mentions damage or injuries.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to detect earthquakes from the accelerometers of Android phones and combine the signals on Google's servers. It alerts users in the expected shaking zone ahead of the slower seismic waves. It covers quakes of magnitude 4.5 and above. This alert was that intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Kasos, Greece", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to detect earthquakes from the accelerometers of Android phones and combine the signals on Google's servers. It alerts users in the expected shaking zone ahead of the slower seismic waves. It covers quakes of magnitude 4.5 and above. This alert was that intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-05-14", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Dated to 14 May 2025 local time: USGS gives the origin as 2025–05–13T22:51:15 UTC, M6.0 mww, 76 km depth, 20 km SSE of Fry, and in.gr places it in the early hours of Wednesday; Newsbomb's text says the early hours of Tuesday 13 May, which contradicts both and is not followed. Greek outlets report 6.1; USGS 6.0. Filed as protective response with prevention unproven as the classifier proposed. Of the Monitor's eight Greek sources, Newsbomb, GizChina Greece, in.gr and Alphafreepress state that users were alerted before the shaking (Newsbomb names Crete, the Dodecanese and farther away, by message and an audible signal like the 112 alert); Parapolitika is an explainer without that claim; eleftherostypos.gr and reader.gr returned HTTP 403 and ant1news.gr a TLS error, so they were not read. No Google statement about this event was found; web search was unavailable in this session, so sourcing is limited to the Monitor's list, USGS, Google's own pages and the Science abstract.", "source": "https://oecd.ai/en/incidents/2025-05-14-779c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys; https://europepmc.org/article/MED/40674485", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A magnitude 6.0 (USGS) quake above the system's 4.5 alert threshold, centered offshore south of Kasos at about 76 km depth. A push alert could therefore outrun the slower waves to Crete and the Dodecanese.", "Enough Android phones near the epicentre with the Earthquake Alerts setting on to trigger Google's server-side detection. In Europe the system uses the accelerometers in phones instead of seismographs (in.gr).", "Receiving phones with location (GPS) enabled and an internet connection, which Alphafreepress lists as requirements for the alerts" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports that only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. The Greek reports document no protective action by anyone who received the alert.", "The phone must have the Earthquake Alerts setting enabled (Alphafreepress: Settings > Safety & Emergency > Earthquake Alerts). The feature must be supported in the region." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Dated to 14 May 2025 local time: USGS gives the origin as 2025–05–13T22:51:15 UTC, M6.0 mww, 76 km depth, 20 km SSE of Fry, and in.gr places it in the early hours of Wednesday; Newsbomb's text says the early hours of Tuesday 13 May, which contradicts both and is not followed. Greek outlets report 6.1; USGS 6.0. Filed as protective response with prevention unproven as the classifier proposed. Of the Monitor's eight Greek sources, Newsbomb, GizChina Greece, in.gr and Alphafreepress state that users were alerted before the shaking (Newsbomb names Crete, the Dodecanese and farther away, by message and an audible signal like the 112 alert); Parapolitika is an explainer without that claim; eleftherostypos.gr and reader.gr returned HTTP 403 and ant1news.gr a TLS error, so they were not read. No Google statement about this event was found; web search was unavailable in this session, so sourcing is limited to the Monitor's list, USGS, Google's own pages and the Science abstract.", "source": "https://oecd.ai/en/incidents/2025-05-14-779c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys; https://europepmc.org/article/MED/40674485", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Disaster risk reduction / earthquake early warning", "segment": "Consumer smartphones (Android)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Newsbomb (2025). «Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς. Newsbomb.gr, 14 May 2025, 23:10.", "title": "«Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys", "date": "2025-05-14", "written_by": "independent", "quote": "Πολλοί κάτοικοι κοντινών περιοχών, όπως εκείνοι στην Κρήτη, στα Δωδεκάνησα αλλά και πιο μακριά ενημερώθηκαν έγκαιρα και έλαβαν ειδοποίηση στο κινητό τους λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση" }, { "kind": "news report", "citation": "GizChina Greece (2025). Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό. GizChina.gr, 14 May 2025.", "title": "Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό", "url": "https://www.gizchina.gr/?p=363710", "date": "2025-05-14", "written_by": "independent", "quote": "αρκετοί κάτοικοι των Δωδεκανήσων, αλλά και ευρύτερων περιοχών, έλαβαν ειδοποιήσεις στο κινητό τους, δευτερόλεπτα πριν το κύμα του σεισμού φτάσει σε αυτούς" }, { "kind": "news report", "citation": "in.gr (2025). Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό. in.gr, 15 May 2025, 11:02.", "title": "Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό", "url": "https://www.in.gr/2025/05/15/in-science/technology/pos-tha-energopoiisete-tin-efarmogi-tis-google-pou-proeidopoiei-gia-seismo", "date": "2025-05-15", "written_by": "independent", "quote": "Μια υπηρεσία έγκαιρης προειδοποίησης για σεισμούς που κρύβεται στο λειτουργικό σύστημα Android ήταν αυτή που ενημέρωσε πολλούς χρήστες για τον σεισμό των 6,1 βαθμών" }, { "kind": "news report", "citation": "Alphafreepress (2025). Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android. Alphafreepress.gr, 14 May 2025, 20:42.", "title": "Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android", "url": "https://www.alphafreepress.gr/2025/05/14/ellada/kasos-seismos-61-rixter-pos-leitourgei-to-systima-proeidopoiisis-sta-android", "date": "2025-05-14", "written_by": "independent", "quote": "το σύστημα σεισμικής ειδοποίησης της Google καταφέρνει να στείλει ενημέρωση στον πολίτη πριν φτάσει το κύμα της δόνησης στην περιοχή του" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 6.0 - 20 km SSE of Fry, Greece (event us7000pz2p, 2025-05-13 22:51:15 UTC, depth 76 km). USGS Earthquake Hazards Program.", "title": "M 6.0 - 20 km SSE of Fry, Greece", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pz2p", "date": "2025-05-13", "written_by": "independent", "quote": "M 6.0 - 20 km SSE of Fry, Greece" }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259. PMID 40674485.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "We show how smartphone-based earthquake detection algorithms can be implemented at scale and improved through postevent analysis." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0130-source-0001", "AIBD-0130-source-0002", "AIBD-0130-source-0003", "AIBD-0130-source-0004", "AIBD-0130-source-0005", "AIBD-0130-source-0006", "AIBD-0130-source-0007", "AIBD-0130-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Newsbomb (2025). «Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς. Newsbomb.gr, 14 May 2025, 23:10.", "title": "«Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys", "date": "2025-05-14", "written_by": "independent", "quote": "Πολλοί κάτοικοι κοντινών περιοχών, όπως εκείνοι στην Κρήτη, στα Δωδεκάνησα αλλά και πιο μακριά ενημερώθηκαν έγκαιρα και έλαβαν ειδοποίηση στο κινητό τους λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση" }, { "kind": "news report", "citation": "GizChina Greece (2025). Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό. GizChina.gr, 14 May 2025.", "title": "Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό", "url": "https://www.gizchina.gr/?p=363710", "date": "2025-05-14", "written_by": "independent", "quote": "αρκετοί κάτοικοι των Δωδεκανήσων, αλλά και ευρύτερων περιοχών, έλαβαν ειδοποιήσεις στο κινητό τους, δευτερόλεπτα πριν το κύμα του σεισμού φτάσει σε αυτούς" }, { "kind": "news report", "citation": "in.gr (2025). Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό. in.gr, 15 May 2025, 11:02.", "title": "Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό", "url": "https://www.in.gr/2025/05/15/in-science/technology/pos-tha-energopoiisete-tin-efarmogi-tis-google-pou-proeidopoiei-gia-seismo", "date": "2025-05-15", "written_by": "independent", "quote": "Μια υπηρεσία έγκαιρης προειδοποίησης για σεισμούς που κρύβεται στο λειτουργικό σύστημα Android ήταν αυτή που ενημέρωσε πολλούς χρήστες για τον σεισμό των 6,1 βαθμών" }, { "kind": "news report", "citation": "Alphafreepress (2025). Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android. Alphafreepress.gr, 14 May 2025, 20:42.", "title": "Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android", "url": "https://www.alphafreepress.gr/2025/05/14/ellada/kasos-seismos-61-rixter-pos-leitourgei-to-systima-proeidopoiisis-sta-android", "date": "2025-05-14", "written_by": "independent", "quote": "το σύστημα σεισμικής ειδοποίησης της Google καταφέρνει να στείλει ενημέρωση στον πολίτη πριν φτάσει το κύμα της δόνησης στην περιοχή του" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 6.0 - 20 km SSE of Fry, Greece (event us7000pz2p, 2025-05-13 22:51:15 UTC, depth 76 km). USGS Earthquake Hazards Program.", "title": "M 6.0 - 20 km SSE of Fry, Greece", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pz2p", "date": "2025-05-13", "written_by": "independent", "quote": "M 6.0 - 20 km SSE of Fry, Greece" }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259. PMID 40674485.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "We show how smartphone-based earthquake detection algorithms can be implemented at scale and improved through postevent analysis." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Android users in Crete and the Dodecanese got Google's quake alert seconds before the M6.0 (Greek press 6.1) Kasos quake of 14 May 2025.", "account": "At 01:51 local time on Wednesday 14 May 2025 (22:51 UTC on 13 May) a magnitude 6.0 (USGS; Greek outlets 6.1) earthquake struck. It struck 20 km south-south-east of Fry on Kasos, between Kasos and Karpathos, at 76 km depth. USGS logged 931 felt reports and a green alert level. Newsbomb, GizChina Greece, in.gr and Alphafreepress reported on 14 and 15 May on Google's Android Earthquake Alerts System. In Europe it detects quakes from phone accelerometers rather than seismographs. It notified users in Crete, the Dodecanese and farther away seconds before the shaking. Notice came by message and an audible signal similar to the 112 alert. None of the reports names a recipient, gives a lead time in seconds, or mentions damage or injuries." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Android Earthquake Alerts System (crowdsourced phone-accelerometer detection; alerts for magnitude 4.5 and above)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Google's Android Earthquake Alerts reached phones in Crete and the Dodecanese seconds before the 14 May 2025 M6.0 (Greek press 6.1) Kasos quake", "headline": "Google alerts Crete phones seconds before the M6.0 Kasos quake" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c434c4947e2cacd4aa85a380b8afca94ec0bad41d6ad884073cdbbb28fd816a4" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At 01:51 local time on Wednesday 14 May 2025 (22:51 UTC on 13 May) a magnitude 6.0 (USGS; Greek outlets 6.1) earthquake struck. It struck 20 km south-south-east of Fry on Kasos, between Kasos and Karpathos, at 76 km depth. USGS logged 931 felt reports and a green alert level. Newsbomb, GizChina Greece, in.gr and Alphafreepress reported on 14 and 15 May on Google's Android Earthquake Alerts System. In Europe it detects quakes from phone accelerometers rather than seismographs. It notified users in Crete, the Dodecanese and farther away seconds before the shaking. Notice came by message and an audible signal similar to the 112 alert. None of the reports names a recipient, gives a lead time in seconds, or mentions damage or injuries.", "replication": { "produced_by": [ "A magnitude 6.0 (USGS) quake above the system's 4.5 alert threshold, centered offshore south of Kasos at about 76 km depth. A push alert could therefore outrun the slower waves to Crete and the Dodecanese.", "Enough Android phones near the epicentre with the Earthquake Alerts setting on to trigger Google's server-side detection. In Europe the system uses the accelerometers in phones instead of seismographs (in.gr).", "Receiving phones with location (GPS) enabled and an internet connection, which Alphafreepress lists as requirements for the alerts" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports that only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. The Greek reports document no protective action by anyone who received the alert.", "The phone must have the Earthquake Alerts setting enabled (Alphafreepress: Settings > Safety & Emergency > Earthquake Alerts). The feature must be supported in the region." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0220", "case_url": "records/AIBD-0220.html", "reason": "The developer is headquartered in Netherlands; the case names Canada.", "details": [ { "label": "Developer headquarters", "value": "Netherlands" }, { "label": "Case jurisdictions", "value": "Canada" }, { "label": "Case location", "value": "Calgary, Canada" } ], "filters": { "origin": "Netherlands", "destination": [ "Canada" ] }, "record": { "id": "AIBD-0220", "title": "Envision Glasses read street signs, menus and scenes for Wes Ramage in Calgary, bringing comfort he had not had in years", "summary": "Globe and Mail, 26 December 2023: Wes Ramage of Calgary uses Envision Glasses (Google Glass plus Envision's AI) to read signs for transit, read menus, find chairs and objects and describe scenes.", "date_observed": "2023-12-26", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In a Globe and Mail report of 26 December 2023, Joe Castaldo described how Wes Ramage of Calgary, who is blind, uses Envision Glasses. They are a Google Glass headset with Envision's AI software. He uses them to describe scenes, identify clothing colors, find empty chairs and misplaced objects, read street signs when navigating transit and read menus. Ramage said the glasses bring a level of comfort he had not had in many years. He described heading out each morning with his glasses, keys and wallet. The article gives a price of US$1,899 to US$3,499, accessed in his case through a provincial assistance program. It also profiles Nayla Farah of Toronto using the Be My AI app, covered by an existing record. Envision's product page says the glasses are built on Google Glass Enterprise Edition 2. They offer Instant Text, Scan Text, Describe Scene and an Ask Envision feature on an OpenAI model. It does not date the individual uses.", "category": "benefit created", "unexpected": false, "purpose": "Envision Glasses are built to speak out text and surroundings for blind and low-vision users. Reading signs and menus is that purpose.", "system": "Envision Glasses: Google Glass Enterprise Edition 2 running Envision's text recognition, scene description and Ask Envision features", "model_version": "GPT-4", "developer": "Envision", "deployer": "Wes Ramage (individual user, Calgary)", "location": "Calgary, Canada", "domain": "Accessibility / visual assistance", "segment": "Blind and low-vision users (wearable reading assistant)", "benefit_type": "Improved Accessibility", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 1, "reason": "A named user reads signs, menus and scenes independently and reports lasting comfort. A small daily-living benefit documented by a reporter, not measured." } ], "ai_role": { "status": "assessed", "statement": "The glasses' text recognition and scene description produced the readings Ramage relies on. The Globe article is the only outcome source. It attributes the comfort to the glasses in the user's own words. No clinical or task measurement exists in the sources." }, "evidence": [ { "kind": "news report", "citation": "Castaldo J (26 Dec 2023, updated 27 Dec 2023). For some blind and low-vision people, AI glasses unlock a new independence. The Globe and Mail (named users Wes Ramage, Calgary; Nayla Farah, Toronto).", "title": "For some blind and low-vision people, AI glasses unlock a new independence", "url": "https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/", "date": "2023-12-26", "written_by": "independent", "quote": "That brings a level of comfort I have not had in so many years." }, { "kind": "press release", "citation": "Envision. Envision Glasses product page (maker's page).", "title": "Envision Glasses", "url": "https://www.letsenvision.com/glasses", "date": null, "written_by": "makers", "quote": "Lightweight glasses with a camera and direct speaker, Envision Glasses speak out text and environmental information." } ], "replication": { "produced_by": [ "A head-worn camera with on-device OCR and cloud scene description", "Funding through a provincial assistance program for a device priced at US$1,899 to US$3,499", "A user who learned the environment and routines with the device" ], "must_hold": [ "Affordability or subsidy for the hardware", "Legible text and adequate lighting for OCR", "Continued software support (the maker sells annual feature updates)" ], "echoes": [ { "date": "2023-12-26", "setting": "Toronto: Nayla Farah using the Be My AI smartphone app for photo and scene descriptions (same article)", "model_version": "GPT-4 (Be My AI, per the article's references to ChatGPT and GPT-4)", "outcome": "recurred", "source": "The Globe and Mail, 26 Dec 2023, https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/" } ] }, "paired_incident": null, "headline": "Envision Glasses read street signs and menus for Wes Ramage", "metric": null, "comparator": { "against": "navigating transit without the glasses", "before": "Before, a bus would drive right by me because I'm standing at a yield sign", "after": "Now he can ask the glasses for a description to ensure he's in the right place.", "basis": "assumed", "quote": "\"Before, a bus would drive right by me because I'm standing at a yield sign,\" he said. Now he can ask the glasses for a description to ensure he's in the right place. \"That brings a level of comfort I have not had in so many years.\"", "url": "https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/" }, "later": { "status": "persisted", "date": null, "note": "Envision's product page, read on 21 October 2026, still sells the glasses and now says Ask Envision uses GPT-5 by OpenAI, so the product continued with a newer model; the page does not date the change.", "quote": "Introducing \"Ask Envision\", a revolutionary feature that enables people who are blind or low-vision to scan documents and ask questions using GPT-5 by OpenAI.", "url": "https://www.letsenvision.com/glasses" }, "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 cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/; https://www.letsenvision.com/glasses; https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.letsenvision.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" } ], "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-0220", "catalog_version": "1.0.0", "source_fingerprint": "0c21b15adc893137d2d87a7739594b7188ee7bc82ce998ecb5aa9655a04d5adf", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0220-source-0001", "AIBD-0220-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": "Envision", "deployer": "Wes Ramage (individual user, Calgary)" } }, "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 glasses' text recognition and scene description produced the readings Ramage relies on. The Globe article is the only outcome source. It attributes the comfort to the glasses in the user's own words. No clinical or task measurement exists in the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The glasses' text recognition and scene description produced the readings Ramage relies on. The Globe article is the only outcome source. It attributes the comfort to the glasses in the user's own words. No clinical or task measurement exists in 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": "A named user reads signs, menus and scenes independently and reports lasting comfort. A small daily-living benefit documented by a reporter, not measured." } ] } }, "provenance": { "kind": "legacy_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. 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": "A named user reads signs, menus and scenes independently and reports lasting comfort. A small daily-living benefit documented by a reporter, not measured." } ] } }, "provenance": { "kind": "legacy_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": "A named user reads signs, menus and scenes independently and reports lasting comfort. A small daily-living benefit documented by a reporter, not measured." } ] } }, "provenance": { "kind": "legacy_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" 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}, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In a Globe and Mail report of 26 December 2023, Joe Castaldo described how Wes Ramage of Calgary, who is blind, uses Envision Glasses. They are a Google Glass headset with Envision's AI software. He uses them to describe scenes, identify clothing colors, find empty chairs and misplaced objects, read street signs when navigating transit and read menus. Ramage said the glasses bring a level of comfort he had not had in many years. He described heading out each morning with his glasses, keys and wallet. The article gives a price of US$1,899 to US$3,499, accessed in his case through a provincial assistance program. It also profiles Nayla Farah of Toronto using the Be My AI app, covered by an existing record. Envision's product page says the glasses are built on Google Glass Enterprise Edition 2. They offer Instant Text, Scan Text, Describe Scene and an Ask Envision feature on an OpenAI model. It does not date the individual uses.", "benefit_type": "Improved Accessibility" } }, "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": "A named user reads signs, menus and scenes independently and reports lasting comfort. 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Reading signs and menus 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": "Calgary, Canada", "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": "Envision Glasses are built to speak out text and surroundings for blind and low-vision users. Reading signs and menus 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": "2023-12-26", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/; https://www.letsenvision.com/glasses; https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.letsenvision.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" } ] } }, "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 head-worn camera with on-device OCR and cloud scene description", "Funding through a provincial assistance program for a device priced at US$1,899 to US$3,499", "A user who learned the environment and routines with the device" ], "must_hold": [ "Affordability or subsidy for the hardware", "Legible text and adequate lighting for OCR", "Continued software support (the maker sells annual feature updates)" ], "echoes": [ { "date": "2023-12-26", "setting": "Toronto: Nayla Farah using the Be My AI smartphone app for photo and scene descriptions (same article)", "model_version": "GPT-4 (Be My AI, per the article's references to ChatGPT and GPT-4)", "outcome": "recurred", "source": "The Globe and Mail, 26 Dec 2023, https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/" } ] } } }, "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 head-worn camera with on-device OCR and cloud scene description", "Funding through a provincial assistance program for a device priced at US$1,899 to US$3,499", "A user who learned the environment and routines with the device" ], "must_hold": [ "Affordability or subsidy for the hardware", "Legible text and adequate lighting for OCR", "Continued software support (the maker sells annual feature updates)" ], "echoes": [ { "date": "2023-12-26", "setting": "Toronto: Nayla Farah using the Be My AI smartphone app for photo and scene descriptions (same article)", "model_version": "GPT-4 (Be My AI, per the article's references to ChatGPT and GPT-4)", "outcome": "recurred", "source": "The Globe and Mail, 26 Dec 2023, https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/; https://www.letsenvision.com/glasses; https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.letsenvision.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" } ] } }, "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": "Accessibility / visual assistance", "segment": "Blind and low-vision users (wearable reading assistant)" } }, "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": "Castaldo J (26 Dec 2023, updated 27 Dec 2023). For some blind and low-vision people, AI glasses unlock a new independence. The Globe and Mail (named users Wes Ramage, Calgary; Nayla Farah, Toronto).", "title": "For some blind and low-vision people, AI glasses unlock a new independence", "url": "https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/", "date": "2023-12-26", "written_by": "independent", "quote": "That brings a level of comfort I have not had in so many years." }, { "kind": "press release", "citation": "Envision. Envision Glasses product page (maker's page).", "title": "Envision Glasses", "url": "https://www.letsenvision.com/glasses", "written_by": "makers", "quote": "Lightweight glasses with a camera and direct speaker, Envision Glasses speak out text and environmental information." } ] } }, "provenance": { "kind": "legacy_record", "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-0220-source-0001", "AIBD-0220-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": "Castaldo J (26 Dec 2023, updated 27 Dec 2023). For some blind and low-vision people, AI glasses unlock a new independence. The Globe and Mail (named users Wes Ramage, Calgary; Nayla Farah, Toronto).", "title": "For some blind and low-vision people, AI glasses unlock a new independence", "url": "https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/", "date": "2023-12-26", "written_by": "independent", "quote": "That brings a level of comfort I have not had in so many years." }, { "kind": "press release", "citation": "Envision. Envision Glasses product page (maker's page).", "title": "Envision Glasses", "url": "https://www.letsenvision.com/glasses", "written_by": "makers", "quote": "Lightweight glasses with a camera and direct speaker, Envision Glasses speak out text and environmental information." } ] } }, "provenance": { "kind": "legacy_record", "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": "Globe and Mail, 26 December 2023: Wes Ramage of Calgary uses Envision Glasses (Google Glass plus Envision's AI) to read signs for transit, read menus, find chairs and objects and describe scenes.", "account": "In a Globe and Mail report of 26 December 2023, Joe Castaldo described how Wes Ramage of Calgary, who is blind, uses Envision Glasses. They are a Google Glass headset with Envision's AI software. He uses them to describe scenes, identify clothing colors, find empty chairs and misplaced objects, read street signs when navigating transit and read menus. Ramage said the glasses bring a level of comfort he had not had in many years. He described heading out each morning with his glasses, keys and wallet. The article gives a price of US$1,899 to US$3,499, accessed in his case through a provincial assistance program. It also profiles Nayla Farah of Toronto using the Be My AI app, covered by an existing record. Envision's product page says the glasses are built on Google Glass Enterprise Edition 2. They offer Instant Text, Scan Text, Describe Scene and an Ask Envision feature on an OpenAI model. It does not date the individual uses." } }, "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 glasses recognized text and described scenes aloud." }, "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 for the Ask Envision feature." }, "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": "Envision Glasses: Google Glass Enterprise Edition 2 running Envision's text recognition, scene description and Ask Envision features" } }, "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": "Envision" } }, "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": "Envision Glasses read street signs, menus and scenes for Wes Ramage in Calgary, bringing comfort he had not had in years", "headline": "Envision Glasses read street signs and menus for Wes Ramage" } }, "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": [ "0c21b15adc893137d2d87a7739594b7188ee7bc82ce998ecb5aa9655a04d5adf" ], "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 a Globe and Mail report of 26 December 2023, Joe Castaldo described how Wes Ramage of Calgary, who is blind, uses Envision Glasses. They are a Google Glass headset with Envision's AI software. He uses them to describe scenes, identify clothing colors, find empty chairs and misplaced objects, read street signs when navigating transit and read menus. Ramage said the glasses bring a level of comfort he had not had in many years. He described heading out each morning with his glasses, keys and wallet. The article gives a price of US$1,899 to US$3,499, accessed in his case through a provincial assistance program. It also profiles Nayla Farah of Toronto using the Be My AI app, covered by an existing record. Envision's product page says the glasses are built on Google Glass Enterprise Edition 2. They offer Instant Text, Scan Text, Describe Scene and an Ask Envision feature on an OpenAI model. It does not date the individual uses.", "replication": { "produced_by": [ "A head-worn camera with on-device OCR and cloud scene description", "Funding through a provincial assistance program for a device priced at US$1,899 to US$3,499", "A user who learned the environment and routines with the device" ], "must_hold": [ "Affordability or subsidy for the hardware", "Legible text and adequate lighting for OCR", "Continued software support (the maker sells annual feature updates)" ], "echoes": [ { "date": "2023-12-26", "setting": "Toronto: Nayla Farah using the Be My AI smartphone app for photo and scene descriptions (same article)", "model_version": "GPT-4 (Be My AI, per the article's references to ChatGPT and GPT-4)", "outcome": "recurred", "source": "The Globe and Mail, 26 Dec 2023, https://www.theglobeandmail.com/business/article-ai-smart-glasses-apps-vision/" } ] } } }, "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-0318", "case_url": "records/AIBD-0318.html", "reason": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Kishanpur Wildlife Sanctuary, Dudhwa Tiger Reserve, Uttar Pradesh, India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "record": { "id": "AIBD-0318", "title": "TrailGuard AI camera in Kishanpur Wildlife Sanctuary transmits images of armed poachers on two nights; Dudhwa Tiger Reserve officials identify them and investigate", "summary": "Dertien et al. (BioScience 2023): one TrailGuard AI unit captured poachers “carrying a gun and knives” on two dates and a poached chital; officials identified them. Arrests claimed only in releases.", "date_observed": "2023-09-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Dertien, Negi, Dinerstein and colleagues (BioScience 73(10):748–757, published online 14 September 2023) report deploying the TrailGuard AI camera-alert system. The paper does not date the detections. The system “runs on-the-edge artificial intelligence algorithms to detect tigers and poachers and transmit real-time images”. It was deployed in the Kanha-Pench corridor and in Dudhwa Tiger Reserve, Uttar Pradesh. All seven Dudhwa units transmitted images; three transmitted objects of interest to forest department officials. “One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates, and a presumably poached chital (Axis axis) on the second detection”. “Authorities were able to quickly identify the individuals via the transmitted images and were investigating the matter”. Tigers were detected at the same spot three days before the second poacher detection. The authors include RESOLVE and CVEDIA staff (the system's developers) alongside Dudhwa Tiger Reserve, Wildlife Institute of India and Global Tiger Forum co-authors. AFP (Taipei Times, 6 October 2023) reported the study's claim of “identifying a team of poachers”. A Clemson University release of 15 September 2023 carries Nightjar/RESOLVE boilerplate. It states that the alerts “led the authorities to the arrest of these individuals for poaching a few weeks later”. It quotes Sanjay Kumar Pathak, former field director of Dudhwa, that the system “led to the detection and arrest of poachers in Dudhwa”. The Hindu (16 September 2023) cited a person involved in the experiment. Poachers caught on camera had a year later helped forest authorities secure a conviction. It quoted Piyush Yadav of Nightjar, the manufacturer, that “A year down, these poachers were arrested with images from TrailGuard playing a key role”. The article's lead puts the capture in Kanha-Pench and the Yadav quote in Dudhwa. No forest-department statement or court record of an arrest was found.", "category": "protective response", "unexpected": false, "purpose": "Built to detect tigers and people on trails with embedded AI and send real-time images to forest officials. Detecting and transmitting the poachers' images was that purpose.", "system": "TrailGuard AI camera-alert system (embedded AI on an Intel computer-vision chip; cellular transmission)", "model_version": null, "developer": "Nightjar / RESOLVE, Washington DC (inventor Steve Gulick), with CVEDIA Ltd", "deployer": "Dudhwa Tiger Reserve, Uttar Pradesh Forest Department, with the Global Tiger Forum, National Tiger Conservation Authority and Clemson University", "location": "Kishanpur Wildlife Sanctuary, Dudhwa Tiger Reserve, Uttar Pradesh, India", "domain": "Conservation / anti-poaching law enforcement", "segment": "Anti-poaching enforcement", "benefit_type": "Reduced Violence", "taxonomy_note": "The paper documents detection and identification of armed poachers for investigation, with arrests claimed only in releases, and the protected asset is wildlife.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 1, "reason": "Forest officials received the images in near real time and identified the individuals. The paper documents an investigation, not an arrest or prosecution, so the recorded gain is an investigative lead." }, { "group": "societal", "depth": null, "reason": "Protection of the reserve's wildlife from this gang is claimed through arrests only in the Clemson release, developer material and The Hindu's second-hand report. The paper notes a chital presumably poached before the second detection." } ], "ai_role": { "status": "assessed", "statement": "The camera's on-board classifier filtered motion triggers to people and tigers and transmitted the poachers' images to officials' phones within about a minute. Identification and investigation were done by officials. The peer-reviewed account is co-authored by the developers (RESOLVE, CVEDIA) with Dudhwa and Wildlife Institute of India staff and stops at “investigating”. The arrest claim rests on a Clemson release quoting the ex-field director, developer press releases and The Hindu quoting the manufacturer and an unnamed participant." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Dertien JS, Negi H, Dinerstein E, Krishnamurthy R, Negi HS, Gopal R, Gulick S, Pathak SK, Kapoor M, Yadav P, Benitez M, Ferreira M, Wijnveen AJ, Lee ATL, Wright B, Baldwin RF (2023). Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system. BioScience 73(10):748-757, doi:10.1093/biosci/biad076 (PMC10580963; authors include RESOLVE and CVEDIA developer staff and Dudhwa Tiger Reserve staff).", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/", "date": "2023-09-14", "written_by": "makers", "quote": "One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates" }, { "kind": "peer-reviewed study", "citation": "Dertien et al. (2023), same paper, results on the Dudhwa deployment.", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/", "date": "2023-09-14", "written_by": "makers", "quote": "Authorities were able to quickly identify the individuals via the transmitted images and were investigating the matter" }, { "kind": "news report", "citation": "AFP (2023). AI of the tiger: Tiny camera 'protects' predator - and people. Taipei Times, 6 October 2023.", "title": "AI of the tiger: Tiny camera 'protects' predator - and people", "url": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276", "date": "2023-10-06", "written_by": "independent", "quote": "The research claims the cameras were immediately effective, picking up a tiger just 300 meters from a village, and on another occasion identifying a team of poachers." }, { "kind": "press release", "citation": "Clemson News (2023). Clemson students' deployment of novel camera alert system, TrailGuard AI, featured in BioScience, promotes coexistence of tigers, humans. 15 September 2023 (university release of the lead author's institution; carries Nightjar/RESOLVE boilerplate; quotes former Dudhwa field director Sanjay Kumar Pathak).", "title": "Clemson students' deployment of novel camera alert system, TrailGuard AI, featured in BioScience, promotes coexistence of tigers, humans", "url": "https://news.clemson.edu/clemson-students-deployment-of-novel-camera-alert-system-trailguard-ai-featured-in-bioscience-promotes-coexistence-of-tigers-humans/", "date": "2023-09-15", "written_by": "deployer", "quote": "TrailGuard AI offers park protection that led to the detection and arrest of poachers in Dudhwa," }, { "kind": "news report", "citation": "The Hindu (2023). AI cameras to curb poaching in Madhya Pradesh. 16 Sep 2023.", "title": "AI cameras to curb poaching in Madhya Pradesh", "url": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "date": "2023-09-16", "written_by": "independent", "quote": "A year down, these poachers were arrested with images from TrailGuard playing a key role" }, { "kind": "news report", "citation": "The Hindu (2023). Same article, lead paragraph.", "title": "AI cameras to curb poaching in Madhya Pradesh (conviction claim)", "url": "https://web.archive.org/web/20240808102127/https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "date": "2023-09-16", "written_by": "independent", "quote": "caught poachers on camera that — a year down — helped forest authorities secure a conviction, according to a person involved in the experiment" } ], "replication": { "produced_by": [ "Camouflaged camera units on trails inside the reserve used by both tigers and poachers", "Cellular coverage allowing images to reach forest officials' phones in under a minute" ], "must_hold": [ "Officials act on the images while the poachers can still be found. The paper records identification and investigation only", "The embedded classifier suppresses non-target triggers so that officials keep trusting the alerts" ], "echoes": [ { "date": null, "setting": "Similipal Tiger Reserve, Odisha (recorded separately as AIBD-0102); The Better India (30 June 2025) reports “96 poacher arrests and 86 firearms seized within ten months” from TrailGuard AI deployments there", "model_version": null, "outcome": "recurred", "source": "https://thebetterindia.com/457142/ai-applications-that-tackle-poaching-india/" } ] }, "paired_incident": null, "headline": "TrailGuard AI transmits images of armed poachers in Kishanpur", "metric": null, "comparator": { "against": "camera traps without real-time notification", "before": "camera traps have lacked the real-time notification capabilities and practicality necessary for prevention of poaching", "after": "notifications of tiger presence via email or push notification between 30 and 42 seconds after detections", "basis": "assumed", "quote": "Until recently, camera traps have lacked the real-time notification capabilities and practicality necessary for prevention of poaching and human–wildlife conflict.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/" }, "later": { "status": "persisted", "date": "2023-09-16", "note": "Two days after the paper, The Hindu quoted Nightjar's Piyush Yadav that the Dudhwa poachers were arrested about a year after the images, with TrailGuard images playing a key role; Clemson's release of 15 September 2023 says the arrests came a few weeks later, and no forest department or court record was found.", "quote": "But in the Dudhwa [tiger reserve] we learnt that not only could we track [in this case] tigers approaching a village but we also caught a gang of poachers on camera who were walking with a hunted chital. A year down, these poachers were arrested with images from TrailGuard playing a key role,", "url": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece" }, "value": null, "developer_country": "United States", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged the Dudhwa poacher material from a second write-up: added The Hindu's report of the arrest and conviction claims; the BioScience paper is marked as written by the system's developers.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/; https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.3blmedia.com/news/trailguard-ai-named-times-list-best-inventions-2023; https://en.wikipedia.org/wiki/Dudhwa_Tiger_Reserve", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports, the makers' peer-reviewed study and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0318", "catalog_version": "1.0.0", "source_fingerprint": "3da3ea7fd080d05b95a11dd87baa78b9d68631cb63ea47cec85c691e5b783a51", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0318-source-0001", "AIBD-0318-source-0002", "AIBD-0318-source-0003", "AIBD-0318-source-0004", "AIBD-0318-source-0005", "AIBD-0318-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Nightjar / RESOLVE, Washington DC (inventor Steve Gulick), with CVEDIA Ltd", "deployer": "Dudhwa Tiger Reserve, Uttar Pradesh Forest Department, with the Global Tiger Forum, National Tiger Conservation Authority and Clemson University" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The camera's on-board classifier filtered motion triggers to people and tigers and transmitted the poachers' images to officials' phones within about a minute. Identification and investigation were done by officials. The peer-reviewed account is co-authored by the developers (RESOLVE, CVEDIA) with Dudhwa and Wildlife Institute of India staff and stops at “investigating”. The arrest claim rests on a Clemson release quoting the ex-field director, developer press releases and The Hindu quoting the manufacturer and an unnamed participant." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The camera's on-board classifier filtered motion triggers to people and tigers and transmitted the poachers' images to officials' phones within about a minute. Identification and investigation were done by officials. The peer-reviewed account is co-authored by the developers (RESOLVE, CVEDIA) with Dudhwa and Wildlife Institute of India staff and stops at “investigating”. The arrest claim rests on a Clemson release quoting the ex-field director, developer press releases and The Hindu quoting the manufacturer and an unnamed participant." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Forest officials received the images in near real time and identified the individuals. 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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": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Kanha-Pench corridor, Madhya Pradesh, India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "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. 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" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": 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{ "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-0116", "case_url": "records/AIBD-0116.html", "reason": "The developer is headquartered in United States; the case names United Kingdom.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "United Kingdom" }, { "label": "Case location", "value": "York, United Kingdom" } ], "filters": { "origin": "United States", "destination": [ "United Kingdom" ] }, "record": { "id": "AIBD-0116", "title": "York student has a £60 parking fine withdrawn after sending an appeal letter drafted by ChatGPT", "summary": "Millie Houlton, 22, asked ChatGPT to draft an appeal against a wrongly issued £60 parking fine; York City Council withdrew it.", "date_observed": "2023-03-31", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Millie Houlton, a 22-year-old final-year events and business management student at York St John University, received a £60 parking notice from York City Council. It was for parking on her own street, where she holds a permit. Busy with coursework, she was tempted to pay. Instead she typed into ChatGPT “please help me write a letter to the council, they gave me a parking ticket”. She gave the location, reference number and circumstances. The letter came back in minutes. Within days the council told her the appeal was accepted and the fine withdrawn. The BBC and ITV News reported the case on 30 and 31 March 2023, and she shared it on TikTok.", "category": "benefit created", "unexpected": false, "purpose": "ChatGPT is a general-purpose conversational assistant built to answer questions and draft text on request. Writing a formal appeal letter from the user's facts is squarely within that purpose.", "system": "ChatGPT", "model_version": null, "developer": "OpenAI", "deployer": "OpenAI", "location": "York, United Kingdom", "domain": "Public administration (parking enforcement appeals)", "segment": "Consumer", "benefit_type": "Reduced Task Time", "taxonomy_note": "The good is a wrongly issued 60-pound fine withdrawn, and the type names the minutes-long drafting of the appeal that produced it.", "registry_ids": { "oecd_aim": "2023-03-31-6b48" }, "ratings": [ { "group": "personal", "depth": 1, "reason": "One identifiable person avoided a £60 fine she considered wrongly issued and the time of drafting an appeal she said she found hard to articulate." } ], "ai_role": { "status": "assessed", "statement": "ChatGPT drafted the appeal from the facts the student entered. The ground for withdrawal was her valid permit, which a human-written appeal could also have stated. The system's contribution was overcoming the effort barrier. She said she would otherwise have paid rather than spend time on a response. The council's reasoning has not been published, so the letter's causal weight is inferred, not documented." }, "evidence": [ { "kind": "news report", "citation": "BBC News (2023). York student uses AI chatbot to get parking fine revoked. BBC News, York & North Yorkshire, 31 March 2023.", "title": "York student uses AI chatbot to get parking fine revoked", "url": "https://www.bbc.co.uk/news/uk-england-york-north-yorkshire-65126772", "date": "2023-03-31", "written_by": "independent", "quote": "A student has successfully appealed against a £60 parking fine by using a letter written by an artificial intelligence chatbot." }, { "kind": "news report", "citation": "ITV News Calendar (2023). York student uses AI 'chatbot' to get £60 parking fine overturned. ITV News, 31 March 2023.", "title": "York student uses AI 'chatbot' to get £60 parking fine overturned", "url": "https://www.itv.com/news/calendar/2023-03-31/appeal-written-by-ai-chatbot-helps-student-overturn-parking-fine", "date": "2023-03-31", "written_by": "independent", "quote": "Within days she was notified that the appeal had been accepted." }, { "kind": "news report", "citation": "Ali, A. / Yorkshire Post (2023). Student overturns £60 parking ticket using ChatGPT. Yorkshire Post, 31 March 2023.", "title": "Student overturns £60 parking ticket using ChatGPT", "url": "https://www.yorkshirepost.co.uk/read-this/student-overturns-ps60-parking-ticket-using-chatgpt-4088236", "date": "2023-03-31", "written_by": "independent", "quote": "Millie Houlton, a 22-year-old from York, used the language tool to write a formal appeal to York City Council." }, { "kind": "news report", "citation": "Colton, E. / Fox News (2023). How an AI chatbot allegedly helped student terminate parking fine: 'Very relieved'. Fox News, 2 April 2023.", "title": "How an AI chatbot allegedly helped student terminate parking fine: 'Very relieved'", "url": "https://www.foxnews.com/world/how-ai-chatbot-allegedly-helped-student-terminate-parking-fine-very-relieved", "date": "2023-04-02", "written_by": "independent", "quote": "Houlton said she was \"very relieved\" when the local authority revoked the fine." } ], "replication": { "produced_by": [ "The appellant had a genuine ground (a valid residents' permit) and the specific facts to hand: location, date, reference number and circumstances.", "The appeal channel accepted a written letter, so a well-formed draft was the whole task.", "The user already used ChatGPT for work emails and so trusted it enough to try it rather than pay." ], "must_hold": [ "The facts entered are true and the appellant checks the draft before sending. 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The model can produce plausible but incorrect text, as the BBC noted OpenAI admits.", "The authority judges the appeal on its merits, so the outcome rests on the permit, not on prose quality." ], "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. The Monitor's listed sources are Macedonian-language reposts; the record is built on the BBC, ITV News, Yorkshire Post and Fox News originals. The BBC page displays 31 March 2023 while its metadata timestamp is 2023–03–30T16:21Z; the dates of the ticket and of the withdrawal letter are not reported, so the date observed is the reporting date. The Monitor's “hazard” label is dropped: the attached rationale concerns a different story (expert warnings about AI).", "source": "https://oecd.ai/en/incidents/2023-03-31-6b48", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Task Time under Trustible's full taxonomy of 23 types; it was Improved 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 the value as stated from the cited sources.", "source": "https://www.bbc.co.uk/news/uk-england-york-north-yorkshire-65126772", "editor": "AIBD" }, { "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: 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 administration (parking enforcement appeals)", "segment": "Consumer" } }, "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": "BBC News (2023). York student uses AI chatbot to get parking fine revoked. BBC News, York & North Yorkshire, 31 March 2023.", "title": "York student uses AI chatbot to get parking fine revoked", "url": "https://www.bbc.co.uk/news/uk-england-york-north-yorkshire-65126772", "date": "2023-03-31", "written_by": "independent", "quote": "A student has successfully appealed against a £60 parking fine by using a letter written by an artificial intelligence chatbot." }, { "kind": "news report", "citation": "ITV News Calendar (2023). York student uses AI 'chatbot' to get £60 parking fine overturned. ITV News, 31 March 2023.", "title": "York student uses AI 'chatbot' to get £60 parking fine overturned", "url": "https://www.itv.com/news/calendar/2023-03-31/appeal-written-by-ai-chatbot-helps-student-overturn-parking-fine", "date": "2023-03-31", "written_by": "independent", "quote": "Within days she was notified that the appeal had been accepted." }, { "kind": "news report", "citation": "Ali, A. / Yorkshire Post (2023). Student overturns £60 parking ticket using ChatGPT. Yorkshire Post, 31 March 2023.", "title": "Student overturns £60 parking ticket using ChatGPT", "url": "https://www.yorkshirepost.co.uk/read-this/student-overturns-ps60-parking-ticket-using-chatgpt-4088236", "date": "2023-03-31", "written_by": "independent", "quote": "Millie Houlton, a 22-year-old from York, used the language tool to write a formal appeal to York City Council." }, { "kind": "news report", "citation": "Colton, E. / Fox News (2023). How an AI chatbot allegedly helped student terminate parking fine: 'Very relieved'. Fox News, 2 April 2023.", "title": "How an AI chatbot allegedly helped student terminate parking fine: 'Very relieved'", "url": "https://www.foxnews.com/world/how-ai-chatbot-allegedly-helped-student-terminate-parking-fine-very-relieved", "date": "2023-04-02", "written_by": "independent", "quote": "Houlton said she was \"very relieved\" when the local authority revoked the fine." } ] } }, "provenance": { "kind": "legacy_record", "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-0116-source-0001", "AIBD-0116-source-0002", "AIBD-0116-source-0003", "AIBD-0116-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": "BBC News (2023). York student uses AI chatbot to get parking fine revoked. BBC News, York & North Yorkshire, 31 March 2023.", "title": "York student uses AI chatbot to get parking fine revoked", "url": "https://www.bbc.co.uk/news/uk-england-york-north-yorkshire-65126772", "date": "2023-03-31", "written_by": "independent", "quote": "A student has successfully appealed against a £60 parking fine by using a letter written by an artificial intelligence chatbot." }, { "kind": "news report", "citation": "ITV News Calendar (2023). York student uses AI 'chatbot' to get £60 parking fine overturned. ITV News, 31 March 2023.", "title": "York student uses AI 'chatbot' to get £60 parking fine overturned", "url": "https://www.itv.com/news/calendar/2023-03-31/appeal-written-by-ai-chatbot-helps-student-overturn-parking-fine", "date": "2023-03-31", "written_by": "independent", "quote": "Within days she was notified that the appeal had been accepted." }, { "kind": "news report", "citation": "Ali, A. / Yorkshire Post (2023). Student overturns £60 parking ticket using ChatGPT. Yorkshire Post, 31 March 2023.", "title": "Student overturns £60 parking ticket using ChatGPT", "url": "https://www.yorkshirepost.co.uk/read-this/student-overturns-ps60-parking-ticket-using-chatgpt-4088236", "date": "2023-03-31", "written_by": "independent", "quote": "Millie Houlton, a 22-year-old from York, used the language tool to write a formal appeal to York City Council." }, { "kind": "news report", "citation": "Colton, E. / Fox News (2023). How an AI chatbot allegedly helped student terminate parking fine: 'Very relieved'. Fox News, 2 April 2023.", "title": "How an AI chatbot allegedly helped student terminate parking fine: 'Very relieved'", "url": "https://www.foxnews.com/world/how-ai-chatbot-allegedly-helped-student-terminate-parking-fine-very-relieved", "date": "2023-04-02", "written_by": "independent", "quote": "Houlton said she was \"very relieved\" when the local authority revoked the fine." } ] } }, "provenance": { "kind": "legacy_record", "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": "Millie Houlton, 22, asked ChatGPT to draft an appeal against a wrongly issued £60 parking fine; York City Council withdrew it.", "account": "Millie Houlton, a 22-year-old final-year events and business management student at York St John University, received a £60 parking notice from York City Council. It was for parking on her own street, where she holds a permit. Busy with coursework, she was tempted to pay. Instead she typed into ChatGPT “please help me write a letter to the council, they gave me a parking ticket”. She gave the location, reference number and circumstances. The letter came back in minutes. Within days the council told her the appeal was accepted and the fine withdrawn. The BBC and ITV News reported the case on 30 and 31 March 2023, and she shared it on TikTok." } }, "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": "York student has a £60 parking fine withdrawn after sending an appeal letter drafted by ChatGPT", "headline": "ChatGPT appeal letter gets a York student's £60 fine withdrawn" } }, "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": [ "a8fb11627b1e865bc3cad7ab5718ad45bb284af62153e90166cfc52ca1c35af1" ], "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": "Millie Houlton, a 22-year-old final-year events and business management student at York St John University, received a £60 parking notice from York City Council. It was for parking on her own street, where she holds a permit. Busy with coursework, she was tempted to pay. Instead she typed into ChatGPT “please help me write a letter to the council, they gave me a parking ticket”. She gave the location, reference number and circumstances. The letter came back in minutes. Within days the council told her the appeal was accepted and the fine withdrawn. The BBC and ITV News reported the case on 30 and 31 March 2023, and she shared it on TikTok.", "replication": { "produced_by": [ "The appellant had a genuine ground (a valid residents' permit) and the specific facts to hand: location, date, reference number and circumstances.", "The appeal channel accepted a written letter, so a well-formed draft was the whole task.", "The user already used ChatGPT for work emails and so trusted it enough to try it rather than pay." ], "must_hold": [ "The facts entered are true and the appellant checks the draft before sending. The model can produce plausible but incorrect text, as the BBC noted OpenAI admits.", "The authority judges the appeal on its merits, so the outcome rests on the permit, not on prose quality." ], "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-0111", "case_url": "records/AIBD-0111.html", "reason": "The developer is headquartered in United States; the case names Turkey.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Turkey" }, { "label": "Case location", "value": "Alanya, Turkey" } ], "filters": { "origin": "United States", "destination": [ "Turkey" ] }, "record": { "id": "AIBD-0111", "title": "Google's Android Earthquake Alerts reached Alanya phones seconds before the 5 January 2022 M5.1 (AFAD 5.3) Mediterranean quake", "summary": "Android users near Alanya, Turkey, got Google's earthquake alert seconds before a M5.1 (AFAD 5.3) offshore quake on 5 January 2022.", "date_observed": "2022-01-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "At 06:21 local time on 5 January 2022 a magnitude 5.1 (USGS; AFAD 5.3) earthquake struck the Mediterranean about 57 km off Alanya, Antalya, Turkey. Depth: roughly 40 km. Yeni Şafak and DonanımHaber reported the same day on Google's Android Earthquake Alerts System, introduced in Turkey in June 2021. It sent an audible and visual alert for a magnitude 5.3 quake to Android users seconds before the shaking. Hürriyet put the lead at about 10 seconds, while NTV and İndigo Dergisi claimed about a minute. AFAD and the Alanya district governor reported no damage or injuries. On 3 February 2022 Google said the system had alerted users to two January quakes centered on Alanya and Balıkesir.", "category": "protective response", "unexpected": false, "purpose": "Built to detect earthquakes from Android phones' accelerometers and alert users in the expected shaking zone seconds before the slower destructive waves arrive. It covers quakes of magnitude 4.5 and above. This alert was that intended function.", "system": "Android Earthquake Alerts System (crowdsourced phone-accelerometer detection; alerts for magnitude 4.5 and above)", "model_version": null, "developer": "Google", "deployer": "Google", "location": "Alanya, Turkey", "domain": "Disaster risk reduction / earthquake early warning", "segment": "Consumer smartphones (Android)", "benefit_type": "Increased Product Safety", "taxonomy_note": "The alert arrived seconds before shaking, but no protective action by a recipient and no harm are documented.", "registry_ids": { "oecd_aim": "2022-01-18-136b" }, "ratings": [ { "group": "personal", "depth": 1, "reason": "An unstated number of Android users near Alanya received a few seconds' audible and visual notice of the quake. AFAD and the district governor said it caused no damage or injuries. No protective action by a recipient is documented. Hürriyet reported on 7 January 2022 that most users who posted about the alert said they panicked." } ], "ai_role": { "status": "assessed", "statement": "The alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake and estimate its size. Alerts were pushed to phones in the expected shaking zone ahead of the slower S-waves. Google's own descriptions of this process do not use the words AI or machine learning. The OECD Monitor classifies it as an AI system doing event detection and forecasting, and the 2025 Science paper calls it smartphone-based earthquake detection algorithms. The reported lead time varies by outlet (seconds, about 10 seconds, about one minute); Google promises seconds." }, "evidence": [ { "kind": "news report", "citation": "Yeni Şafak (2022). Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı. Yeni Şafak, 5 January 2022.", "title": "Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı", "url": "https://www.yenisafak.com/teknoloji/google-android-deprem-uyari-sistemi-nasil-acilir-deprem-uyari-sistemi-nedir-deprem-uyari-sistemi-hangi-telefonlarda-acilir-3728848", "date": "2022-01-05", "written_by": "independent", "quote": "Depremden saniyeler önce bazı Android kullanıcıları, Google Android’in deprem erken uyarı sisteminden depreme dair telefonlarına 5.3 büyüklüğünde bir depremin olacağına dair sesli ve görsel bir uyarı mesajı gönderildi." }, { "kind": "news report", "citation": "DonanımHaber (2022). Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi. DonanımHaber, 5 January 2022.", "title": "Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi", "url": "https://www.donanimhaber.com/google-erken-uyari-sistemi-alanya-depremini-onceden-bildi--143452", "date": "2022-01-05", "written_by": "independent", "quote": "Google deprem uyarı sistemi, erken saatlerde meydana gelen depremden saniyeler önce, o bölgede oturan kullanıcılara deprem uyarı sistemi aracılığıyla bir uyarı mesajı gönderdi." }, { "kind": "news report", "citation": "Öztürk T (2022). Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi? Hürriyet, 7 January 2022.", "title": "Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi?", "url": "https://www.hurriyet.com.tr/gundem/erken-uyari-sistemi-tartismasi-depremin-olacagini-10-saniye-onceden-bilmek-guvenli-mi-41976168", "date": "2022-01-07", "written_by": "independent", "quote": "Depremden yaklaşık 10 saniye önce, cep telefonlarına 'deprem olacak' bildirimi gitti." }, { "kind": "news report", "citation": "Hürriyet (2022). Google, Android Deprem Uyarı Sistemi’ni tanıttı. Hürriyet, 3 February 2022.", "title": "Google, Android Deprem Uyarı Sistemi’ni tanıttı", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "date": "2022-02-03", "written_by": "independent", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi" }, { "kind": "news report", "citation": "İndigo Dergisi (2022). Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi? İndigo Dergisi, 5 January 2022.", "title": "Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi?", "url": "https://indigodergisi.com/2022/01/google-deprem-uyari-sistemi-nasil-ailir/", "date": "2022-01-05", "written_by": "independent", "quote": "AFAD’dan yapılan açıklamada “Akdeniz’de Antalya’nın Alanya ilçesi açıklarında saat 06.21’de 5,3 büyüklüğünde bir deprem meydana geldi. Gelişmeleri takip ediyoruz” denildi." }, { "kind": "news report", "citation": "NTV (2022). Deprem uyarı sistemi nedir, nasıl çalışır? NTV, 18 January 2022.", "title": "Deprem uyarı sistemi nedir, nasıl çalışır?", "url": "https://www.ntv.com.tr/turkiye/deprem-uyari-sistemi-nedir-nasil-calisir,PtR3VF38DUOdDzApupgnQQ", "date": "2022-01-18", "written_by": "independent", "quote": "Geçtiğimiz haftalarda Google'ın sunduğu deprem uyarı sistemi, Alanya açıklarında meydana gelen 5.3'lük depremi 1 dakika öncesinden tahmin ederek kullanıcılarına sesli uyarı göndermişti." }, { "kind": "news report", "citation": "Yeni Akit (2022). Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır? Yeni Akit, 18 January 2022.", "title": "Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır?", "url": "https://www.yeniakit.com.tr/haber/android-deprem-uyari-sistemi-nedir-nasil-yapilir-nasil-kullanilir-1618101.html", "date": "2022-01-18", "written_by": "independent", "quote": "Android deprem uyarı sistemi sayesinde 5 Ocak 2022 sabahı Antalya’da meydana gelen depremin kısa süre öncesinde vatandaşlara uyarı yollandı." }, { "kind": "news report", "citation": "Hürriyet (2022). Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları. Hürriyet, 18 January 2022.", "title": "Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları", "url": "https://www.hurriyet.com.tr/galeri-deprem-uyari-sistemi-nasil-yapilir-nedir-android-erken-deprem-uyari-sistemi-kullanim-detaylari-41984292", "date": "2022-01-18", "written_by": "independent", "quote": "Olası bir depremi 10 saniye önceden haber verebilen deprem uyarı sistemi Alanya'da yaşanan depremi gerçekleşmeden 1 dakika kadar önce bölgede oturanlara Google tarafından bildirilmişti." }, { "kind": "dataset", "citation": "U.S. Geological Survey (2022). M 5.1 - 67 km SSW of Okurcalar, Turkey (event us7000g94d). USGS Earthquake Hazards Program.", "title": "M 5.1 - 67 km SSW of Okurcalar, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000g94d", "date": "2022-01-05", "written_by": "independent", "quote": "M 5.1 - 67 km SSW of Okurcalar, Turkey" }, { "kind": "news report", "citation": "Duckett C (2021). Android opens up earthquake alerts and end-to-end encrypted messages. ZDNet, 16 June 2021.", "title": "Android opens up earthquake alerts and end-to-end encrypted messages", "url": "https://www.zdnet.com/article/android-opens-up-earthquake-alerts-and-end-to-end-encrypted-messages/", "date": "2021-06-16", "written_by": "independent", "quote": "The system is already live in New Zealand and Greece, with Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan coming on board." }, { "kind": "press release", "citation": "Lee F (2021). 6 new features on Android this summer. Google, The Keyword, 15 June 2021.", "title": "6 new features on Android this summer", "url": "https://blog.google/products-and-platforms/platforms/android/new-features-summer-2021/", "date": "2021-06-15", "written_by": "makers", "quote": "Today, we're introducing the Android Earthquake Alerts System in Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan." }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "date": null, "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively." } ], "replication": { "produced_by": [ "A magnitude 5.1 (AFAD 5.3) quake above the 4.5 alert threshold, centered about 57 km offshore and roughly 40 km deep. So the push alert could outrun the slower S-waves to the coast.", "Enough Android phones near the epicenter with Earthquake Alerts on to trigger Google's server-side detection. Per Google's 3 February 2022 presentation it runs on Android 5.0 and later and users can switch it off.", "Crowdsourced phone detection was the only input: Google partners with the ShakeAlert seismic network only in California, Oregon and Washington" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. Hürriyet reported on 7 January 2022 that most users who posted about the Alanya alert said they panicked", "The phone must have location and the Earthquake Alerts setting enabled" ], "echoes": [] }, "paired_incident": null, "headline": "Google alerts Alanya phones seconds before a M5.1 quake", "metric": "Hürriyet put the lead at about 10 seconds", "comparator": null, "later": { "status": "persisted", "date": "2022-02-03", "note": "At a 3 February 2022 press event Google said the Android system had alerted users to the two January quakes centered on Alanya and Balıkesir.", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi hakkında detaylı bilgi vermek ve depremde saniyelerin önemini tekrar vurgulamak üzere Google bir basın toplantısı düzenledi.", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-01-18-136b", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: nine independent news reports, one independent dataset, the makers' peer-reviewed study and three press releases from the makers. 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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. 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"At a 3 February 2022 press event Google said the Android system had alerted users to the two January quakes centered on Alanya and Balıkesir.", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi hakkında detaylı bilgi vermek ve depremde saniyelerin önemini tekrar vurgulamak üzere Google bir basın toplantısı düzenledi.", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": 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rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At 06:21 local time on 5 January 2022 a magnitude 5.1 (USGS; AFAD 5.3) earthquake struck the Mediterranean about 57 km off Alanya, Antalya, Turkey. Depth: roughly 40 km. Yeni Şafak and DonanımHaber reported the same day on Google's Android Earthquake Alerts System, introduced in Turkey in June 2021. It sent an audible and visual alert for a magnitude 5.3 quake to Android users seconds before the shaking. Hürriyet put the lead at about 10 seconds, while NTV and İndigo Dergisi claimed about a minute. AFAD and the Alanya district governor reported no damage or injuries. On 3 February 2022 Google said the system had alerted users to two January quakes centered on Alanya and Balıkesir.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "An unstated number of Android users near Alanya received a few seconds' audible and visual notice of the quake. AFAD and the district governor said it caused no damage or injuries. No protective action by a recipient is documented. Hürriyet reported on 7 January 2022 that most users who posted about the alert said they panicked." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to detect earthquakes from Android phones' accelerometers and alert users in the expected shaking zone seconds before the slower destructive waves arrive. It covers quakes of magnitude 4.5 and above. This alert was that intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Alanya, Turkey", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to detect earthquakes from Android phones' accelerometers and alert users in the expected shaking zone seconds before the slower destructive waves arrive. It covers quakes of magnitude 4.5 and above. This alert was that intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-01-05", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-01-18-136b", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A magnitude 5.1 (AFAD 5.3) quake above the 4.5 alert threshold, centered about 57 km offshore and roughly 40 km deep. So the push alert could outrun the slower S-waves to the coast.", "Enough Android phones near the epicenter with Earthquake Alerts on to trigger Google's server-side detection. Per Google's 3 February 2022 presentation it runs on Android 5.0 and later and users can switch it off.", "Crowdsourced phone detection was the only input: Google partners with the ShakeAlert seismic network only in California, Oregon and Washington" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. 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Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı. Yeni Şafak, 5 January 2022.", "title": "Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı", "url": "https://www.yenisafak.com/teknoloji/google-android-deprem-uyari-sistemi-nasil-acilir-deprem-uyari-sistemi-nedir-deprem-uyari-sistemi-hangi-telefonlarda-acilir-3728848", "date": "2022-01-05", "written_by": "independent", "quote": "Depremden saniyeler önce bazı Android kullanıcıları, Google Android’in deprem erken uyarı sisteminden depreme dair telefonlarına 5.3 büyüklüğünde bir depremin olacağına dair sesli ve görsel bir uyarı mesajı gönderildi." }, { "kind": "news report", "citation": "DonanımHaber (2022). Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi. DonanımHaber, 5 January 2022.", "title": "Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi", "url": "https://www.donanimhaber.com/google-erken-uyari-sistemi-alanya-depremini-onceden-bildi--143452", "date": "2022-01-05", "written_by": "independent", "quote": "Google deprem uyarı sistemi, erken saatlerde meydana gelen depremden saniyeler önce, o bölgede oturan kullanıcılara deprem uyarı sistemi aracılığıyla bir uyarı mesajı gönderdi." }, { "kind": "news report", "citation": "Öztürk T (2022). Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi? Hürriyet, 7 January 2022.", "title": "Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi?", "url": "https://www.hurriyet.com.tr/gundem/erken-uyari-sistemi-tartismasi-depremin-olacagini-10-saniye-onceden-bilmek-guvenli-mi-41976168", "date": "2022-01-07", "written_by": "independent", "quote": "Depremden yaklaşık 10 saniye önce, cep telefonlarına 'deprem olacak' bildirimi gitti." }, { "kind": "news report", "citation": "Hürriyet (2022). Google, Android Deprem Uyarı Sistemi’ni tanıttı. Hürriyet, 3 February 2022.", "title": "Google, Android Deprem Uyarı Sistemi’ni tanıttı", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "date": "2022-02-03", "written_by": "independent", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi" }, { "kind": "news report", "citation": "İndigo Dergisi (2022). Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi? İndigo Dergisi, 5 January 2022.", "title": "Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi?", "url": "https://indigodergisi.com/2022/01/google-deprem-uyari-sistemi-nasil-ailir/", "date": "2022-01-05", "written_by": "independent", "quote": "AFAD’dan yapılan açıklamada “Akdeniz’de Antalya’nın Alanya ilçesi açıklarında saat 06.21’de 5,3 büyüklüğünde bir deprem meydana geldi. Gelişmeleri takip ediyoruz” denildi." }, { "kind": "news report", "citation": "NTV (2022). Deprem uyarı sistemi nedir, nasıl çalışır? NTV, 18 January 2022.", "title": "Deprem uyarı sistemi nedir, nasıl çalışır?", "url": "https://www.ntv.com.tr/turkiye/deprem-uyari-sistemi-nedir-nasil-calisir,PtR3VF38DUOdDzApupgnQQ", "date": "2022-01-18", "written_by": "independent", "quote": "Geçtiğimiz haftalarda Google'ın sunduğu deprem uyarı sistemi, Alanya açıklarında meydana gelen 5.3'lük depremi 1 dakika öncesinden tahmin ederek kullanıcılarına sesli uyarı göndermişti." }, { "kind": "news report", "citation": "Yeni Akit (2022). Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır? Yeni Akit, 18 January 2022.", "title": "Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır?", "url": "https://www.yeniakit.com.tr/haber/android-deprem-uyari-sistemi-nedir-nasil-yapilir-nasil-kullanilir-1618101.html", "date": "2022-01-18", "written_by": "independent", "quote": "Android deprem uyarı sistemi sayesinde 5 Ocak 2022 sabahı Antalya’da meydana gelen depremin kısa süre öncesinde vatandaşlara uyarı yollandı." }, { "kind": "news report", "citation": "Hürriyet (2022). Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları. Hürriyet, 18 January 2022.", "title": "Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları", "url": "https://www.hurriyet.com.tr/galeri-deprem-uyari-sistemi-nasil-yapilir-nedir-android-erken-deprem-uyari-sistemi-kullanim-detaylari-41984292", "date": "2022-01-18", "written_by": "independent", "quote": "Olası bir depremi 10 saniye önceden haber verebilen deprem uyarı sistemi Alanya'da yaşanan depremi gerçekleşmeden 1 dakika kadar önce bölgede oturanlara Google tarafından bildirilmişti." }, { "kind": "dataset", "citation": "U.S. Geological Survey (2022). M 5.1 - 67 km SSW of Okurcalar, Turkey (event us7000g94d). USGS Earthquake Hazards Program.", "title": "M 5.1 - 67 km SSW of Okurcalar, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000g94d", "date": "2022-01-05", "written_by": "independent", "quote": "M 5.1 - 67 km SSW of Okurcalar, Turkey" }, { "kind": "news report", "citation": "Duckett C (2021). Android opens up earthquake alerts and end-to-end encrypted messages. ZDNet, 16 June 2021.", "title": "Android opens up earthquake alerts and end-to-end encrypted messages", "url": "https://www.zdnet.com/article/android-opens-up-earthquake-alerts-and-end-to-end-encrypted-messages/", "date": "2021-06-16", "written_by": "independent", "quote": "The system is already live in New Zealand and Greece, with Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan coming on board." }, { "kind": "press release", "citation": "Lee F (2021). 6 new features on Android this summer. Google, The Keyword, 15 June 2021.", "title": "6 new features on Android this summer", "url": "https://blog.google/products-and-platforms/platforms/android/new-features-summer-2021/", "date": "2021-06-15", "written_by": "makers", "quote": "Today, we're introducing the Android Earthquake Alerts System in Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan." }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. 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Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı. Yeni Şafak, 5 January 2022.", "title": "Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı", "url": "https://www.yenisafak.com/teknoloji/google-android-deprem-uyari-sistemi-nasil-acilir-deprem-uyari-sistemi-nedir-deprem-uyari-sistemi-hangi-telefonlarda-acilir-3728848", "date": "2022-01-05", "written_by": "independent", "quote": "Depremden saniyeler önce bazı Android kullanıcıları, Google Android’in deprem erken uyarı sisteminden depreme dair telefonlarına 5.3 büyüklüğünde bir depremin olacağına dair sesli ve görsel bir uyarı mesajı gönderildi." }, { "kind": "news report", "citation": "DonanımHaber (2022). Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi. DonanımHaber, 5 January 2022.", "title": "Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi", "url": "https://www.donanimhaber.com/google-erken-uyari-sistemi-alanya-depremini-onceden-bildi--143452", "date": "2022-01-05", "written_by": "independent", "quote": "Google deprem uyarı sistemi, erken saatlerde meydana gelen depremden saniyeler önce, o bölgede oturan kullanıcılara deprem uyarı sistemi aracılığıyla bir uyarı mesajı gönderdi." }, { "kind": "news report", "citation": "Öztürk T (2022). Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi? Hürriyet, 7 January 2022.", "title": "Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi?", "url": "https://www.hurriyet.com.tr/gundem/erken-uyari-sistemi-tartismasi-depremin-olacagini-10-saniye-onceden-bilmek-guvenli-mi-41976168", "date": "2022-01-07", "written_by": "independent", "quote": "Depremden yaklaşık 10 saniye önce, cep telefonlarına 'deprem olacak' bildirimi gitti." }, { "kind": "news report", "citation": "Hürriyet (2022). Google, Android Deprem Uyarı Sistemi’ni tanıttı. Hürriyet, 3 February 2022.", "title": "Google, Android Deprem Uyarı Sistemi’ni tanıttı", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "date": "2022-02-03", "written_by": "independent", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi" }, { "kind": "news report", "citation": "İndigo Dergisi (2022). Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi? İndigo Dergisi, 5 January 2022.", "title": "Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi?", "url": "https://indigodergisi.com/2022/01/google-deprem-uyari-sistemi-nasil-ailir/", "date": "2022-01-05", "written_by": "independent", "quote": "AFAD’dan yapılan açıklamada “Akdeniz’de Antalya’nın Alanya ilçesi açıklarında saat 06.21’de 5,3 büyüklüğünde bir deprem meydana geldi. Gelişmeleri takip ediyoruz” denildi." }, { "kind": "news report", "citation": "NTV (2022). Deprem uyarı sistemi nedir, nasıl çalışır? NTV, 18 January 2022.", "title": "Deprem uyarı sistemi nedir, nasıl çalışır?", "url": "https://www.ntv.com.tr/turkiye/deprem-uyari-sistemi-nedir-nasil-calisir,PtR3VF38DUOdDzApupgnQQ", "date": "2022-01-18", "written_by": "independent", "quote": "Geçtiğimiz haftalarda Google'ın sunduğu deprem uyarı sistemi, Alanya açıklarında meydana gelen 5.3'lük depremi 1 dakika öncesinden tahmin ederek kullanıcılarına sesli uyarı göndermişti." }, { "kind": "news report", "citation": "Yeni Akit (2022). Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır? Yeni Akit, 18 January 2022.", "title": "Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır?", "url": "https://www.yeniakit.com.tr/haber/android-deprem-uyari-sistemi-nedir-nasil-yapilir-nasil-kullanilir-1618101.html", "date": "2022-01-18", "written_by": "independent", "quote": "Android deprem uyarı sistemi sayesinde 5 Ocak 2022 sabahı Antalya’da meydana gelen depremin kısa süre öncesinde vatandaşlara uyarı yollandı." }, { "kind": "news report", "citation": "Hürriyet (2022). Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları. Hürriyet, 18 January 2022.", "title": "Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları", "url": "https://www.hurriyet.com.tr/galeri-deprem-uyari-sistemi-nasil-yapilir-nedir-android-erken-deprem-uyari-sistemi-kullanim-detaylari-41984292", "date": "2022-01-18", "written_by": "independent", "quote": "Olası bir depremi 10 saniye önceden haber verebilen deprem uyarı sistemi Alanya'da yaşanan depremi gerçekleşmeden 1 dakika kadar önce bölgede oturanlara Google tarafından bildirilmişti." }, { "kind": "dataset", "citation": "U.S. Geological Survey (2022). M 5.1 - 67 km SSW of Okurcalar, Turkey (event us7000g94d). USGS Earthquake Hazards Program.", "title": "M 5.1 - 67 km SSW of Okurcalar, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000g94d", "date": "2022-01-05", "written_by": "independent", "quote": "M 5.1 - 67 km SSW of Okurcalar, Turkey" }, { "kind": "news report", "citation": "Duckett C (2021). Android opens up earthquake alerts and end-to-end encrypted messages. ZDNet, 16 June 2021.", "title": "Android opens up earthquake alerts and end-to-end encrypted messages", "url": "https://www.zdnet.com/article/android-opens-up-earthquake-alerts-and-end-to-end-encrypted-messages/", "date": "2021-06-16", "written_by": "independent", "quote": "The system is already live in New Zealand and Greece, with Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan coming on board." }, { "kind": "press release", "citation": "Lee F (2021). 6 new features on Android this summer. Google, The Keyword, 15 June 2021.", "title": "6 new features on Android this summer", "url": "https://blog.google/products-and-platforms/platforms/android/new-features-summer-2021/", "date": "2021-06-15", "written_by": "makers", "quote": "Today, we're introducing the Android Earthquake Alerts System in Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan." }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Android users near Alanya, Turkey, got Google's earthquake alert seconds before a M5.1 (AFAD 5.3) offshore quake on 5 January 2022.", "account": "At 06:21 local time on 5 January 2022 a magnitude 5.1 (USGS; AFAD 5.3) earthquake struck the Mediterranean about 57 km off Alanya, Antalya, Turkey. Depth: roughly 40 km. Yeni Şafak and DonanımHaber reported the same day on Google's Android Earthquake Alerts System, introduced in Turkey in June 2021. It sent an audible and visual alert for a magnitude 5.3 quake to Android users seconds before the shaking. Hürriyet put the lead at about 10 seconds, while NTV and İndigo Dergisi claimed about a minute. AFAD and the Alanya district governor reported no damage or injuries. On 3 February 2022 Google said the system had alerted users to two January quakes centered on Alanya and Balıkesir." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Android Earthquake Alerts System (crowdsourced phone-accelerometer detection; alerts for magnitude 4.5 and above)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Google's Android Earthquake Alerts reached Alanya phones seconds before the 5 January 2022 M5.1 (AFAD 5.3) Mediterranean quake", "headline": "Google alerts Alanya phones seconds before a M5.1 quake" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "65b16e850751b1199c57592eb6bfa2df4ce99dc8579627ff3f0d2be754b77e98" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At 06:21 local time on 5 January 2022 a magnitude 5.1 (USGS; AFAD 5.3) earthquake struck the Mediterranean about 57 km off Alanya, Antalya, Turkey. Depth: roughly 40 km. Yeni Şafak and DonanımHaber reported the same day on Google's Android Earthquake Alerts System, introduced in Turkey in June 2021. It sent an audible and visual alert for a magnitude 5.3 quake to Android users seconds before the shaking. Hürriyet put the lead at about 10 seconds, while NTV and İndigo Dergisi claimed about a minute. AFAD and the Alanya district governor reported no damage or injuries. On 3 February 2022 Google said the system had alerted users to two January quakes centered on Alanya and Balıkesir.", "replication": { "produced_by": [ "A magnitude 5.1 (AFAD 5.3) quake above the 4.5 alert threshold, centered about 57 km offshore and roughly 40 km deep. So the push alert could outrun the slower S-waves to the coast.", "Enough Android phones near the epicenter with Earthquake Alerts on to trigger Google's server-side detection. Per Google's 3 February 2022 presentation it runs on Android 5.0 and later and users can switch it off.", "Crowdsourced phone detection was the only input: Google partners with the ShakeAlert seismic network only in California, Oregon and Washington" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. Hürriyet reported on 7 January 2022 that most users who posted about the Alanya alert said they panicked", "The phone must have location and the Earthquake Alerts setting enabled" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0074", "case_url": "records/AIBD-0074.html", "reason": "The developer is headquartered in France; the case names Austria.", "details": [ { "label": "Developer headquarters", "value": "France" }, { "label": "Case jurisdictions", "value": "Austria" }, { "label": "Case location", "value": "Vienna, Austria" } ], "filters": { "origin": "France", "destination": [ "Austria" ] }, "record": { "id": "AIBD-0074", "title": "Machine-learning color reconstruction of Klimt's destroyed Faculty Paintings, made with the Belvedere and later exhibited by it", "summary": "ML color reconstruction of Klimt's destroyed Faculty Paintings launched 7 October 2021 with the Belvedere, shown in its 2025 exhibition", "date_observed": "2021-10-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Gustav Klimt's Faculty Paintings (Philosophy, Medicine and Jurisprudence) were made for the ceiling of the University of Vienna's Great Hall. Fire destroyed them in 1945; only black-and-white photographs survive. The online exhibition Klimt vs. Klimt – The Man of Contradictions was unveiled by Google Arts & Culture with the Belvedere on 7 October 2021. For it, Google Arts & Culture Lab resident Emil Wallner trained a colorization model on 91,749 artworks and then on Klimt's color paintings. Curator Franz Smola's compilation of contemporary descriptions of the colors guided him. The Belvedere describes the result as an attempt to reconstruct the original colors in which artificial intelligence played an essential role. It showed the color reconstructions at original size in its exhibition Gustav Klimt – Pigment & Pixel (20 February to 7 September 2025).", "category": "benefit created", "unexpected": false, "purpose": "Colorize black-and-white photographs of destroyed paintings in the artist's style, guided by archival color descriptions", "system": "The Klimt Color Enigma colourisation model (Google Arts & Culture Lab)", "model_version": null, "developer": "Google (Emil Wallner)", "deployer": "Belvedere, Vienna, with Google Arts & Culture", "location": "Vienna, Austria", "domain": "Art", "segment": "Museums and art history", "benefit_type": "Increased Creativity", "taxonomy_note": "The colorization is a reconstruction hypothesis for destroyed paintings rather than a new creative work.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 1, "reason": "Small: the Belvedere adopted and exhibited the reconstructions in 2025, per its exhibition page" }, { "group": "societal", "depth": 1, "reason": "Small: the public can view a color hypothesis of lost works; Smola himself called the result surprising where no color knowledge existed (Jing Daily)" } ], "ai_role": { "status": "assessed", "statement": "The model proposed colors for the monochrome photographs. Known colors from Smola's archival research were added directly, so the output mixes documented evidence with learned statistical guesses. The Belvedere calls it an attempt at reconstruction, and no source claims the colors are verified." }, "evidence": [ { "kind": "press release", "citation": "Belvedere (2025). Gustav Klimt – Pigment & Pixel, exhibition page, 20 February – 7 September 2025. belvedere.at.", "title": "Gustav Klimt – Pigment & Pixel", "url": "https://www.belvedere.at/en/gustav-klimt-pigment-pixel", "date": "2025-02-20", "written_by": "deployer", "quote": "Now for the first time, the Ceiling Paintings can be viewed in color in their original size and arrangement on the ceiling." }, { "kind": "news report", "citation": "Chen, M. (2021). Google Arts & Culture And Belvedere Museum Trained An AI To Color Like Klimt. Jing Daily Culture, 7 October 2021.", "title": "Google Arts & Culture And Belvedere Museum Trained An AI To Color Like Klimt", "url": "https://jingdailyculture.com/google-arts-and-culture-klimt-ai/", "date": "2021-10-07", "written_by": "independent", "quote": "on October 7, Google Arts & Culture unveiled Klimt vs. Klimt – The Man of Contradictions" }, { "kind": "news report", "citation": "I Programmer (2022). Google AI Recreates Lost Klimt Artworks. i-programmer.info.", "title": "Google AI Recreates Lost Klimt Artworks", "url": "https://www.i-programmer.info/news/105-artificial-intelligence/14928-google-ai-recreates-lost-klimt-artworks-.html", "date": "2022-01-03", "written_by": "independent", "quote": "was trained on 91749 artworks from Google Arts & Culture to enable it to learn object boundaries, textures, and frequent compositions in artworks" }, { "kind": "press release", "citation": "Google Arts & Culture (2021). The Klimt Color Enigma. artsandculture.google.com.", "title": "The Klimt Color Enigma", "url": "https://artsandculture.google.com/story/01001/SQWxuZfE5ki3mQ", "date": null, "written_by": "makers", "quote": "developed an algorithm to use Dr. Smola's research to restore the Faculty Paintings" } ], "replication": { "produced_by": [ "Surviving black-and-white photographs of the destroyed paintings", "A colorization model pre-trained on 91,749 artworks and fine-tuned on Klimt's color paintings", "Curator-compiled written color testimony injected as fixed colors" ], "must_hold": [ "Monochrome photographs of adequate quality exist", "Written color descriptions exist for key areas", "An institutional partner validates and displays the result" ], "echoes": [ { "date": "2025-02-20", "setting": "Belvedere exhibition Gustav Klimt – Pigment & Pixel shows the color reconstructions at original size and ceiling arrangement", "model_version": null, "outcome": "recurred", "source": "https://www.belvedere.at/en/gustav-klimt-pigment-pixel" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "554", "title": "Google Results for Johannes Vermeer Featured AI Version of His Artwork as Top Result", "url": "https://incidentdatabase.ai/cite/554", "severity": null, "group": "societal", "basis": "Same task, AI reimagining a canonical painting shown to the public: Google surfaced an AI-generated version of Vermeer's “Girl With a Pearl Earring” as its top result, misleading users, the public the Klimt reconstruction also reaches." }, "headline": "Google Arts & Culture Lab recolours Klimt's destroyed paintings", "metric": null, "comparator": { "against": "the surviving black-and-white photographs of the destroyed paintings", "before": "have only survived in grainy black-and-white photographs, devoid of the sumptuous, aurous pigment on which the painter's name was made", "after": "a digital reconstruction of his Faculty works — in full and glorious color", "basis": "assumed", "quote": "For decades, Klimt's so-called Faculty Paintings, \"Philosophy,\" \"Jurisprudence,\" and \"Medicine,\" have only survived in grainy black-and-white photographs, devoid of the sumptuous, aurous pigment on which the painter's name was made. But no more: on October 7, Google Arts & Culture unveiled Klimt vs. Klimt – The Man of Contradictions, an expansive virtual hub that chronicles Klimt's art and lifetime, and presents a digital reconstruction of his Faculty works — in full and glorious color.", "url": "https://jingdailyculture.com/google-arts-and-culture-klimt-ai/" }, "later": null, "value": null, "developer_country": "France", "deployer_country": "Austria", "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 554 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/554", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Replaced the I Programmer quote with the verbatim clause, since the earlier version joined “The model” to the training sentence while silently dropping the DeOldify clause, and used the Jing Daily headline in the evidence title and citation.", "source": "https://www.i-programmer.info/news/105-artificial-intelligence/14928-google-ai-recreates-lost-klimt-artworks-.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://jingdailyculture.com/google-arts-and-culture-klimt-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Creativity 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://artsandculture.google.com/story/01001/SQWxuZfE5ki3mQ; https://en.wikipedia.org/wiki/Belvedere,_Vienna", "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" }, { "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: two independent news reports, the makers' press release 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-0074", "catalog_version": "1.0.0", "source_fingerprint": "96c3a11a391bf3fe05578856cb1ba97ee51dd1c061005d785de6334b66203dc8", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0074-source-0001", "AIBD-0074-source-0002", "AIBD-0074-source-0003", "AIBD-0074-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": "Google (Emil Wallner)", "deployer": "Belvedere, Vienna, with Google Arts & Culture" } }, "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 proposed colors for the monochrome photographs. Known colors from Smola's archival research were added directly, so the output mixes documented evidence with learned statistical guesses. The Belvedere calls it an attempt at reconstruction, and no source claims the colors are verified." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The model proposed colors for the monochrome photographs. Known colors from Smola's archival research were added directly, so the output mixes documented evidence with learned statistical guesses. The Belvedere calls it an attempt at reconstruction, and no source claims the colors are verified." } } }, "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": "Small: the Belvedere adopted and exhibited the reconstructions in 2025, per its exhibition page" }, { "group": "societal", "depth": 1, "reason": "Small: the public can view a color hypothesis of lost works; Smola himself called the result surprising where no color knowledge existed (Jing Daily)" } ] } }, "provenance": { "kind": "legacy_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 the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Small: the Belvedere adopted and exhibited the reconstructions in 2025, per its exhibition page" }, { "group": "societal", "depth": 1, "reason": "Small: the public can view a color hypothesis of lost works; Smola himself called the result surprising where no color knowledge existed (Jing Daily)" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Small: the Belvedere adopted and exhibited the reconstructions in 2025, per its exhibition page" }, { "group": "societal", "depth": 1, "reason": "Small: the public can view a color hypothesis of lost works; Smola himself called the result surprising where no color knowledge existed (Jing Daily)" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not 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task, AI reimagining a canonical painting shown to the public: Google surfaced an AI-generated version of Vermeer's “Girl With a Pearl Earring” as its top result, misleading users, the public the Klimt reconstruction also reaches.", "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", 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Original description of satellite analysis, AI, and notifications." } ], "excluded_republications": [ { "url": "https://eurasianet.org/turkmenistan-tackling-methane-leak-problem", "title": "Turkmenistan tackling methane leak problem", "published_on": "2026-09-03", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0345-source-0001" }, { "url": "https://eurasianet.org/turkmenistan-tackling-methane-leak-problem", "title": "Turkmenistan tackling methane leak problem (repairs)", "published_on": "2026-09-03", "relationship": "Repeated quotation or alternate version of a retained original work", "previous_source_ref": "AIBD-0345-source-0002" }, { "url": "https://eurasianet.org/turkmenistan-tackling-methane-leak-problem", "title": "Turkmenistan tackling methane leak problem (cases addressed)", "published_on": "2026-09-03", "relationship": "Repeated quotation or alternate version of a retained original work", "previous_source_ref": "AIBD-0345-source-0003" }, { "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "published_on": "2026-05-05", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0345-source-0004" }, { "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "title": "Canada plugs 'major source' of methane emissions from oil and gas field leak", "published_on": "2026-09-04", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0345-source-0005" }, { "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "title": "Methane Alert and Response System (MARS)", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0345-source-0006" } ], "independent_evaluations_of_reported_results": 0, "lead_source_ref": "AIBD-0345-source-0001", "retained_source_order": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "ordering_basis": "The source that directly supports the central case leads. Additional sources contribute the specific evidence or limitations described in claim_lineage.", "claim_lineage": [ { "claim": "Eight named mitigation cases and dates", "source_chain": [ { "url": "https://www.unep.org/en/what-we-do/energy/methane/mars-case-studies", "locator": "Central Asia, Turkmenistan entries TKM_S_406, 451, 452, 118, 053, 071, 080, and 081", "role": "Original UNEP mitigation index", "access_status": "Full relevant text reviewed" }, { "url": "https://wedocs.unep.org/items/5c86c2bd-db0e-49ab-98ed-f9d4730b1af4", "locator": "TKM_S_406, Lebap Province, March 2026", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/ab7219e7-8192-4ad3-91f5-1c93b440ec48", "locator": "TKM_S_451, Dauletabad, February 2026", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/24553364-60bb-4cd9-841f-3e8ca35dfc3a", "locator": "TKM_S_452, Dauletabad, February 2026", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/fa3e52ea-97bd-4009-b5f2-7dafb2a5a4e9", "locator": "TKM_S_118, Balguyy, August 2025", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/1bc9fee8-eb37-4f2a-b690-0f7ee470c8c5", "locator": "TKM_S_053, Gugurtli field, July 2025", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/490eba84-19e5-41eb-93fe-e885f74cb68e", "locator": "TKM_S_071, Babarap, June 2025", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/12065ac0-2bee-4097-a140-7c94924603af", "locator": "TKM_S_080, Yelguyi, November 2024", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/ba302a6c-8b00-404e-9e5a-ec6912f65250", "locator": "TKM_S_081, Yelguyi, November 2024", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" } ], "limitations": [ "The index supports named cases and dates; underlying sheets were inaccessible, so repair procedures and emissions quantities were not independently checked" ], "source_ref": "AIBD-0345-source-0001" }, { "claim": "192 alerts and roughly 20% response rate", "source_chain": [ { "url": "https://www.theguardian.com/environment/2026/aug/31/turkmenistan-methane-leaks-un-alerts-mars", "locator": "Contemporaneous MARS totals and direct interview", "role": "Original reporting", "access_status": "Full relevant text reviewed", "published_on": "2026-08-31" }, { "url": "https://methanedata.unep.org/", "locator": "August 2026 MARS snapshot underlying the reported totals", "role": "UNEP original data platform", "access_status": "Exact archived August snapshot not retrieved" } ], "limitations": [ "Historical totals could not be recomputed from the underlying snapshot" ], "source_ref": "AIBD-0345-source-0002" }, { "claim": "AI contribution to MARS", "source_chain": [ { "url": "https://www.unep.org/en/what-we-do/energy/methane/methane-alert-and-response-system-mars", "locator": "What is MARS?", "role": "Original system description", "access_status": "Full relevant text reviewed" } ], "limitations": [ "System-level AI contribution is not an isolated per-repair causal estimate" ], "source_ref": "AIBD-0345-source-0003" } ], "limitations": [ "Eight linked case sheets and the historical snapshot remain uninspected" ], "chronology_source": { "url": "https://www.theguardian.com/environment/2026/aug/31/turkmenistan-methane-leaks-un-alerts-mars", "locator": "Original aggregate report predating Eurasianet and The Chemical Engineer", "role": "First verified publication of the aggregate case", "access_status": "Full relevant text reviewed", "published_on": "2026-08-31", "date": "2026-08-31", "title": "‘Breakthrough’ as Turkmenistan starts fixing ‘mindboggling’ methane mega-leaks" } } }, "provenance": { "kind": "source_review", "basis": "Replaced derivative summaries with UNEP’s original case index and the original Guardian reporting. Corrected the assertion that site-level documents do not exist.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Eight linked case sheets and the historical website snapshot remain uninspected.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "UNEP lists eight mitigation cases, with site identifiers and dates from November 2024 to March 2026. The contribution of AI is described at system level, rather than isolated for each repair.", "account": "UNEP’s MARS mitigation index lists eight Turkmenistan cases: two in Yelguyi in November 2024, Babarap in June 2025, Gugurtli field in July 2025, Balguyy in August 2025, two in Dauletabad in February 2026, and Lebap Province in March 2026. Each entry has a source identifier and a link to an individual case document. UNEP describes these as mitigations that its notification and monitoring system helped prompt and verify. UNEP states that MARS combines satellite observations, scientific review, and AI to identify emissions for government and company action. The available aggregate evidence does not isolate AI’s contribution from analysts, notifications, and physical repairs or quantify their combined benefit." } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UNEP records eight methane mitigations in Turkmenistan after MARS notifications", "headline": "UNEP records eight methane mitigations in Turkmenistan" } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "title", "headline" ], "source_refs": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "99d559a555e9dd72aff880cf95666fa1cd18d56f5ab7dc5148de450fe943f190" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version after the October 24, 2026 primary-source review.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNEP’s MARS mitigation index lists eight Turkmenistan cases: two in Yelguyi in November 2024, Babarap in June 2025, Gugurtli field in July 2025, Balguyy in August 2025, two in Dauletabad in February 2026, and Lebap Province in March 2026. Each entry has a source identifier and a link to an individual case document. UNEP describes these as mitigations that its notification and monitoring system helped prompt and verify. UNEP states that MARS combines satellite observations, scientific review, and AI to identify emissions for government and company action. The available aggregate evidence does not isolate AI’s contribution from analysts, notifications, and physical repairs or quantify their combined benefit.", "replication": { "produced_by": [ "Satellite detection and analysis, notification to government or operators, and physical mitigation that UNEP reports as verified" ], "must_hold": [ "Operators can identify and address the notified emissions source", "Continued monitoring distinguishes durable mitigation from temporary non-detection", "Site-level evidence is needed to establish each repair and its effects" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: a single documented MARS-enabled pipeline mitigation in a neighboring country (UNEP case document)", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" } ] } } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 4, "record_mapped": 25, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0330", "case_url": "records/AIBD-0330.html", "reason": "The developer is headquartered in United States; the case names Taiwan.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Taiwan" }, { "label": "Case location", "value": "Taipei, Taiwan (Computex 2026, TaiNEX 2)" } ], "filters": { "origin": "United States", "destination": [ "Taiwan" ] }, "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-0348", "case_url": "records/AIBD-0348.html", "reason": "The developer is headquartered in United States; the case names Brazil, Peru, Guyana, Venezuela.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Brazil, Peru, Guyana, Venezuela" }, { "label": "Case location", "value": "Amazon basin (Brazil, Peru, Guyana, Venezuela and other basin countries)" } ], "filters": { "origin": "United States", "destination": [ "Brazil", "Peru", "Guyana", "Venezuela" ] }, "record": { "id": "AIBD-0348", "title": "Amazon Mining Watch's convolutional-network detector maps gold-mining scars basin-wide; 496,000 ha of protected and Indigenous lands affected 2018–2025, with no documented enforcement action", "summary": "AI mining detector (Earth Genome, Amazon Conservation, Pulitzer Center) maps mining scars basin-wide; 496,000 ha in protected areas 2018–2025 reported Apr 2026; no attributable enforcement event.", "date_observed": "2026-04-24", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Amazon Mining Watch is a public platform built by the Pulitzer Center's Rainforest Investigations Network, Amazon Conservation Association and Earth Genome. In its own words, it leverages artificial intelligence to map the impact of gold mining. Its code repository describes the detector as an ensemble of convolutional neural networks (CNNs) trained on hand-labeled Sentinel-2 patches. The detector is run over the 8.2 million km2 Amazon basin, with detections recomputed yearly for 2018–2025 and quarterly from 2025. The repository reports specificity 0.976 and sensitivity 0.991 on a Venezuelan geographic holdout for the cumulative 2024 detections. Mongabay reported on 24 April 2026, on the platform's quarterly report. That report found about 6,000 hectares of mining deforestation in protected areas and Indigenous territories in the last three months of 2025. It found 496,000 hectares affected between 2018 and 2025. The enforcement operations on record are documented by the same tool maker without any AI role. MAAP #104 (4 August 2019) attributes a 92% fall in gold-mining deforestation at La Pampa to Peru's Operation Mercury of February 2019. That analysis used satellite imagery (ESA, Maxar, Sentinel-1) with no mention of machine learning. No cited source documents an enforcement action taken on an Amazon Mining Watch detection.", "category": "general benefit", "unexpected": false, "purpose": "Automated detection and mapping of artisanal gold-mining scars in satellite imagery. The users are journalists, researchers, civil society and policymakers. The mapping is the intended use.", "system": "Amazon Mining Watch gold-mine detector (ensemble of convolutional neural networks on Sentinel-2 imagery)", "model_version": "48px_v4.10b-18d-20g-21a-22bc-ensemble (July 2026 production model, per the repository README)", "developer": "Earth Genome (model), with Amazon Conservation Association and the Pulitzer Center's Rainforest Investigations Network", "deployer": "Amazon Mining Watch partnership (public web platform)", "location": "Amazon basin (Brazil, Peru, Guyana, Venezuela and other basin countries)", "domain": "Environmental monitoring: illegal gold mining in tropical forest", "segment": "Non-profit remote-sensing platform for journalism and conservation", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary documented." }, { "group": "organizational", "depth": null, "reason": "Journalists and non-governmental organizations (NGOs) use the maps, but no organization's decision is documented as changed by a detection." }, { "group": "societal", "depth": null, "reason": "Basin-wide statistics exist, but no enforcement or policy outcome is attributed to the tool in a cited source." } ], "ai_role": { "status": "assessed", "statement": "The AI system produces the maps and area statistics themselves. The makers' repository describes an ensemble of convolutional neural networks on Sentinel-2 patches, with reported holdout specificity 0.976 and sensitivity 0.991. Mongabay independently describes the platform as training AI algorithms to recognize mining scars. No cited source links a detection to an enforcement action. Mongabay remarks that the platform is useful for cross-border work, citing 2025 Brazil-Colombia operations on the Pure River. It does not state that those operations used its detections. MAAP's Operation Mercury analysis, by the same maker, used satellite imagery without machine learning." }, "evidence": [ { "kind": "news report", "citation": "Malleret, C. (2026). AI tool tracks spread of illegal gold mining in Amazon protected areas. Mongabay, 24 April 2026.", "title": "AI tool tracks spread of illegal gold mining in Amazon protected areas", "url": "https://news.mongabay.com/2026/04/ai-tool-tracks-spread-of-illegal-gold-mining-in-amazon-protected-areas/", "date": "2026-04-24", "written_by": "independent", "quote": "496,000 hectares (1.2 million acres) of Indigenous territories and protected areas were affected by mining between 2018 and 2025." }, { "kind": "news report", "citation": "Malleret, C. (2026). Same article, method sentence.", "title": "AI tool tracks spread of illegal gold mining in Amazon protected areas (method)", "url": "https://news.mongabay.com/2026/04/ai-tool-tracks-spread-of-illegal-gold-mining-in-amazon-protected-areas/", "date": "2026-04-24", "written_by": "independent", "quote": "Amazon Mining Watch trains artificial intelligence algorithms to recognize mining scars in satellite images." }, { "kind": "press release", "citation": "Amazon Mining Watch (n.d.). About. amazonminingwatch.org.", "title": "Amazon Mining Watch: About", "url": "https://amazonminingwatch.org/en/about", "date": null, "written_by": "makers", "quote": "Amazon Mining Watch is a collaborative platform developed through a partnership between the Pulitzer Center's Rainforest Investigations Network, Amazon Conservation Association, and Earth Genome." }, { "kind": "other", "citation": "Earth Genome (2026). Gold Mine Detector, README (August 2026 updates and Methodology). GitHub repository earthrise-media/mining-detector.", "title": "Gold Mine Detector README (Methodology)", "url": "https://github.com/earthrise-media/mining-detector", "date": null, "written_by": "makers", "quote": "The mine detector is an ensemble of convolutional neural networks trained to discriminate mines from other terrain using hand-labeled examples in Sentinel-2 L1C imagery." }, { "kind": "other", "citation": "Earth Genome (2026). Same README, Persistence section, evaluation of cumulative 2024 detections.", "title": "Gold Mine Detector README (Persistence)", "url": "https://github.com/earthrise-media/mining-detector", "date": null, "written_by": "makers", "quote": "Venezuela holdout: specificity 0.976, sensitivity 0.991" }, { "kind": "report", "citation": "Finer, M. et al. (2019). MAAP #104: Major Reduction in Illegal Gold Mining from Peru's Operation Mercury. Monitoring of the Andean Amazon Project, Amazon Conservation, 4 August 2019 (no mention of machine learning or AI).", "title": "MAAP #104: Major Reduction in Illegal Gold Mining from Peru's Operation Mercury", "url": "https://www.maapprogram.org/lapampa_opermercury/", "date": "2019-08-04", "written_by": "makers", "quote": "The gold mining deforestation decreased 92% between 2018 (900 hectares) and 2019 (67 hectares)" } ], "replication": { "produced_by": [ "Free Sentinel-2 imagery composited on Google Earth Engine", "Hand-labeled training set of 23,463 patches built from earlier human surveys with journalists", "Ensemble CNN detector run over the whole basin yearly and quarterly", "Public release of detections and code for journalists and NGOs" ], "must_hold": [ "Cloud-free composites over the period monitored", "Mining scars visually distinct from sandbars, braided rivers and aquaculture ponds", "Continued labeling and corroboration across years to limit false detections", "A user, agency or newsroom willing to act on detections, which no cited source documents" ], "echoes": [ { "date": "2019-08-04", "setting": "MAAP #104, La Pampa, Peru: 92% drop in mining deforestation after Operation Mercury, analyzed from satellite imagery without AI", "model_version": null, "outcome": "unknown", "source": "https://www.maapprogram.org/lapampa_opermercury/" }, { "date": "2026-04-24", "setting": "Quarterly “Panorama” report, Q4 2025: about 6,000 ha of mining deforestation in protected areas and Indigenous territories (Mongabay)", "model_version": null, "outcome": "recurred", "source": "https://news.mongabay.com/2026/04/ai-tool-tracks-spread-of-illegal-gold-mining-in-amazon-protected-areas/" } ] }, "paired_incident": null, "headline": "Amazon Mining Watch maps 496,000 ha of mining in protected areas", "metric": "496,000 hectares affected between 2018 and 2025", "comparator": { "against": "hand-labeled Sentinel-2 patches (Venezuela geographic holdout)", "before": null, "after": "specificity 0.976, sensitivity 0.991", "basis": "measured", "quote": "For 2024, the last period with confirmed detections at time of writing, the cumulative detections return the following scores with respect to the evaluation locations used above: Venezuela holdout: specificity 0.976, sensitivity 0.991", "url": "https://github.com/earthrise-media/mining-detector" }, "later": { "status": "persisted", "date": "2026-08-01", "note": "Earth Genome published a full rebuild of the Amazon Mining Watch models and data in August 2026, continuing the platform after the April 2026 report.", "quote": "In August 2026 we published a full rebuild of Amazon Mining Watch models and data, to improve model sensitivity and to account for monitoring conditions on shorter time intervals (especially, increased cloud cover).", "url": "https://github.com/earthrise-media/mining-detector" }, "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": "Added what happened since from the cited sources.", "source": "https://github.com/earthrise-media/mining-detector", "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 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 added from the organization's own site or a registry.", "source": "https://projects.propublica.org/nonprofits/organizations/472935807", "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, the makers' report, the makers' press release and the makers' other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0348", "catalog_version": "1.0.0", "source_fingerprint": "65329a090a51f990f3856c3ff0e717fac09db0cac334467ef2bdf8968a426135", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0348-source-0001", "AIBD-0348-source-0002", "AIBD-0348-source-0003", "AIBD-0348-source-0004", "AIBD-0348-source-0005", "AIBD-0348-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": "Earth Genome (model), with Amazon Conservation Association and the Pulitzer Center's Rainforest Investigations Network", "deployer": "Amazon Mining Watch partnership (public web 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": "The AI system produces the maps and area statistics themselves. The makers' repository describes an ensemble of convolutional neural networks on Sentinel-2 patches, with reported holdout specificity 0.976 and sensitivity 0.991. Mongabay independently describes the platform as training AI algorithms to recognize mining scars. No cited source links a detection to an enforcement action. Mongabay remarks that the platform is useful for cross-border work, citing 2025 Brazil-Colombia operations on the Pure River. It does not state that those operations used its detections. MAAP's Operation Mercury analysis, by the same maker, used satellite imagery without machine learning." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "The AI system produces the maps and area statistics themselves. The makers' repository describes an ensemble of convolutional neural networks on Sentinel-2 patches, with reported holdout specificity 0.976 and sensitivity 0.991. Mongabay independently describes the platform as training AI algorithms to recognize mining scars. No cited source links a detection to an enforcement action. Mongabay remarks that the platform is useful for cross-border work, citing 2025 Brazil-Colombia operations on the Pure River. It does not state that those operations used its detections. MAAP's Operation Mercury analysis, by the same maker, used satellite imagery without machine learning." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report, the makers' report, the makers' press release and the makers' other. 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"provenance": { "kind": "legacy_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 documented." }, { "group": "organizational", "reason": "Journalists and non-governmental organizations (NGOs) use the maps, but no organization's decision is documented as changed by a detection." }, { "group": "societal", "reason": "Basin-wide statistics exist, but no enforcement or policy outcome is attributed to the tool in a cited source." } ] } }, 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(2026). AI tool tracks spread of illegal gold mining in Amazon protected areas. Mongabay, 24 April 2026.", "title": "AI tool tracks spread of illegal gold mining in Amazon protected areas", "url": "https://news.mongabay.com/2026/04/ai-tool-tracks-spread-of-illegal-gold-mining-in-amazon-protected-areas/", "date": "2026-04-24", "written_by": "independent", "quote": "496,000 hectares (1.2 million acres) of Indigenous territories and protected areas were affected by mining between 2018 and 2025." }, { "kind": "news report", "citation": "Malleret, C. (2026). Same article, method sentence.", "title": "AI tool tracks spread of illegal gold mining in Amazon protected areas (method)", "url": "https://news.mongabay.com/2026/04/ai-tool-tracks-spread-of-illegal-gold-mining-in-amazon-protected-areas/", "date": "2026-04-24", "written_by": "independent", "quote": "Amazon Mining Watch trains artificial intelligence algorithms to recognize mining scars in satellite images." }, { "kind": "press release", "citation": "Amazon Mining Watch (n.d.). About. amazonminingwatch.org.", "title": "Amazon Mining Watch: About", "url": "https://amazonminingwatch.org/en/about", "written_by": "makers", "quote": "Amazon Mining Watch is a collaborative platform developed through a partnership between the Pulitzer Center's Rainforest Investigations Network, Amazon Conservation Association, and Earth Genome." }, { "kind": "other", "citation": "Earth Genome (2026). Gold Mine Detector, README (August 2026 updates and Methodology). GitHub repository earthrise-media/mining-detector.", "title": "Gold Mine Detector README (Methodology)", "url": "https://github.com/earthrise-media/mining-detector", "written_by": "makers", "quote": "The mine detector is an ensemble of convolutional neural networks trained to discriminate mines from other terrain using hand-labeled examples in Sentinel-2 L1C imagery." }, { "kind": "other", "citation": "Earth Genome (2026). Same README, Persistence section, evaluation of cumulative 2024 detections.", "title": "Gold Mine Detector README (Persistence)", "url": "https://github.com/earthrise-media/mining-detector", "written_by": "makers", "quote": "Venezuela holdout: specificity 0.976, sensitivity 0.991" }, { "kind": "report", "citation": "Finer, M. et al. (2019). MAAP #104: Major Reduction in Illegal Gold Mining from Peru's Operation Mercury. Monitoring of the Andean Amazon Project, Amazon Conservation, 4 August 2019 (no mention of machine learning or AI).", "title": "MAAP #104: Major Reduction in Illegal Gold Mining from Peru's Operation Mercury", "url": "https://www.maapprogram.org/lapampa_opermercury/", "date": "2019-08-04", "written_by": "makers", "quote": "The gold mining deforestation decreased 92% between 2018 (900 hectares) and 2019 (67 hectares)" } ] } }, "provenance": { "kind": "legacy_record", "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-0348-source-0001", "AIBD-0348-source-0002", "AIBD-0348-source-0003", "AIBD-0348-source-0004", "AIBD-0348-source-0005", "AIBD-0348-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": "Malleret, C. (2026). AI tool tracks spread of illegal gold mining in Amazon protected areas. Mongabay, 24 April 2026.", "title": "AI tool tracks spread of illegal gold mining in Amazon protected areas", "url": "https://news.mongabay.com/2026/04/ai-tool-tracks-spread-of-illegal-gold-mining-in-amazon-protected-areas/", "date": "2026-04-24", "written_by": "independent", "quote": "496,000 hectares (1.2 million acres) of Indigenous territories and protected areas were affected by mining between 2018 and 2025." }, { "kind": "news report", "citation": "Malleret, C. (2026). Same article, method sentence.", "title": "AI tool tracks spread of illegal gold mining in Amazon protected areas (method)", "url": "https://news.mongabay.com/2026/04/ai-tool-tracks-spread-of-illegal-gold-mining-in-amazon-protected-areas/", "date": "2026-04-24", "written_by": "independent", "quote": "Amazon Mining Watch trains artificial intelligence algorithms to recognize mining scars in satellite images." }, { "kind": "press release", "citation": "Amazon Mining Watch (n.d.). About. amazonminingwatch.org.", "title": "Amazon Mining Watch: About", "url": "https://amazonminingwatch.org/en/about", "written_by": "makers", "quote": "Amazon Mining Watch is a collaborative platform developed through a partnership between the Pulitzer Center's Rainforest Investigations Network, Amazon Conservation Association, and Earth Genome." }, { "kind": "other", "citation": "Earth Genome (2026). Gold Mine Detector, README (August 2026 updates and Methodology). GitHub repository earthrise-media/mining-detector.", "title": "Gold Mine Detector README (Methodology)", "url": "https://github.com/earthrise-media/mining-detector", "written_by": "makers", "quote": "The mine detector is an ensemble of convolutional neural networks trained to discriminate mines from other terrain using hand-labeled examples in Sentinel-2 L1C imagery." }, { "kind": "other", "citation": "Earth Genome (2026). Same README, Persistence section, evaluation of cumulative 2024 detections.", "title": "Gold Mine Detector README (Persistence)", "url": "https://github.com/earthrise-media/mining-detector", "written_by": "makers", "quote": "Venezuela holdout: specificity 0.976, sensitivity 0.991" }, { "kind": "report", "citation": "Finer, M. et al. (2019). MAAP #104: Major Reduction in Illegal Gold Mining from Peru's Operation Mercury. Monitoring of the Andean Amazon Project, Amazon Conservation, 4 August 2019 (no mention of machine learning or AI).", "title": "MAAP #104: Major Reduction in Illegal Gold Mining from Peru's Operation Mercury", "url": "https://www.maapprogram.org/lapampa_opermercury/", "date": "2019-08-04", "written_by": "makers", "quote": "The gold mining deforestation decreased 92% between 2018 (900 hectares) and 2019 (67 hectares)" } ] } }, "provenance": { "kind": "legacy_record", "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 mining detector (Earth Genome, Amazon Conservation, Pulitzer Center) maps mining scars basin-wide; 496,000 ha in protected areas 2018–2025 reported Apr 2026; no attributable enforcement event.", "account": "Amazon Mining Watch is a public platform built by the Pulitzer Center's Rainforest Investigations Network, Amazon Conservation Association and Earth Genome. In its own words, it leverages artificial intelligence to map the impact of gold mining. Its code repository describes the detector as an ensemble of convolutional neural networks (CNNs) trained on hand-labeled Sentinel-2 patches. The detector is run over the 8.2 million km2 Amazon basin, with detections recomputed yearly for 2018–2025 and quarterly from 2025. The repository reports specificity 0.976 and sensitivity 0.991 on a Venezuelan geographic holdout for the cumulative 2024 detections. Mongabay reported on 24 April 2026, on the platform's quarterly report. That report found about 6,000 hectares of mining deforestation in protected areas and Indigenous territories in the last three months of 2025. It found 496,000 hectares affected between 2018 and 2025. The enforcement operations on record are documented by the same tool maker without any AI role. MAAP #104 (4 August 2019) attributes a 92% fall in gold-mining deforestation at La Pampa to Peru's Operation Mercury of February 2019. That analysis used satellite imagery (ESA, Maxar, Sentinel-1) with no mention of machine learning. No cited source documents an enforcement action taken on an Amazon Mining Watch detection." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The models detected mining activity in satellite images and calculated its extent." }, "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 repository identified the July 2026 production model as 48px_v4.10b-18d-20g-21a-22bc-ensemble." }, "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": "48px_v4.10b-18d-20g-21a-22bc-ensemble (July 2026 production model, per the repository README)" } }, "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": "48px_v4.10b-18d-20g-21a-22bc-ensemble.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Amazon Mining Watch gold-mine detector (ensemble of convolutional neural networks on Sentinel-2 imagery)" } }, "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": "Earth Genome (model), with Amazon Conservation Association and the Pulitzer Center's Rainforest Investigations Network" } }, "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": "Amazon Mining Watch's convolutional-network detector maps gold-mining scars basin-wide; 496,000 ha of protected and Indigenous lands affected 2018–2025, with no documented enforcement action", "headline": "Amazon Mining Watch maps 496,000 ha of mining in protected areas" } }, "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": [ "65329a090a51f990f3856c3ff0e717fac09db0cac334467ef2bdf8968a426135" ], "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": "Amazon Mining Watch is a public platform built by the Pulitzer Center's Rainforest Investigations Network, Amazon Conservation Association and Earth Genome. In its own words, it leverages artificial intelligence to map the impact of gold mining. Its code repository describes the detector as an ensemble of convolutional neural networks (CNNs) trained on hand-labeled Sentinel-2 patches. The detector is run over the 8.2 million km2 Amazon basin, with detections recomputed yearly for 2018–2025 and quarterly from 2025. The repository reports specificity 0.976 and sensitivity 0.991 on a Venezuelan geographic holdout for the cumulative 2024 detections. Mongabay reported on 24 April 2026, on the platform's quarterly report. That report found about 6,000 hectares of mining deforestation in protected areas and Indigenous territories in the last three months of 2025. It found 496,000 hectares affected between 2018 and 2025. The enforcement operations on record are documented by the same tool maker without any AI role. MAAP #104 (4 August 2019) attributes a 92% fall in gold-mining deforestation at La Pampa to Peru's Operation Mercury of February 2019. That analysis used satellite imagery (ESA, Maxar, Sentinel-1) with no mention of machine learning. No cited source documents an enforcement action taken on an Amazon Mining Watch detection.", "replication": { "produced_by": [ "Free Sentinel-2 imagery composited on Google Earth Engine", "Hand-labeled training set of 23,463 patches built from earlier human surveys with journalists", "Ensemble CNN detector run over the whole basin yearly and quarterly", "Public release of detections and code for journalists and NGOs" ], "must_hold": [ "Cloud-free composites over the period monitored", "Mining scars visually distinct from sandbars, braided rivers and aquaculture ponds", "Continued labeling and corroboration across years to limit false detections", "A user, agency or newsroom willing to act on detections, which no cited source documents" ], "echoes": [ { "date": "2019-08-04", "setting": "MAAP #104, La Pampa, Peru: 92% drop in mining deforestation after Operation Mercury, analyzed from satellite imagery without AI", "outcome": "unknown", "source": "https://www.maapprogram.org/lapampa_opermercury/" }, { "date": "2026-04-24", "setting": "Quarterly “Panorama” report, Q4 2025: about 6,000 ha of mining deforestation in protected areas and Indigenous territories (Mongabay)", "outcome": "recurred", "source": "https://news.mongabay.com/2026/04/ai-tool-tracks-spread-of-illegal-gold-mining-in-amazon-protected-areas/" } ] } } }, "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-0309", "case_url": "records/AIBD-0309.html", "reason": "The developer is headquartered in Canada; the case names Netherlands.", "details": [ { "label": "Developer headquarters", "value": "Canada" }, { "label": "Case jurisdictions", "value": "Netherlands" }, { "label": "Case location", "value": "Netherlands" } ], "filters": { "origin": "Canada", "destination": [ "Netherlands" ] }, "record": { "id": "AIBD-0309", "title": "ProRail replaces 288 lineside cameras with AI “smart cameras” in 2026 and uses Genetec video analytics across 7,000 cameras to flag trespassers to train dispatchers", "summary": "SpoorPro: ProRail swaps 288 lineside cameras for smart cameras in 2026, tests drones and runs Genetec video analytics on 7,000 cameras to flag trespassers to dispatchers; no results are given.", "date_observed": "2026-02-02", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "SpoorPro.nl reported on 2 February 2026 that ProRail, the Dutch rail infrastructure manager, is replacing 288 lineside cameras in 2026 with new “smart” cameras. ProRail is also testing drones that fly autonomously and analyze images. ProRail is quoted that these forms of camera surveillance help it see and prevent incidents sooner, making the track safer with fewer disruptions and delays. The article's photo caption says the AI cameras' images are monitored 24/7. The rest of the text is behind a paywall. SpoorPro reported on 3 March 2026 that ProRail is bringing the 7,000 security cameras it manages into one Genetec platform. It is monitored from ProRail's Smart Monitoring Room. Genetec Mission Control filters and analyzes alerts and forwards relevant images to an operator with suggested actions. Video analysis is used among other things to flag spoorlopers (people on the track). In that case the train dispatcher can warn drivers. The article quotes ProRail project lead Kristian Ruiter and Genetec's Bob van Keulen. No source gives a detection count, a stopped train or a trespass reduction attributable to the analytics. No source on record identifies a UIC trespass-detection trial.", "category": "general benefit", "unexpected": false, "purpose": "Camera upgrades and video analytics intended to detect incidents such as trespassers sooner so that dispatchers can warn drivers. No detection event is documented.", "system": "ProRail smart lineside cameras with Genetec Security Center / Mission Control video analytics", "model_version": null, "developer": "Genetec (video management and analytics platform); the smart-camera supplier is not named", "deployer": "ProRail", "location": "Netherlands", "domain": "Railway safety / trespass detection", "segment": "Rail infrastructure camera surveillance", "benefit_type": "Increased Product Safety", "taxonomy_note": "Deployment only; trespass detection for dispatchers is read as a safety detection mechanism.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "ProRail says the cameras help it see and prevent incidents sooner. No incident, detection count or delay figure is published." }, { "group": "societal", "depth": null, "reason": "Fewer trespass incidents and disruptions are the stated aim. No before-and-after data are given." } ], "ai_role": { "status": "unassessed", "statement": "No incident is documented. The AI character of the cameras rests on SpoorPro's caption and ProRail's “smart cameras”. The trespasser-flagging function is described by Genetec, the platform vendor. Whether the analytics changed any outcome is not reported. The full 2 February 2026 article is paywalled." }, "evidence": [ { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail zet 288 nóg slimmere camera's in langs het spoor. 2 February 2026 (remainder paywalled).", "title": "ProRail zet 288 nóg slimmere camera's in langs het spoor", "url": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/", "date": "2026-02-02", "written_by": "independent", "quote": "ProRail vervangt in 2026 in totaal 288 camera's langs het spoor voor nieuwe, slimme camera's." }, { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail gaat alle 7000 beveiligingscamera's centraal monitoren. 3 March 2026 (also published on Mobiliteit.nl).", "title": "ProRail gaat alle 7000 beveiligingscamera's centraal monitoren", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "date": "2026-03-03", "written_by": "independent", "quote": "Video-analyse wordt onder meer ingezet om spoorlopers te signaleren. In dat geval kan de treindienstleider machinisten waarschuwen." } ], "replication": { "produced_by": [ "Thousands of lineside and station security cameras brought into one Genetec video-management platform", "Operators in ProRail's Smart Monitoring Room receiving filtered alerts with suggested actions", "Video analytics configured to flag people on the track" ], "must_hold": [ "A dispatcher able to warn drivers once a trespasser is flagged", "Privacy masking and blurring applied to passenger images, as the article states ProRail does" ], "echoes": [] }, "paired_incident": null, "headline": "ProRail uses Genetec analytics on 7,000 cameras to flag trespass", "metric": null, "comparator": { "against": "ProRail's previous lineside camera surveillance (ProRail's own assertion)", "before": null, "after": "incidenten sneller te zien en te voorkomen", "basis": "assumed", "quote": "‘Deze nieuwe vormen van cameratoezicht helpen ons om incidenten sneller te zien en te voorkomen. Dat maakt het spoor veiliger en zorgt voor minder storingen en vertragingen.’", "url": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/" }, "later": { "status": "scaled", "date": "2026-03-03", "note": "A month later SpoorPro reported that ProRail is bringing all 7,000 cameras it manages into one Genetec platform monitored from its Smart Monitoring Room, with video analytics used among other things to flag people on the track to train dispatchers; no detection count or outcome is given.", "quote": "ProRail brengt de 7000 beveiligingscamera’s die de organisatie beheert onder in één geïntegreerd platform van technologiebedrijf Genetec. Daarmee wil de spoorbeheerder incidenten op en rond het spoor sneller signaleren en afhandelen.", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/" }, "value": null, "developer_country": "Canada", "deployer_country": "Netherlands", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/; https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wikidata.org/wiki/Q30267239; https://www.prorail.nl/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0309", "catalog_version": "1.0.0", "source_fingerprint": "a4a48ce8da38592a09e4f07df3b22efedbddebcbe5d7f00ddd3215a53e1fc190", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0309-source-0001", "AIBD-0309-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Genetec (video management and analytics platform); the smart-camera supplier is not named", "deployer": "ProRail" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "No incident is documented. The AI character of the cameras rests on SpoorPro's caption and ProRail's “smart cameras”. The trespasser-flagging function is described by Genetec, the platform vendor. Whether the analytics changed any outcome is not reported. 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"not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-03-03", "note": "A month later SpoorPro reported that ProRail is bringing all 7,000 cameras it manages into one Genetec platform monitored from its Smart Monitoring Room, with video analytics used among other things to flag people on the track to train dispatchers; no detection count or outcome is given.", "quote": "ProRail brengt de 7000 beveiligingscamera’s die de organisatie beheert onder in één geïntegreerd platform van technologiebedrijf Genetec. Daarmee wil de spoorbeheerder incidenten op en rond het spoor sneller signaleren en afhandelen.", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0309" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety", "taxonomy_note": "Deployment only; trespass detection for dispatchers is read as a safety detection mechanism." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "SpoorPro.nl reported on 2 February 2026 that ProRail, the Dutch rail infrastructure manager, is replacing 288 lineside cameras in 2026 with new “smart” cameras. ProRail is also testing drones that fly autonomously and analyze images. ProRail is quoted that these forms of camera surveillance help it see and prevent incidents sooner, making the track safer with fewer disruptions and delays. The article's photo caption says the AI cameras' images are monitored 24/7. The rest of the text is behind a paywall. SpoorPro reported on 3 March 2026 that ProRail is bringing the 7,000 security cameras it manages into one Genetec platform. It is monitored from ProRail's Smart Monitoring Room. Genetec Mission Control filters and analyzes alerts and forwards relevant images to an operator with suggested actions. Video analysis is used among other things to flag spoorlopers (people on the track). In that case the train dispatcher can warn drivers. The article quotes ProRail project lead Kristian Ruiter and Genetec's Bob van Keulen. No source gives a detection count, a stopped train or a trespass reduction attributable to the analytics. No source on record identifies a UIC trespass-detection trial.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "ProRail says the cameras help it see and prevent incidents sooner. No incident, detection count or delay figure is published." }, { "group": "societal", "reason": "Fewer trespass incidents and disruptions are the stated aim. No before-and-after data are given." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Camera upgrades and video analytics intended to detect incidents such as trespassers sooner so that dispatchers can warn drivers. No detection event is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Netherlands", "developer_country": "Canada", "deployer_country": "Netherlands" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Camera upgrades and video analytics intended to detect incidents such as trespassers sooner so that dispatchers can warn drivers. No detection event is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-02-02", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/; https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wikidata.org/wiki/Q30267239; https://www.prorail.nl/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Thousands of lineside and station security cameras brought into one Genetec video-management platform", "Operators in ProRail's Smart Monitoring Room receiving filtered alerts with suggested actions", "Video analytics configured to flag people on the track" ], "must_hold": [ "A dispatcher able to warn drivers once a trespasser is flagged", "Privacy masking and blurring applied to passenger images, as the article states ProRail does" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/; https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wikidata.org/wiki/Q30267239; https://www.prorail.nl/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Railway safety / trespass detection", "segment": "Rail infrastructure camera surveillance" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail zet 288 nóg slimmere camera's in langs het spoor. 2 February 2026 (remainder paywalled).", "title": "ProRail zet 288 nóg slimmere camera's in langs het spoor", "url": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/", "date": "2026-02-02", "written_by": "independent", "quote": "ProRail vervangt in 2026 in totaal 288 camera's langs het spoor voor nieuwe, slimme camera's." }, { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail gaat alle 7000 beveiligingscamera's centraal monitoren. 3 March 2026 (also published on Mobiliteit.nl).", "title": "ProRail gaat alle 7000 beveiligingscamera's centraal monitoren", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "date": "2026-03-03", "written_by": "independent", "quote": "Video-analyse wordt onder meer ingezet om spoorlopers te signaleren. In dat geval kan de treindienstleider machinisten waarschuwen." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0309-source-0001", "AIBD-0309-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail zet 288 nóg slimmere camera's in langs het spoor. 2 February 2026 (remainder paywalled).", "title": "ProRail zet 288 nóg slimmere camera's in langs het spoor", "url": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/", "date": "2026-02-02", "written_by": "independent", "quote": "ProRail vervangt in 2026 in totaal 288 camera's langs het spoor voor nieuwe, slimme camera's." }, { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail gaat alle 7000 beveiligingscamera's centraal monitoren. 3 March 2026 (also published on Mobiliteit.nl).", "title": "ProRail gaat alle 7000 beveiligingscamera's centraal monitoren", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "date": "2026-03-03", "written_by": "independent", "quote": "Video-analyse wordt onder meer ingezet om spoorlopers te signaleren. In dat geval kan de treindienstleider machinisten waarschuwen." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "SpoorPro: ProRail swaps 288 lineside cameras for smart cameras in 2026, tests drones and runs Genetec video analytics on 7,000 cameras to flag trespassers to dispatchers; no results are given.", "account": "SpoorPro.nl reported on 2 February 2026 that ProRail, the Dutch rail infrastructure manager, is replacing 288 lineside cameras in 2026 with new “smart” cameras. ProRail is also testing drones that fly autonomously and analyze images. ProRail is quoted that these forms of camera surveillance help it see and prevent incidents sooner, making the track safer with fewer disruptions and delays. The article's photo caption says the AI cameras' images are monitored 24/7. The rest of the text is behind a paywall. SpoorPro reported on 3 March 2026 that ProRail is bringing the 7,000 security cameras it manages into one Genetec platform. It is monitored from ProRail's Smart Monitoring Room. Genetec Mission Control filters and analyzes alerts and forwards relevant images to an operator with suggested actions. Video analysis is used among other things to flag spoorlopers (people on the track). In that case the train dispatcher can warn drivers. The article quotes ProRail project lead Kristian Ruiter and Genetec's Bob van Keulen. No source gives a detection count, a stopped train or a trespass reduction attributable to the analytics. No source on record identifies a UIC trespass-detection trial." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ProRail smart lineside cameras with Genetec Security Center / Mission Control video analytics" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Genetec (video management and analytics platform); the smart-camera supplier is not named" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ProRail replaces 288 lineside cameras with AI “smart cameras” in 2026 and uses Genetec video analytics across 7,000 cameras to flag trespassers to train dispatchers", "headline": "ProRail uses Genetec analytics on 7,000 cameras to flag trespass" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "a4a48ce8da38592a09e4f07df3b22efedbddebcbe5d7f00ddd3215a53e1fc190" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "SpoorPro.nl reported on 2 February 2026 that ProRail, the Dutch rail infrastructure manager, is replacing 288 lineside cameras in 2026 with new “smart” cameras. ProRail is also testing drones that fly autonomously and analyze images. ProRail is quoted that these forms of camera surveillance help it see and prevent incidents sooner, making the track safer with fewer disruptions and delays. The article's photo caption says the AI cameras' images are monitored 24/7. The rest of the text is behind a paywall. SpoorPro reported on 3 March 2026 that ProRail is bringing the 7,000 security cameras it manages into one Genetec platform. It is monitored from ProRail's Smart Monitoring Room. Genetec Mission Control filters and analyzes alerts and forwards relevant images to an operator with suggested actions. Video analysis is used among other things to flag spoorlopers (people on the track). In that case the train dispatcher can warn drivers. The article quotes ProRail project lead Kristian Ruiter and Genetec's Bob van Keulen. No source gives a detection count, a stopped train or a trespass reduction attributable to the analytics. No source on record identifies a UIC trespass-detection trial.", "replication": { "produced_by": [ "Thousands of lineside and station security cameras brought into one Genetec video-management platform", "Operators in ProRail's Smart Monitoring Room receiving filtered alerts with suggested actions", "Video analytics configured to flag people on the track" ], "must_hold": [ "A dispatcher able to warn drivers once a trespasser is flagged", "Privacy masking and blurring applied to passenger images, as the article states ProRail does" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 26, "reviewed": 0, "record_mapped": 26, "not_assessed": 65, "complete": false, "by_state": { "value_present": 26, "not_reported": 0, "unresolved": 0, "not_assessed": 65, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0325", "case_url": "records/AIBD-0325.html", "reason": "The developer is headquartered in United States; the case names Egypt.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Egypt" }, { "label": "Case location", "value": "Abnoub and Assiut University Hospital, Assiut Governorate, Egypt" } ], "filters": { "origin": "United States", "destination": [ "Egypt" ] }, "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": "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": "2025-05-24-415b" } } }, "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": "The developer is headquartered in Israel; the case names India, Pakistan.", "details": [ { "label": "Developer headquarters", "value": "Israel" }, { "label": "Case jurisdictions", "value": "India, Pakistan" }, { "label": "Case location", "value": "India-Pakistan border airspace, Operation Sindoor, 7-10 May 2025" } ], "filters": { "origin": "Israel", "destination": [ "India", "Pakistan" ] }, "record": { "id": "AIBD-0326", "title": "Rafael's X-Guard towed decoy, labeled “AI-powered”, may have diverted Pakistani missiles from IAF Rafales in Operation Sindoor (7–10 May 2025)", "summary": "Every account traces to ex-USAF pilot Ryan Bodenheimer via idrw.org (6 July 2025). No IAF, MoD, Dassault or Rafael confirmation; CDS Gen Anil Chauhan admitted on 31 May 2025 that India lost jets.", "date_observed": "2025-05-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The OECD AI Incidents entry concerns Operation Sindoor in May 2025. It says the Indian Air Force (IAF) used “the AI-powered X-Guard decoy system on Rafale jets”. It says this “prevented enemy missiles from hitting real aircraft”. The claim originates in an idrw.org post of 6 July 2025, sourced to “AFI”, an outsourced content partner. The post quotes Ryan Bodenheimer, a former US Air Force F-15E and F-16 pilot. He calls the IAF's tactics “the best spoofing and deception we've ever seen” and credits “the Rafale's X-Guard AI-enhanced jamming decoy system”. idrw itself writes that the system “likely played a pivotal role”. It writes that Pakistani claims of downed Rafales were “likely X-Guard decoys” according to Jane's Defence Weekly. The Economic Times (ET) writes on 8 July 2025 that the IAF “reportedly used artificial intelligence-powered technology”. LatestLY (9 Jul) says it “allegedly used” it. India TV (9 Jul) attributes a finding that Pakistani kills were hits on towed decoys to “a recent Jane's Defence Weekly investigation”. No other outlet on record corroborates it, and the Jane's Defence Weekly investigation itself is not among the sources. Oneindia (9 Jul) says false claims of downed Rafales “may have been decoys”. TFIPOST (9 Jul) says the IAF “reportedly deployed” the system and names the maker correctly as Rafael Advanced Defense Systems. ET, India TV, LatestLY, Oneindia and idrw write “Rafale Advanced Defense Systems”. The Week and Business Today are quoted from an earlier reading; their pages were not available afterward. The Week's headline says the IAF “may have used” X-Guard. It describes Bodenheimer as “a military tech digital creator”. Business Today writes “reportedly misdirected”. No IAF, Ministry of Defence (MoD), Dassault or Rafael statement confirms that X-Guard was carried or deployed. Rafael's product page was not available. The claimed outcome (no losses) is contradicted by India's Chief of Defence Staff (CDS), General Anil Chauhan. At the Shangri-La Dialogue on 31 May 2025 he admitted that an unspecified number of Indian jets were shot down. He rejected Pakistan's claim of six (Al Jazeera, 1 June 2025; Wikipedia citing Bloomberg). Operation Sindoor began on 7 May 2025 and lasted four days (India TV). The Monitor's 7 July date is the coverage date.", "category": "harm prevented", "unexpected": false, "purpose": "A towed decoy is built to present a false target to enemy radars and radar-guided missiles. They then engage the decoy rather than the aircraft. The outlets describe a 30 kg body on a 100 m fiber-optic cable emitting a 500 W jamming signal.", "system": "X-Guard fiber-optic towed decoy (Rafael Advanced Defense Systems), described by Indian outlets as 'AI-powered' with 'AI-enhanced algorithms' adjusting its emissions; no read manufacturer or official source describes it", "model_version": null, "developer": "Rafael Advanced Defense Systems (Israel); several outlets misname it 'Rafale Advanced Defense Systems'", "deployer": "Indian Air Force (claimed by commentary; unconfirmed by any official source)", "location": "India-Pakistan border airspace, Operation Sindoor, 7-10 May 2025", "domain": "Defense: electronic warfare", "segment": "Airborne towed radar decoys", "benefit_type": "Reduced Violence", "taxonomy_note": "Missiles diverted from aircraft are read as a physically harmful incident averted; no defense type exists.", "registry_ids": { "oecd_aim": "2025-07-07-7754" }, "ratings": [ { "group": "personal", "depth": null, "reason": "No pilot or aircraft is documented as saved by the decoy. The CDS admitted jet losses. The claim rests on one commentator's inference." }, { "group": "organizational", "depth": null, "reason": "No official account of the engagements names X-Guard. India's later move to expedite X-Guard deliveries (idrw search listing, 13 July 2025) is procurement news, not an outcome of this operation." } ], "ai_role": { "status": "unassessed", "statement": "No source independent of the commentary chain describes X-Guard as an AI system. idrw writes “AI-enhanced jamming decoy” and “AI-enhanced algorithms dynamically adjust signals” without attribution. ET, India TV, LatestLY, Oneindia and TFIPOST repeat the label. The Week gives no source for “AI-enabled”. Rafael's own page was unreadable. Deployment itself is unconfirmed by the IAF, MoD, Dassault or Rafael. The no-loss outcome is contradicted by the CDS. Neither the system's AI character nor any contribution to the engagements can therefore be assessed." }, "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2025-07-07-7754, 'AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor' (AI-generated summary).", "title": "AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor", "url": "https://oecd.ai/en/incidents/2025-07-07-7754", "date": "2025-07-07", "written_by": "independent", "quote": "This technology prevented enemy missiles from hitting real aircraft, enabling successful Indian airstrikes and avoiding loss of personnel and equipment." }, { "kind": "news report", "citation": "idrw.org (sourced to AFI), 6 July 2025, 'India's X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot' (origin of the claim).", "title": "India’s X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot", "url": "https://idrw.org/indias-x-guard-decoy-system-was-game-changing-deception-in-operation-sindoor-claims-former-f-15-pilot/", "date": "2025-07-06", "written_by": "independent", "quote": "crediting the Rafale’s X-Guard AI-enhanced jamming decoy system for outsmarting Pakistan’s air defenses" }, { "kind": "news report", "citation": "The Economic Times (ET Online), 8 July 2025, ''Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale'.", "title": "'Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale", "url": "https://economictimes.indiatimes.com/news/defence/best-deception-ever-seen-us-f-16-pilot-reveals-how-a-30-kg-device-fooled-pakistan-into-thinking-it-downed-a-rafale/articleshow/122313728.cms", "date": "2025-07-08", "written_by": "independent", "quote": "the Indian Air Force (IAF) reportedly used artificial intelligence-powered technology during a four-day military operation with Pakistan in May 2025" }, { "kind": "news report", "citation": "India TV News (ed. Ankit Kumar), 9 July 2025, 'Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained'.", "title": "Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained", "url": "https://www.indiatvnews.com/news/india/operation-sindoor-how-indias-ai-powered-ghost-rafales-outfoxed-pakistan-explained-2025-07-09-998147", "date": "2025-07-09", "written_by": "independent", "quote": "A recent Jane's Defence Weekly investigation has revealed that several Pakistani claims of downing Rafales were based on successful hits on towed decoys, not actual aircraft." }, { "kind": "news report", "citation": "LatestLY (Sunny Rodricks), 9 July 2025, 'What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System..'.", "title": "What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System Which Helped Air Force Neutralise Pakistani Air Threats During Operation Sindoor", "url": "https://www.latestly.com/india/news/what-is-x-guard-all-about-indian-rafales-ai-powered-towed-decoy-system-which-helped-air-force-neutralise-pakistani-air-threats-during-operation-sindoor-6982417.html", "date": "2025-07-09", "written_by": "independent", "quote": "it is learned that the Indian Air Force (IAF) allegedly used artificial intelligence-powered technology during Operation Sindoor against Pakistan in May 2025" }, { "kind": "news report", "citation": "TFIPOST (Harishanker R P), 9 July 2025, 'Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot'.", "title": "Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot", "url": "https://tfipost.com/2025/07/rafale-decoy-confused-pakistan-jets-during-operation-sindoor-claims-us-veteran-pilot", "date": "2025-07-09", "written_by": "independent", "quote": "Developed by Rafael Advanced Defense Systems, this smart decoy transformed the battlefield" }, { "kind": "news report", "citation": "Oneindia, 9 July 2025, 'AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses'.", "title": "AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses", "url": "https://www.oneindia.com/artificial-intelligence/ai-powered-decoy-system-turns-the-tide-how-indias-rafale-outsmarted-pakistans-air-defenses-7795811.html", "date": "2025-07-09", "written_by": "independent", "quote": "false claims of downed Rafales-many of which may have been decoys" }, { "kind": "news report", "citation": "Al Jazeera, 1 June 2025, 'India general admits jet losses in clash with Pakistan: Here's what he said'.", "title": "India general admits jet losses in clash with Pakistan: Here’s what he said", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "date": "2025-06-01", "written_by": "independent", "quote": "General Anil Chauhan, India’s chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month." }, { "kind": "other", "citation": "Wikipedia, '2025 India–Pakistan conflict', accessed 20 October 2026 (cites Bloomberg, 31 May 2025, 'India Confirms It Lost Fighter Jets in Recent Pakistan Conflict'; the article does not mention X-Guard).", "title": "2025 India–Pakistan conflict", "url": "https://en.wikipedia.org/wiki/2025_India%E2%80%93Pakistan_conflict", "date": null, "written_by": "independent", "quote": "On 31 May 2025, India's Chief of Defence Staff General Anil Chauhan admitted that India lost jets, but dismissed Pakistan's claim of downing six fighter jets." } ], "replication": { "produced_by": [ "A towed decoy on a fiber-optic cable emitting a jamming signal that mimics the aircraft's radar return and Doppler shift (as the outlets describe it)", "Radar-guided missiles (PL-15E, per the outlets) engaging the decoy rather than the aircraft" ], "must_hold": [ "Official confirmation that X-Guard was carried and deployed by the IAF in the 7–10 May 2025 engagements", "Evidence that missiles struck decoys rather than aircraft. India TV attributes this to Jane's Defence Weekly, but no other outlet corroborates it. It is at odds with the CDS's admission of losses." ], "echoes": [] }, "paired_incident": null, "headline": "X-Guard decoy may have diverted Pakistani missiles from IAF jets", "metric": null, "comparator": null, "later": { "status": "reversed", "date": "2025-05-31", "note": "India's chief of defence staff admitted on 31 May 2025 that an unspecified number of Indian jets were shot down in the May conflict, which contradicts the outlets' claim that the decoy kept missiles from hitting real aircraft.", "quote": "General Anil Chauhan, India's chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month.", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said" }, "value": null, "developer_country": "Israel", "deployer_country": "India", "unrated_because": "no official confirmation, and the CDS admitted losses", "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: no official confirmation, and the CDS admitted losses.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rafael_Advanced_Defense_Systems; https://en.wikipedia.org/wiki/Indian_Air_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: seven independent news reports, a registry entry and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0326", "catalog_version": "1.0.0", "source_fingerprint": "16fad776b788e393ffadc107502a285dd2a9c30163095ec46c10bfe8653abb72", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0326-source-0001", "AIBD-0326-source-0002", "AIBD-0326-source-0003", "AIBD-0326-source-0004", "AIBD-0326-source-0005", "AIBD-0326-source-0006", "AIBD-0326-source-0007", "AIBD-0326-source-0008", "AIBD-0326-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Rafael Advanced Defense Systems (Israel); several outlets misname it 'Rafale Advanced Defense Systems'", "deployer": "Indian Air Force (claimed by commentary; unconfirmed by any official source)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "unassessed", "statement": "No source independent of the commentary chain describes X-Guard as an AI system. idrw writes “AI-enhanced jamming decoy” and “AI-enhanced algorithms dynamically adjust signals” without attribution. ET, India TV, LatestLY, Oneindia and TFIPOST repeat the label. The Week gives no source for “AI-enabled”. Rafael's own page was unreadable. Deployment itself is unconfirmed by the IAF, MoD, Dassault or Rafael. The no-loss outcome is contradicted by the CDS. Neither the system's AI character nor any contribution to the engagements can therefore be assessed." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: seven independent news reports, a registry entry and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No pilot or aircraft is documented as saved by the decoy. The CDS admitted jet losses. The claim rests on one commentator's inference." }, { "group": "organizational", "reason": "No official account of the engagements names X-Guard. India's later move to expedite X-Guard deliveries (idrw search listing, 13 July 2025) is procurement news, not an outcome of this operation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No pilot or aircraft is documented as saved by the decoy. The CDS admitted jet losses. The claim rests on one commentator's inference." }, { "group": "organizational", "reason": "No official account of the engagements names X-Guard. India's later move to expedite X-Guard deliveries (idrw search listing, 13 July 2025) is procurement news, not an outcome of this operation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": 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aircraft.", "quote": "General Anil Chauhan, India's chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month.", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0326" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence", "taxonomy_note": "Missiles diverted from aircraft are read as a physically harmful incident averted; no defense type exists." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents entry concerns Operation Sindoor in May 2025. It says the Indian Air Force (IAF) used “the AI-powered X-Guard decoy system on Rafale jets”. It says this “prevented enemy missiles from hitting real aircraft”. The claim originates in an idrw.org post of 6 July 2025, sourced to “AFI”, an outsourced content partner. The post quotes Ryan Bodenheimer, a former US Air Force F-15E and F-16 pilot. He calls the IAF's tactics “the best spoofing and deception we've ever seen” and credits “the Rafale's X-Guard AI-enhanced jamming decoy system”. idrw itself writes that the system “likely played a pivotal role”. It writes that Pakistani claims of downed Rafales were “likely X-Guard decoys” according to Jane's Defence Weekly. The Economic Times (ET) writes on 8 July 2025 that the IAF “reportedly used artificial intelligence-powered technology”. LatestLY (9 Jul) says it “allegedly used” it. India TV (9 Jul) attributes a finding that Pakistani kills were hits on towed decoys to “a recent Jane's Defence Weekly investigation”. No other outlet on record corroborates it, and the Jane's Defence Weekly investigation itself is not among the sources. Oneindia (9 Jul) says false claims of downed Rafales “may have been decoys”. TFIPOST (9 Jul) says the IAF “reportedly deployed” the system and names the maker correctly as Rafael Advanced Defense Systems. ET, India TV, LatestLY, Oneindia and idrw write “Rafale Advanced Defense Systems”. The Week and Business Today are quoted from an earlier reading; their pages were not available afterward. The Week's headline says the IAF “may have used” X-Guard. It describes Bodenheimer as “a military tech digital creator”. Business Today writes “reportedly misdirected”. No IAF, Ministry of Defence (MoD), Dassault or Rafael statement confirms that X-Guard was carried or deployed. Rafael's product page was not available. The claimed outcome (no losses) is contradicted by India's Chief of Defence Staff (CDS), General Anil Chauhan. At the Shangri-La Dialogue on 31 May 2025 he admitted that an unspecified number of Indian jets were shot down. He rejected Pakistan's claim of six (Al Jazeera, 1 June 2025; Wikipedia citing Bloomberg). Operation Sindoor began on 7 May 2025 and lasted four days (India TV). The Monitor's 7 July date is the coverage date.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No pilot or aircraft is documented as saved by the decoy. The CDS admitted jet losses. The claim rests on one commentator's inference." }, { "group": "organizational", "reason": "No official account of the engagements names X-Guard. India's later move to expedite X-Guard deliveries (idrw search listing, 13 July 2025) is procurement news, not an outcome of this operation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "A towed decoy is built to present a false target to enemy radars and radar-guided missiles. They then engage the decoy rather than the aircraft. The outlets describe a 30 kg body on a 100 m fiber-optic cable emitting a 500 W jamming signal." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "India-Pakistan border airspace, Operation Sindoor, 7-10 May 2025", "developer_country": "Israel", "deployer_country": "India" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "A towed decoy is built to present a false target to enemy radars and radar-guided missiles. They then engage the decoy rather than the aircraft. The outlets describe a 30 kg body on a 100 m fiber-optic cable emitting a 500 W jamming signal." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-05-07", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: no official confirmation, and the CDS admitted losses.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rafael_Advanced_Defense_Systems; https://en.wikipedia.org/wiki/Indian_Air_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A towed decoy on a fiber-optic cable emitting a jamming signal that mimics the aircraft's radar return and Doppler shift (as the outlets describe it)", "Radar-guided missiles (PL-15E, per the outlets) engaging the decoy rather than the aircraft" ], "must_hold": [ "Official confirmation that X-Guard was carried and deployed by the IAF in the 7–10 May 2025 engagements", "Evidence that missiles struck decoys rather than aircraft. India TV attributes this to Jane's Defence Weekly, but no other outlet corroborates it. It is at odds with the CDS's admission of losses." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: no official confirmation, and the CDS admitted losses.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rafael_Advanced_Defense_Systems; https://en.wikipedia.org/wiki/Indian_Air_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Defense: electronic warfare", "segment": "Airborne towed radar decoys" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2025-07-07-7754, 'AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor' (AI-generated summary).", "title": "AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor", "url": "https://oecd.ai/en/incidents/2025-07-07-7754", "date": "2025-07-07", "written_by": "independent", "quote": "This technology prevented enemy missiles from hitting real aircraft, enabling successful Indian airstrikes and avoiding loss of personnel and equipment." }, { "kind": "news report", "citation": "idrw.org (sourced to AFI), 6 July 2025, 'India's X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot' (origin of the claim).", "title": "India’s X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot", "url": "https://idrw.org/indias-x-guard-decoy-system-was-game-changing-deception-in-operation-sindoor-claims-former-f-15-pilot/", "date": "2025-07-06", "written_by": "independent", "quote": "crediting the Rafale’s X-Guard AI-enhanced jamming decoy system for outsmarting Pakistan’s air defenses" }, { "kind": "news report", "citation": "The Economic Times (ET Online), 8 July 2025, ''Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale'.", "title": "'Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale", "url": "https://economictimes.indiatimes.com/news/defence/best-deception-ever-seen-us-f-16-pilot-reveals-how-a-30-kg-device-fooled-pakistan-into-thinking-it-downed-a-rafale/articleshow/122313728.cms", "date": "2025-07-08", "written_by": "independent", "quote": "the Indian Air Force (IAF) reportedly used artificial intelligence-powered technology during a four-day military operation with Pakistan in May 2025" }, { "kind": "news report", "citation": "India TV News (ed. Ankit Kumar), 9 July 2025, 'Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained'.", "title": "Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained", "url": "https://www.indiatvnews.com/news/india/operation-sindoor-how-indias-ai-powered-ghost-rafales-outfoxed-pakistan-explained-2025-07-09-998147", "date": "2025-07-09", "written_by": "independent", "quote": "A recent Jane's Defence Weekly investigation has revealed that several Pakistani claims of downing Rafales were based on successful hits on towed decoys, not actual aircraft." }, { "kind": "news report", "citation": "LatestLY (Sunny Rodricks), 9 July 2025, 'What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System..'.", "title": "What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System Which Helped Air Force Neutralise Pakistani Air Threats During Operation Sindoor", "url": "https://www.latestly.com/india/news/what-is-x-guard-all-about-indian-rafales-ai-powered-towed-decoy-system-which-helped-air-force-neutralise-pakistani-air-threats-during-operation-sindoor-6982417.html", "date": "2025-07-09", "written_by": "independent", "quote": "it is learned that the Indian Air Force (IAF) allegedly used artificial intelligence-powered technology during Operation Sindoor against Pakistan in May 2025" }, { "kind": "news report", "citation": "TFIPOST (Harishanker R P), 9 July 2025, 'Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot'.", "title": "Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot", "url": "https://tfipost.com/2025/07/rafale-decoy-confused-pakistan-jets-during-operation-sindoor-claims-us-veteran-pilot", "date": "2025-07-09", "written_by": "independent", "quote": "Developed by Rafael Advanced Defense Systems, this smart decoy transformed the battlefield" }, { "kind": "news report", "citation": "Oneindia, 9 July 2025, 'AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses'.", "title": "AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses", "url": "https://www.oneindia.com/artificial-intelligence/ai-powered-decoy-system-turns-the-tide-how-indias-rafale-outsmarted-pakistans-air-defenses-7795811.html", "date": "2025-07-09", "written_by": "independent", "quote": "false claims of downed Rafales-many of which may have been decoys" }, { "kind": "news report", "citation": "Al Jazeera, 1 June 2025, 'India general admits jet losses in clash with Pakistan: Here's what he said'.", "title": "India general admits jet losses in clash with Pakistan: Here’s what he said", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "date": "2025-06-01", "written_by": "independent", "quote": "General Anil Chauhan, India’s chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month." }, { "kind": "other", "citation": "Wikipedia, '2025 India–Pakistan conflict', accessed 20 October 2026 (cites Bloomberg, 31 May 2025, 'India Confirms It Lost Fighter Jets in Recent Pakistan Conflict'; the article does not mention X-Guard).", "title": "2025 India–Pakistan conflict", "url": "https://en.wikipedia.org/wiki/2025_India%E2%80%93Pakistan_conflict", "written_by": "independent", "quote": "On 31 May 2025, India's Chief of Defence Staff General Anil Chauhan admitted that India lost jets, but dismissed Pakistan's claim of downing six fighter jets." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0326-source-0001", "AIBD-0326-source-0002", "AIBD-0326-source-0003", "AIBD-0326-source-0004", "AIBD-0326-source-0005", "AIBD-0326-source-0006", "AIBD-0326-source-0007", "AIBD-0326-source-0008", "AIBD-0326-source-0009" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2025-07-07-7754, 'AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor' (AI-generated summary).", "title": "AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor", "url": "https://oecd.ai/en/incidents/2025-07-07-7754", "date": "2025-07-07", "written_by": "independent", "quote": "This technology prevented enemy missiles from hitting real aircraft, enabling successful Indian airstrikes and avoiding loss of personnel and equipment." }, { "kind": "news report", "citation": "idrw.org (sourced to AFI), 6 July 2025, 'India's X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot' (origin of the claim).", "title": "India’s X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot", "url": "https://idrw.org/indias-x-guard-decoy-system-was-game-changing-deception-in-operation-sindoor-claims-former-f-15-pilot/", "date": "2025-07-06", "written_by": "independent", "quote": "crediting the Rafale’s X-Guard AI-enhanced jamming decoy system for outsmarting Pakistan’s air defenses" }, { "kind": "news report", "citation": "The Economic Times (ET Online), 8 July 2025, ''Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale'.", "title": "'Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale", "url": "https://economictimes.indiatimes.com/news/defence/best-deception-ever-seen-us-f-16-pilot-reveals-how-a-30-kg-device-fooled-pakistan-into-thinking-it-downed-a-rafale/articleshow/122313728.cms", "date": "2025-07-08", "written_by": "independent", "quote": "the Indian Air Force (IAF) reportedly used artificial intelligence-powered technology during a four-day military operation with Pakistan in May 2025" }, { "kind": "news report", "citation": "India TV News (ed. Ankit Kumar), 9 July 2025, 'Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained'.", "title": "Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained", "url": "https://www.indiatvnews.com/news/india/operation-sindoor-how-indias-ai-powered-ghost-rafales-outfoxed-pakistan-explained-2025-07-09-998147", "date": "2025-07-09", "written_by": "independent", "quote": "A recent Jane's Defence Weekly investigation has revealed that several Pakistani claims of downing Rafales were based on successful hits on towed decoys, not actual aircraft." }, { "kind": "news report", "citation": "LatestLY (Sunny Rodricks), 9 July 2025, 'What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System..'.", "title": "What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System Which Helped Air Force Neutralise Pakistani Air Threats During Operation Sindoor", "url": "https://www.latestly.com/india/news/what-is-x-guard-all-about-indian-rafales-ai-powered-towed-decoy-system-which-helped-air-force-neutralise-pakistani-air-threats-during-operation-sindoor-6982417.html", "date": "2025-07-09", "written_by": "independent", "quote": "it is learned that the Indian Air Force (IAF) allegedly used artificial intelligence-powered technology during Operation Sindoor against Pakistan in May 2025" }, { "kind": "news report", "citation": "TFIPOST (Harishanker R P), 9 July 2025, 'Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot'.", "title": "Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot", "url": "https://tfipost.com/2025/07/rafale-decoy-confused-pakistan-jets-during-operation-sindoor-claims-us-veteran-pilot", "date": "2025-07-09", "written_by": "independent", "quote": "Developed by Rafael Advanced Defense Systems, this smart decoy transformed the battlefield" }, { "kind": "news report", "citation": "Oneindia, 9 July 2025, 'AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses'.", "title": "AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses", "url": "https://www.oneindia.com/artificial-intelligence/ai-powered-decoy-system-turns-the-tide-how-indias-rafale-outsmarted-pakistans-air-defenses-7795811.html", "date": "2025-07-09", "written_by": "independent", "quote": "false claims of downed Rafales-many of which may have been decoys" }, { "kind": "news report", "citation": "Al Jazeera, 1 June 2025, 'India general admits jet losses in clash with Pakistan: Here's what he said'.", "title": "India general admits jet losses in clash with Pakistan: Here’s what he said", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "date": "2025-06-01", "written_by": "independent", "quote": "General Anil Chauhan, India’s chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month." }, { "kind": "other", "citation": "Wikipedia, '2025 India–Pakistan conflict', accessed 20 October 2026 (cites Bloomberg, 31 May 2025, 'India Confirms It Lost Fighter Jets in Recent Pakistan Conflict'; the article does not mention X-Guard).", "title": "2025 India–Pakistan conflict", "url": "https://en.wikipedia.org/wiki/2025_India%E2%80%93Pakistan_conflict", "written_by": "independent", "quote": "On 31 May 2025, India's Chief of Defence Staff General Anil Chauhan admitted that India lost jets, but dismissed Pakistan's claim of downing six fighter jets." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Every account traces to ex-USAF pilot Ryan Bodenheimer via idrw.org (6 July 2025). No IAF, MoD, Dassault or Rafael confirmation; CDS Gen Anil Chauhan admitted on 31 May 2025 that India lost jets.", "account": "The OECD AI Incidents entry concerns Operation Sindoor in May 2025. It says the Indian Air Force (IAF) used “the AI-powered X-Guard decoy system on Rafale jets”. It says this “prevented enemy missiles from hitting real aircraft”. The claim originates in an idrw.org post of 6 July 2025, sourced to “AFI”, an outsourced content partner. The post quotes Ryan Bodenheimer, a former US Air Force F-15E and F-16 pilot. He calls the IAF's tactics “the best spoofing and deception we've ever seen” and credits “the Rafale's X-Guard AI-enhanced jamming decoy system”. idrw itself writes that the system “likely played a pivotal role”. It writes that Pakistani claims of downed Rafales were “likely X-Guard decoys” according to Jane's Defence Weekly. The Economic Times (ET) writes on 8 July 2025 that the IAF “reportedly used artificial intelligence-powered technology”. LatestLY (9 Jul) says it “allegedly used” it. India TV (9 Jul) attributes a finding that Pakistani kills were hits on towed decoys to “a recent Jane's Defence Weekly investigation”. No other outlet on record corroborates it, and the Jane's Defence Weekly investigation itself is not among the sources. Oneindia (9 Jul) says false claims of downed Rafales “may have been decoys”. TFIPOST (9 Jul) says the IAF “reportedly deployed” the system and names the maker correctly as Rafael Advanced Defense Systems. ET, India TV, LatestLY, Oneindia and idrw write “Rafale Advanced Defense Systems”. The Week and Business Today are quoted from an earlier reading; their pages were not available afterward. The Week's headline says the IAF “may have used” X-Guard. It describes Bodenheimer as “a military tech digital creator”. Business Today writes “reportedly misdirected”. No IAF, Ministry of Defence (MoD), Dassault or Rafael statement confirms that X-Guard was carried or deployed. Rafael's product page was not available. The claimed outcome (no losses) is contradicted by India's Chief of Defence Staff (CDS), General Anil Chauhan. At the Shangri-La Dialogue on 31 May 2025 he admitted that an unspecified number of Indian jets were shot down. He rejected Pakistan's claim of six (Al Jazeera, 1 June 2025; Wikipedia citing Bloomberg). Operation Sindoor began on 7 May 2025 and lasted four days (India TV). The Monitor's 7 July date is the coverage date." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "X-Guard fiber-optic towed decoy (Rafael Advanced Defense Systems), described by Indian outlets as 'AI-powered' with 'AI-enhanced algorithms' adjusting its emissions; no read manufacturer or official source describes it" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Rafael Advanced Defense Systems (Israel); several outlets misname it 'Rafale Advanced Defense Systems'" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Rafael's X-Guard towed decoy, labeled “AI-powered”, may have diverted Pakistani missiles from IAF Rafales in Operation Sindoor (7–10 May 2025)", "headline": "X-Guard decoy may have diverted Pakistani missiles from IAF jets" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "16fad776b788e393ffadc107502a285dd2a9c30163095ec46c10bfe8653abb72" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents entry concerns Operation Sindoor in May 2025. It says the Indian Air Force (IAF) used “the AI-powered X-Guard decoy system on Rafale jets”. It says this “prevented enemy missiles from hitting real aircraft”. The claim originates in an idrw.org post of 6 July 2025, sourced to “AFI”, an outsourced content partner. The post quotes Ryan Bodenheimer, a former US Air Force F-15E and F-16 pilot. He calls the IAF's tactics “the best spoofing and deception we've ever seen” and credits “the Rafale's X-Guard AI-enhanced jamming decoy system”. idrw itself writes that the system “likely played a pivotal role”. It writes that Pakistani claims of downed Rafales were “likely X-Guard decoys” according to Jane's Defence Weekly. The Economic Times (ET) writes on 8 July 2025 that the IAF “reportedly used artificial intelligence-powered technology”. LatestLY (9 Jul) says it “allegedly used” it. India TV (9 Jul) attributes a finding that Pakistani kills were hits on towed decoys to “a recent Jane's Defence Weekly investigation”. No other outlet on record corroborates it, and the Jane's Defence Weekly investigation itself is not among the sources. Oneindia (9 Jul) says false claims of downed Rafales “may have been decoys”. TFIPOST (9 Jul) says the IAF “reportedly deployed” the system and names the maker correctly as Rafael Advanced Defense Systems. ET, India TV, LatestLY, Oneindia and idrw write “Rafale Advanced Defense Systems”. The Week and Business Today are quoted from an earlier reading; their pages were not available afterward. The Week's headline says the IAF “may have used” X-Guard. It describes Bodenheimer as “a military tech digital creator”. Business Today writes “reportedly misdirected”. No IAF, Ministry of Defence (MoD), Dassault or Rafael statement confirms that X-Guard was carried or deployed. Rafael's product page was not available. The claimed outcome (no losses) is contradicted by India's Chief of Defence Staff (CDS), General Anil Chauhan. At the Shangri-La Dialogue on 31 May 2025 he admitted that an unspecified number of Indian jets were shot down. He rejected Pakistan's claim of six (Al Jazeera, 1 June 2025; Wikipedia citing Bloomberg). Operation Sindoor began on 7 May 2025 and lasted four days (India TV). The Monitor's 7 July date is the coverage date.", "replication": { "produced_by": [ "A towed decoy on a fiber-optic cable emitting a jamming signal that mimics the aircraft's radar return and Doppler shift (as the outlets describe it)", "Radar-guided missiles (PL-15E, per the outlets) engaging the decoy rather than the aircraft" ], "must_hold": [ "Official confirmation that X-Guard was carried and deployed by the IAF in the 7–10 May 2025 engagements", "Evidence that missiles struck decoys rather than aircraft. India TV attributes this to Jane's Defence Weekly, but no other outlet corroborates it. It is at odds with the CDS's admission of losses." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0357", "case_url": "records/AIBD-0357.html", "reason": "The developer is headquartered in United States; the case names Kenya.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Kenya" }, { "label": "Case location", "value": "Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, Kenya" } ], "filters": { "origin": "United States", "destination": [ "Kenya" ] }, "record": { "id": "AIBD-0357", "title": "Kenya Wildlife Service installs AI-enabled FLIR thermal cameras at Solio, Lake Nakuru and Ol Pejeta to send intrusion alerts to rhino rangers", "summary": "KWS installed AI-enabled FLIR thermal cameras at three black rhino sites by Sept 2024 (US State Department and WWF funding); intrusion alerts only; no AI-linked arrest or prevented event documented.", "date_observed": "2024-09-21", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Citizen Digital reported on 21 September 2024 that the Kenya Wildlife Service (KWS) had installed FLIR thermal cameras at three black rhino sites. The US Department of State Bureau of International Narcotics and Law Enforcement Affairs and WWF supported it. They are Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, with Solio the latest and Meru National Park named next. Geoffrey Chege (WWF-Kenya) said the cameras “have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife”. Colby Loucks of WWF-US said the cameras “can tell you when a human is in the picture or an animal” and alert rangers. KWS Director General Dr Erustus Kanga said: “I am aware that over the last six years, we have not lost a single black rhino in this sanctuary”. That predates the Solio installation. The article reports no arrest, interception or prevented poaching event attributable to the cameras.", "category": "general benefit", "unexpected": false, "purpose": "Automated night-time detection of human and vehicle intrusion at rhino sanctuaries to alert rangers. The claimed benefit is the intended one.", "system": "Teledyne FLIR thermal cameras with on-board AI analytics for human, vehicle and animal classification and automated ranger alerts", "model_version": null, "developer": "Teledyne FLIR (camera analytics; developer named only as FLIR in the cited source)", "deployer": "Kenya Wildlife Service with WWF-Kenya, WWF-US and WWF-UK; Solio Game Reserve management", "location": "Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, Kenya", "domain": "Wildlife protection and anti-poaching", "segment": "National wildlife authority and private conservancies", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": "Intrusion alerts at rhino sites protect an endangered species, which Trustible's description reaches only as “improved environment”; no interception is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary or ranger outcome is documented." }, { "group": "organizational", "depth": null, "reason": "KWS reports installation and intent, not a detected intrusion or response." }, { "group": "societal", "depth": null, "reason": "Kenya's reduced poaching figures cited in the article are not linked to the cameras." } ], "ai_role": { "status": "assessed", "statement": "The sole cited source is one news report (Citizen Digital, 21 September 2024). It describes AI analytics that classify humans, vehicles and animals in thermal video and alert rangers automatically. The statements come from WWF and KWS officials at the handover ceremony. No alert, arrest or prevented poaching event is attributed to the system in any cited source. Its contribution to rhino protection is not established." }, "evidence": [ { "kind": "news report", "citation": "Citizen Reporter (2024). KWS, rhino sites using AI-enabled cameras to stop black rhino poaching. Citizen Digital, 21 Sep 2024.", "title": "KWS, rhino sites using AI-enabled cameras to stop black rhino poaching", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "The artificial intelligence-enabled FLIR cameras have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, KWS Director General Dr Erustus Kanga quoted.", "title": "KWS, rhino sites using AI-enabled cameras (Director General statement)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "I am aware that over the last six years, we have not lost a single black rhino in this sanctuary." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, funding statement.", "title": "KWS, rhino sites using AI-enabled cameras (funding)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "came from the US State Department through WWF US and the WWF UK" } ], "replication": { "produced_by": [ "Thermal cameras with on-board human/vehicle/animal analytics at sanctuary perimeters", "Ranger teams receiving automated alerts", "Donor funding (US State Department INL via WWF) for installation and upgrades" ], "must_hold": [ "Camera coverage of intrusion routes", "Ranger response capacity at night", "Maintenance and power at remote sites", "Evaluation that records alerts and responses, absent so far" ], "echoes": [ { "date": null, "setting": "Lake Nakuru National Park camera system upgraded under the same program", "model_version": null, "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" }, { "date": null, "setting": "Meru National Park named as the next installation site", "model_version": null, "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" } ] }, "paired_incident": null, "headline": "KWS's FLIR cameras send intrusion alerts at three rhino sites", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "The article names Meru National Park as the next site; nothing dated after 21 September 2024 was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "Kenya", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Teledyne_FLIR; https://en.wikipedia.org/wiki/Kenya_Wildlife_Service", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report. 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KWS, rhino sites using AI-enabled cameras to stop black rhino poaching. Citizen Digital, 21 Sep 2024.", "title": "KWS, rhino sites using AI-enabled cameras to stop black rhino poaching", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "The artificial intelligence-enabled FLIR cameras have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, KWS Director General Dr Erustus Kanga quoted.", "title": "KWS, rhino sites using AI-enabled cameras (Director General statement)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "I am aware that over the last six years, we have not lost a single black rhino in this sanctuary." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, funding statement.", "title": "KWS, rhino sites using AI-enabled cameras (funding)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "came from the US State Department through WWF US and the WWF UK" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0357-source-0001", "AIBD-0357-source-0002", "AIBD-0357-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Citizen Reporter (2024). KWS, rhino sites using AI-enabled cameras to stop black rhino poaching. Citizen Digital, 21 Sep 2024.", "title": "KWS, rhino sites using AI-enabled cameras to stop black rhino poaching", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "The artificial intelligence-enabled FLIR cameras have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, KWS Director General Dr Erustus Kanga quoted.", "title": "KWS, rhino sites using AI-enabled cameras (Director General statement)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "I am aware that over the last six years, we have not lost a single black rhino in this sanctuary." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, funding statement.", "title": "KWS, rhino sites using AI-enabled cameras (funding)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "came from the US State Department through WWF US and the WWF UK" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "KWS installed AI-enabled FLIR thermal cameras at three black rhino sites by Sept 2024 (US State Department and WWF funding); intrusion alerts only; no AI-linked arrest or prevented event documented.", "account": "Citizen Digital reported on 21 September 2024 that the Kenya Wildlife Service (KWS) had installed FLIR thermal cameras at three black rhino sites. The US Department of State Bureau of International Narcotics and Law Enforcement Affairs and WWF supported it. They are Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, with Solio the latest and Meru National Park named next. Geoffrey Chege (WWF-Kenya) said the cameras “have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife”. Colby Loucks of WWF-US said the cameras “can tell you when a human is in the picture or an animal” and alert rangers. KWS Director General Dr Erustus Kanga said: “I am aware that over the last six years, we have not lost a single black rhino in this sanctuary”. That predates the Solio installation. The article reports no arrest, interception or prevented poaching event attributable to the cameras." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Teledyne FLIR thermal cameras with on-board AI analytics for human, vehicle and animal classification and automated ranger alerts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Teledyne FLIR (camera analytics; developer named only as FLIR in the cited source)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Kenya Wildlife Service installs AI-enabled FLIR thermal cameras at Solio, Lake Nakuru and Ol Pejeta to send intrusion alerts to rhino rangers", "headline": "KWS's FLIR cameras send intrusion alerts at three rhino sites" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "78575ef230d9f2d3194cbfda50cc133cbbe7be7f6d3e448b16246f06635e3eff" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Citizen Digital reported on 21 September 2024 that the Kenya Wildlife Service (KWS) had installed FLIR thermal cameras at three black rhino sites. The US Department of State Bureau of International Narcotics and Law Enforcement Affairs and WWF supported it. They are Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, with Solio the latest and Meru National Park named next. Geoffrey Chege (WWF-Kenya) said the cameras “have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife”. Colby Loucks of WWF-US said the cameras “can tell you when a human is in the picture or an animal” and alert rangers. KWS Director General Dr Erustus Kanga said: “I am aware that over the last six years, we have not lost a single black rhino in this sanctuary”. That predates the Solio installation. The article reports no arrest, interception or prevented poaching event attributable to the cameras.", "replication": { "produced_by": [ "Thermal cameras with on-board human/vehicle/animal analytics at sanctuary perimeters", "Ranger teams receiving automated alerts", "Donor funding (US State Department INL via WWF) for installation and upgrades" ], "must_hold": [ "Camera coverage of intrusion routes", "Ranger response capacity at night", "Maintenance and power at remote sites", "Evaluation that records alerts and responses, absent so far" ], "echoes": [ { "setting": "Lake Nakuru National Park camera system upgraded under the same program", "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" }, { "setting": "Meru National Park named as the next installation site", "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0315", "case_url": "records/AIBD-0315.html", "reason": "The developer is headquartered in United Kingdom; the case names Ukraine.", "details": [ { "label": "Developer headquarters", "value": "United Kingdom" }, { "label": "Case jurisdictions", "value": "Ukraine" }, { "label": "Case location", "value": "Ukraine" } ], "filters": { "origin": "United Kingdom", "destination": [ "Ukraine" ] }, "record": { "id": "AIBD-0315", "title": "HALO Trust pilots AWS-supported AI to find explosive remnants in drone imagery of Ukrainian minefields; it may be about 70% accurate, with no clearance outcome yet", "summary": "HALO announced a US$4m AWS package (25 June 2024) to trial AI on drone imagery of Ukrainian minefields; Reuters (30 April 2026) reports about 70% accuracy; no cleared land or found mine attributed.", "date_observed": "2024-06-25", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The HALO Trust announced a US$4 million package of support from Amazon Web Services on 25 June 2024. An archived copy of the page, dated 20 May 2026, is among the sources. It “will enable HALO to trial artificial intelligence (AI) to assist in the process of detecting debris of war (including landmines and other explosives) in drone imagery for existing minefields and battlefields in Ukraine”. It also covers machine-learning detection of damaged buildings in satellite imagery with SageMaker. By then HALO had flown 542 drone flights over Ukrainian minefields, producing 11 terabytes of imagery. It said training would require manually labeling thousands of images and ongoing vetting by HALO staff to remove false positives and negatives. A HALO page of 21 February 2025 describes the pilot through its head of research and development, Matthew Abercrombie. He says an analyst “can take three to five days to look through the imagery for an average minefield”. It reports no results. Reuters (Sergiy Karazy, 30 April 2026, syndicated by KELO) reported from Myrotske, 40 km north-west of Kyiv. HALO “has turned to AI to analyze high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already”. It quoted HALO media manager Olena Shustova. The article does not mention AWS and attributes no cleared land, found mine or time saving to the model. No later milestone is dated.", "category": "general benefit", "unexpected": false, "purpose": "Built to flag landmines and other debris of war in drone imagery so that HALO analysts spend less time searching thousands of images per minefield. The pilot is that intended use.", "system": "HALO Trust AI and machine-learning analysis of drone imagery for explosive remnants of war (hosted on AWS; SageMaker used for satellite-imagery damage detection)", "model_version": null, "developer": "The HALO Trust and development team with Amazon Web Services", "deployer": "The HALO Trust, Ukraine program", "location": "Ukraine", "domain": "Humanitarian mine action", "segment": "Survey and prioritization of contaminated land by a demining NGO", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The only figure is a pilot accuracy; faster analyst work is HALO's stated aim, not a result.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "HALO's pages state the aim (three to five analyst-days per minefield) and Reuters gives “around 70% accuracy”. No time saving, detection count or land release attributable to the model is documented." }, { "group": "societal", "depth": null, "reason": "No cited source links the AI pilot to any cleared hectare, found mine or avoided casualty in Ukraine." } ], "ai_role": { "status": "assessed", "statement": "The model is being trained to detect explosive remnants in drone imagery with HALO analysts labeling and vetting outputs. The only performance figure, around 70% accuracy, is reported by Reuters without its basis. The organization's own June 2024 and February 2025 pages report plans and workflow, not results. No independent evaluation, cleared area or detected-mine count is documented." }, "evidence": [ { "kind": "press release", "citation": "The HALO Trust (2024). AI to help accelerate landmine clearance. halotrust.org, 25 June 2024 (original URL now returns 404).", "title": "AI to help accelerate landmine clearance", "url": "http://web.archive.org/web/20260520220522/https://www.halotrust.org/news/artificial-intelligence-to-help-accelerate-landmine-clearance/", "date": "2024-06-25", "written_by": "makers", "quote": "HALO expects this pilot project will begin trialing drone imagery analysis using AI in the coming months." }, { "kind": "press release", "citation": "The HALO Trust (2025). Using drones and AI to eradicate landmines. halotrust.org, 21 February 2025.", "title": "Using drones and AI to eradicate landmines", "url": "https://www.halotrust.org/news/using-drones-and-ai-to-eradicate-landmines/", "date": "2025-02-21", "written_by": "makers", "quote": "It can take three to five days to look through the imagery for an average minefield," }, { "kind": "news report", "citation": "Karazy S (2026). Man, machine and AI combine in battle to clear Ukraine of mines. Reuters, 30 April 2026, syndicated by KELO.", "title": "Man, machine and AI combine in battle to clear Ukraine of mines", "url": "https://kelo.com/2026/04/30/man-machine-and-ai-combine-in-battle-to-clear-ukraine-of-mines/", "date": "2026-04-30", "written_by": "independent", "quote": "HALO Trust has turned to AI to analyse high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already." } ], "replication": { "produced_by": [ "High-resolution drone imagery of known minefields (542 flights, 11 terabytes by June 2024) manually labeled by HALO staff", "AWS cloud credits, storage and SageMaker tooling to train and host the models" ], "must_hold": [ "Trained HALO personnel vet model outputs to remove false positives and negatives before survey decisions", "Imagery resolution and ground conditions resemble the training minefields" ], "echoes": [] }, "paired_incident": null, "headline": "HALO's AI may spot mines in Ukraine with about 70% accuracy", "metric": "achieving around 70% accuracy already", "comparator": { "against": "manual analyst review of drone imagery of a minefield", "before": "three to five days to look through the imagery for an average minefield", "after": null, "basis": "assumed", "quote": "“The problem is that an analyst searching through thousands of images takes a very long time. It can take three to five days to look through the imagery for an average minefield,” said Abercrombie.", "url": "https://www.halotrust.org/news/using-drones-and-ai-to-eradicate-landmines/" }, "later": { "status": "persisted", "date": "2026-04-30", "note": "Reuters reported from Ukraine in April 2026 that HALO's AI trained on drone imagery was identifying mines and explosive remnants with around 70% accuracy, still with no clearance outcome attributed to it.", "quote": "Given the ⁠huge scale of the task, HALO Trust has turned to ⁠AI to analyse high-resolution drone imagery of contaminated areas and train systems to ​identify mines and explosive remnants, achieving around 70% accuracy already.", "url": "https://kelo.com/2026/04/30/man-machine-and-ai-combine-in-battle-to-clear-ukraine-of-mines/" }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.halotrust.org/news/using-drones-and-ai-to-eradicate-landmines/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.halotrust.org/contact-us/; https://www.halotrust.org/contact-us/", "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 The HALO Trust.", "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-0315", "catalog_version": "1.0.0", "source_fingerprint": "ea9458b9fb0cadf42b4bf16a68eda0d38176e92a9ee731a432964a53e4f9a156", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0315-source-0001", "AIBD-0315-source-0002", "AIBD-0315-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": "The HALO Trust and development team with Amazon Web Services", "deployer": "The HALO Trust, Ukraine program" } }, "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 is being trained to detect explosive remnants in drone imagery with HALO analysts labeling and vetting outputs. The only performance figure, around 70% accuracy, is reported by Reuters without its basis. The organization's own June 2024 and February 2025 pages report plans and workflow, not results. No independent evaluation, cleared area or detected-mine count is documented." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "The model is being trained to detect explosive remnants in drone imagery with HALO analysts labeling and vetting outputs. The only performance figure, around 70% accuracy, is reported by Reuters without its basis. The organization's own June 2024 and February 2025 pages report plans and workflow, not results. No independent evaluation, cleared area or detected-mine count is documented." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "HALO's pages state the aim (three to five analyst-days per minefield) and Reuters gives “around 70% accuracy”. No time saving, detection count or land release attributable to the model is documented." }, { "group": "societal", "reason": "No cited source links the AI pilot to any cleared hectare, found mine or avoided casualty in Ukraine." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "HALO's pages state the aim (three to five analyst-days per minefield) and Reuters gives “around 70% accuracy”. 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"value": { "kind": "record_fields", "fields": { "account": "The HALO Trust announced a US$4 million package of support from Amazon Web Services on 25 June 2024. An archived copy of the page, dated 20 May 2026, is among the sources. It “will enable HALO to trial artificial intelligence (AI) to assist in the process of detecting debris of war (including landmines and other explosives) in drone imagery for existing minefields and battlefields in Ukraine”. It also covers machine-learning detection of damaged buildings in satellite imagery with SageMaker. By then HALO had flown 542 drone flights over Ukrainian minefields, producing 11 terabytes of imagery. It said training would require manually labeling thousands of images and ongoing vetting by HALO staff to remove false positives and negatives. A HALO page of 21 February 2025 describes the pilot through its head of research and development, Matthew Abercrombie. He says an analyst “can take three to five days to look through the imagery for an average minefield”. It reports no results. Reuters (Sergiy Karazy, 30 April 2026, syndicated by KELO) reported from Myrotske, 40 km north-west of Kyiv. HALO “has turned to AI to analyze high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already”. It quoted HALO media manager Olena Shustova. The article does not mention AWS and attributes no cleared land, found mine or time saving to the model. 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AI to help accelerate landmine clearance. halotrust.org, 25 June 2024 (original URL now returns 404).", "title": "AI to help accelerate landmine clearance", "url": "http://web.archive.org/web/20260520220522/https://www.halotrust.org/news/artificial-intelligence-to-help-accelerate-landmine-clearance/", "date": "2024-06-25", "written_by": "makers", "quote": "HALO expects this pilot project will begin trialing drone imagery analysis using AI in the coming months." }, { "kind": "press release", "citation": "The HALO Trust (2025). Using drones and AI to eradicate landmines. halotrust.org, 21 February 2025.", "title": "Using drones and AI to eradicate landmines", "url": "https://www.halotrust.org/news/using-drones-and-ai-to-eradicate-landmines/", "date": "2025-02-21", "written_by": "makers", "quote": "It can take three to five days to look through the imagery for an average minefield," }, { "kind": "news report", "citation": "Karazy S (2026). Man, machine and AI combine in battle to clear Ukraine of mines. Reuters, 30 April 2026, syndicated by KELO.", "title": "Man, machine and AI combine in battle to clear Ukraine of mines", "url": "https://kelo.com/2026/04/30/man-machine-and-ai-combine-in-battle-to-clear-ukraine-of-mines/", "date": "2026-04-30", "written_by": "independent", "quote": "HALO Trust has turned to AI to analyse high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0315-source-0001", "AIBD-0315-source-0002", "AIBD-0315-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "The HALO Trust (2024). AI to help accelerate landmine clearance. halotrust.org, 25 June 2024 (original URL now returns 404).", "title": "AI to help accelerate landmine clearance", "url": "http://web.archive.org/web/20260520220522/https://www.halotrust.org/news/artificial-intelligence-to-help-accelerate-landmine-clearance/", "date": "2024-06-25", "written_by": "makers", "quote": "HALO expects this pilot project will begin trialing drone imagery analysis using AI in the coming months." }, { "kind": "press release", "citation": "The HALO Trust (2025). Using drones and AI to eradicate landmines. halotrust.org, 21 February 2025.", "title": "Using drones and AI to eradicate landmines", "url": "https://www.halotrust.org/news/using-drones-and-ai-to-eradicate-landmines/", "date": "2025-02-21", "written_by": "makers", "quote": "It can take three to five days to look through the imagery for an average minefield," }, { "kind": "news report", "citation": "Karazy S (2026). Man, machine and AI combine in battle to clear Ukraine of mines. Reuters, 30 April 2026, syndicated by KELO.", "title": "Man, machine and AI combine in battle to clear Ukraine of mines", "url": "https://kelo.com/2026/04/30/man-machine-and-ai-combine-in-battle-to-clear-ukraine-of-mines/", "date": "2026-04-30", "written_by": "independent", "quote": "HALO Trust has turned to AI to analyse high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "HALO announced a US$4m AWS package (25 June 2024) to trial AI on drone imagery of Ukrainian minefields; Reuters (30 April 2026) reports about 70% accuracy; no cleared land or found mine attributed.", "account": "The HALO Trust announced a US$4 million package of support from Amazon Web Services on 25 June 2024. An archived copy of the page, dated 20 May 2026, is among the sources. It “will enable HALO to trial artificial intelligence (AI) to assist in the process of detecting debris of war (including landmines and other explosives) in drone imagery for existing minefields and battlefields in Ukraine”. It also covers machine-learning detection of damaged buildings in satellite imagery with SageMaker. By then HALO had flown 542 drone flights over Ukrainian minefields, producing 11 terabytes of imagery. It said training would require manually labeling thousands of images and ongoing vetting by HALO staff to remove false positives and negatives. A HALO page of 21 February 2025 describes the pilot through its head of research and development, Matthew Abercrombie. He says an analyst “can take three to five days to look through the imagery for an average minefield”. It reports no results. Reuters (Sergiy Karazy, 30 April 2026, syndicated by KELO) reported from Myrotske, 40 km north-west of Kyiv. HALO “has turned to AI to analyze high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already”. It quoted HALO media manager Olena Shustova. The article does not mention AWS and attributes no cleared land, found mine or time saving to the model. No later milestone is dated." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "HALO Trust AI and machine-learning analysis of drone imagery for explosive remnants of war (hosted on AWS; SageMaker used for satellite-imagery damage detection)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "The HALO Trust and development team with Amazon Web Services" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "HALO Trust pilots AWS-supported AI to find explosive remnants in drone imagery of Ukrainian minefields; it may be about 70% accurate, with no clearance outcome yet", "headline": "HALO's AI may spot mines in Ukraine with about 70% accuracy" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "ea9458b9fb0cadf42b4bf16a68eda0d38176e92a9ee731a432964a53e4f9a156" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The HALO Trust announced a US$4 million package of support from Amazon Web Services on 25 June 2024. An archived copy of the page, dated 20 May 2026, is among the sources. It “will enable HALO to trial artificial intelligence (AI) to assist in the process of detecting debris of war (including landmines and other explosives) in drone imagery for existing minefields and battlefields in Ukraine”. It also covers machine-learning detection of damaged buildings in satellite imagery with SageMaker. By then HALO had flown 542 drone flights over Ukrainian minefields, producing 11 terabytes of imagery. It said training would require manually labeling thousands of images and ongoing vetting by HALO staff to remove false positives and negatives. A HALO page of 21 February 2025 describes the pilot through its head of research and development, Matthew Abercrombie. He says an analyst “can take three to five days to look through the imagery for an average minefield”. It reports no results. Reuters (Sergiy Karazy, 30 April 2026, syndicated by KELO) reported from Myrotske, 40 km north-west of Kyiv. HALO “has turned to AI to analyze high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already”. It quoted HALO media manager Olena Shustova. The article does not mention AWS and attributes no cleared land, found mine or time saving to the model. No later milestone is dated.", "replication": { "produced_by": [ "High-resolution drone imagery of known minefields (542 flights, 11 terabytes by June 2024) manually labeled by HALO staff", "AWS cloud credits, storage and SageMaker tooling to train and host the models" ], "must_hold": [ "Trained HALO personnel vet model outputs to remove false positives and negatives before survey decisions", "Imagery resolution and ground conditions resemble the training minefields" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0371", "case_url": "records/AIBD-0371.html", "reason": "The developer is headquartered in United States; the case names India.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "India" }, { "label": "Case location", "value": "Kota, India" } ], "filters": { "origin": "United States", "destination": [ "India" ] }, "record": { "id": "AIBD-0371", "title": "Kota police tie up with Meta to route suicide and self-harm flags from Facebook and Instagram to Abhay Command Centre; within a week police report a possible suicide by a Jhunjhunu student prevented", "summary": "Kota City SP Amrita Duhan said Meta's red-flag tags for suicidal posts go to a round-the-clock police team; within a week, police prevented a possible suicide by a student from Jhunjhunu (PTI).", "date_observed": "2024-05-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "PTI reported from Kota on 28 May 2024 that city police under Superintendent Amrita Duhan had tied up with Meta. It aims to identify students exhibiting suicidal tendencies on Facebook and Instagram. When a student posts suicide or self-harm content, a red-flag tag appears on Meta's system and account information is shared with Kota police. For students outside Kota it goes to Abhay Command Centre or the district control room. There a dedicated team works round the clock in eight-hour shifts. Within a week of the launch, police said they had prevented a possible suicide by a student from Jhunjhunu district. No red-flag tags had yet been reported in the city. PTI counted nine coaching-student suicides in 2024 to date and 26 in 2023. Duhan planned to extend the arrangement to Jaipur (PTI via LatestLY). Meta's newsroom describes machine-learning detection with human review before authorities are contacted (Card, 2018). The intervention date is not given.", "category": "protective response", "unexpected": false, "purpose": "Meta's pipeline is built to find posts indicating suicide or self-injury risk and route them to trained reviewers. In imminent cases it contacts local authorities (Meta Newsroom). The Kota arrangement routes those flags to police to intervene. This case is that purpose.", "system": "Meta suicide and self-injury detection on Facebook and Instagram, with red-flag tags shared with Kota police and Abhay Command Centre", "model_version": null, "developer": "Meta Platforms", "deployer": "Kota City Police (Rajasthan Police), Abhay Command Centre", "location": "Kota, India", "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police", "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health.", "registry_ids": { "oecd_aim": "2024-05-28-2dfd" }, "ratings": [ { "group": "personal", "depth": null, "reason": "PTI reports only that police “prevented a possible suicide by a student hailing from the Jhunjhunu district” within a week of the launch. The sources give no account of what the student posted, their condition when reached or what officers did. So the outcome is not established." } ], "ai_role": { "status": "assessed", "statement": "PTI's report says the chain starts with a red-flag tag on Meta's system when a student posts suicide or self-harm content. Account information is then shared with police. It does not say whether Meta's classifier or a user report raised the flag in the Jhunjhunu case, nor what happened on arrival. Meta's own description (2018, updated 2021) says machine-learning detection is followed by human review before authorities are contacted. The intervention itself was police work. PTI also notes that no red-flag tags had been reported in Kota city since the launch." }, "evidence": [ { "kind": "news report", "citation": "PTI (2024). Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students. LatestLY, 28 May 2024.", "title": "India News | Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students", "url": "https://www.latestly.com/agency-news/india-news-kota-police-ropes-in-meta-to-prevent-suicides-among-coaching-students-5996637.html", "date": "2024-05-28", "written_by": "independent", "quote": "the police have prevented a possible suicide by a student hailing from the Jhunjhunu district" }, { "kind": "news report", "citation": "PTI (2024). Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students. LatestLY, 28 May 2024 (second quotation).", "title": "India News | Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students", "url": "https://www.latestly.com/agency-news/india-news-kota-police-ropes-in-meta-to-prevent-suicides-among-coaching-students-5996637.html", "date": "2024-05-28", "written_by": "independent", "quote": "A dedicated team, working round the clock on eight-hour shifts, has been deputed at Abhay Command Centre" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom, 10 September 2018 (updated 29 September 2021).", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "Since these efforts began last year, we've worked with first responders on over 1,000 wellness checks based on reports we've received from our proactive detection efforts." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2024). Kota Police and Meta Use AI Monitoring to Prevent Student Suicides. OECD.AI, 28 May 2024.", "title": "Kota Police and Meta Use AI Monitoring to Prevent Student Suicides", "url": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "date": "2024-05-28", "written_by": "independent", "quote": "Within a week, officers intervened to avert a suicide attempt from a student in Jhunjhunu." } ], "replication": { "produced_by": [ "Meta's detection-and-review pipeline flagging suicide or self-harm posts by students on Facebook and Instagram", "A standing arrangement under which Meta shares account information with Kota police. For students outside Kota it goes to Abhay Command Centre or a district control room", "A police team monitoring the flags round the clock in eight-hour shifts" ], "must_hold": [ "The student must post on Facebook or Instagram in a way the pipeline recognizes", "Account details must resolve to a reachable location in time", "Police must be able to reach the student quickly" ], "echoes": [] }, "paired_incident": null, "headline": "Kota police act on a Meta self-harm flag within a week of tie-up", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": "United States", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Rajasthan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, a registry entry and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0371", "catalog_version": "1.0.0", "source_fingerprint": "fde9db963ea3a4d8e85db3224fe3d54c6602cd7c4f686e22a5a6be98d4698e74", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0371-source-0001", "AIBD-0371-source-0002", "AIBD-0371-source-0003", "AIBD-0371-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Meta Platforms", "deployer": "Kota City Police (Rajasthan Police), Abhay Command Centre" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "PTI's report says the chain starts with a red-flag tag on Meta's system when a student posts suicide or self-harm content. 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"account": "PTI reported from Kota on 28 May 2024 that city police under Superintendent Amrita Duhan had tied up with Meta. It aims to identify students exhibiting suicidal tendencies on Facebook and Instagram. When a student posts suicide or self-harm content, a red-flag tag appears on Meta's system and account information is shared with Kota police. For students outside Kota it goes to Abhay Command Centre or the district control room. There a dedicated team works round the clock in eight-hour shifts. Within a week of the launch, police said they had prevented a possible suicide by a student from Jhunjhunu district. No red-flag tags had yet been reported in the city. PTI counted nine coaching-student suicides in 2024 to date and 26 in 2023. Duhan planned to extend the arrangement to Jaipur (PTI via LatestLY). Meta's newsroom describes machine-learning detection with human review before authorities are contacted (Card, 2018). The intervention date is 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", "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" }, "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 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." } }, "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": "Kota, 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": "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." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2024-05-28", "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Rajasthan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Meta's detection-and-review pipeline flagging suicide or self-harm posts by students on Facebook and Instagram", "A standing arrangement under which Meta shares account information with Kota police. For students outside Kota it goes to Abhay Command Centre or a district control room", "A police team monitoring the flags round the clock in eight-hour shifts" ], "must_hold": [ "The student must post on Facebook or Instagram in a way the pipeline recognizes", "Account details must resolve to a reachable location in time", "Police must be able to reach the student quickly" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Rajasthan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "PTI (2024). Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students. LatestLY, 28 May 2024.", "title": "India News | Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students", "url": "https://www.latestly.com/agency-news/india-news-kota-police-ropes-in-meta-to-prevent-suicides-among-coaching-students-5996637.html", "date": "2024-05-28", "written_by": "independent", "quote": "the police have prevented a possible suicide by a student hailing from the Jhunjhunu district" }, { "kind": "news report", "citation": "PTI (2024). Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students. LatestLY, 28 May 2024 (second quotation).", "title": "India News | Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students", "url": "https://www.latestly.com/agency-news/india-news-kota-police-ropes-in-meta-to-prevent-suicides-among-coaching-students-5996637.html", "date": "2024-05-28", "written_by": "independent", "quote": "A dedicated team, working round the clock on eight-hour shifts, has been deputed at Abhay Command Centre" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom, 10 September 2018 (updated 29 September 2021).", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "Since these efforts began last year, we've worked with first responders on over 1,000 wellness checks based on reports we've received from our proactive detection efforts." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2024). Kota Police and Meta Use AI Monitoring to Prevent Student Suicides. OECD.AI, 28 May 2024.", "title": "Kota Police and Meta Use AI Monitoring to Prevent Student Suicides", "url": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "date": "2024-05-28", "written_by": "independent", "quote": "Within a week, officers intervened to avert a suicide attempt from a student in Jhunjhunu." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0371-source-0001", "AIBD-0371-source-0002", "AIBD-0371-source-0003", "AIBD-0371-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "PTI (2024). 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." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Kota City SP Amrita Duhan said Meta's red-flag tags for suicidal posts go to a round-the-clock police team; within a week, police prevented a possible suicide by a student from Jhunjhunu (PTI).", "account": "PTI reported from Kota on 28 May 2024 that city police under Superintendent Amrita Duhan had tied up with Meta. It aims to identify students exhibiting suicidal tendencies on Facebook and Instagram. When a student posts suicide or self-harm content, a red-flag tag appears on Meta's system and account information is shared with Kota police. For students outside Kota it goes to Abhay Command Centre or the district control room. There a dedicated team works round the clock in eight-hour shifts. Within a week of the launch, police said they had prevented a possible suicide by a student from Jhunjhunu district. No red-flag tags had yet been reported in the city. PTI counted nine coaching-student suicides in 2024 to date and 26 in 2023. Duhan planned to extend the arrangement to Jaipur (PTI via LatestLY). Meta's newsroom describes machine-learning detection with human review before authorities are contacted (Card, 2018). The intervention date is not given." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Meta suicide and self-injury detection on Facebook and Instagram, with red-flag tags shared with Kota police and Abhay Command Centre" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Meta Platforms" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Kota police tie up with Meta to route suicide and self-harm flags from Facebook and Instagram to Abhay Command Centre; within a week police report a possible suicide by a Jhunjhunu student prevented", "headline": "Kota police act on a Meta self-harm flag within a week of tie-up" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "fde9db963ea3a4d8e85db3224fe3d54c6602cd7c4f686e22a5a6be98d4698e74" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "PTI reported from Kota on 28 May 2024 that city police under Superintendent Amrita Duhan had tied up with Meta. It aims to identify students exhibiting suicidal tendencies on Facebook and Instagram. When a student posts suicide or self-harm content, a red-flag tag appears on Meta's system and account information is shared with Kota police. For students outside Kota it goes to Abhay Command Centre or the district control room. There a dedicated team works round the clock in eight-hour shifts. Within a week of the launch, police said they had prevented a possible suicide by a student from Jhunjhunu district. No red-flag tags had yet been reported in the city. PTI counted nine coaching-student suicides in 2024 to date and 26 in 2023. Duhan planned to extend the arrangement to Jaipur (PTI via LatestLY). Meta's newsroom describes machine-learning detection with human review before authorities are contacted (Card, 2018). The intervention date is not given.", "replication": { "produced_by": [ "Meta's detection-and-review pipeline flagging suicide or self-harm posts by students on Facebook and Instagram", "A standing arrangement under which Meta shares account information with Kota police. For students outside Kota it goes to Abhay Command Centre or a district control room", "A police team monitoring the flags round the clock in eight-hour shifts" ], "must_hold": [ "The student must post on Facebook or Instagram in a way the pipeline recognizes", "Account details must resolve to a reachable location in time", "Police must be able to reach the student quickly" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0356", "case_url": "records/AIBD-0356.html", "reason": "The developer is headquartered in United States; the case names Indonesia.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Indonesia" }, { "label": "Case location", "value": "Solok regency, West Sumatra, Indonesia (reported deployment); global program" } ], "filters": { "origin": "United States", "destination": [ "Indonesia" ] }, "record": { "id": "AIBD-0356", "title": "Rainforest Connection's Guardian acoustic AI sends chainsaw alerts to community patrols; logging in West Sumatra may have stopped as a result", "summary": "RFCx Guardian sensors detect chainsaws with AI and alert patrols; a 2021 Reuters report from Solok, West Sumatra quotes a patroller that logging stopped. No dated intervention is documented.", "date_observed": "2021-02-11", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Rainforest Connection (RFCx), a US non-profit founded by Topher White, deploys solar-powered “Guardian” acoustic sensors. In its own words, they use “sound & artificial intelligence” to track chainsaws, vehicle engines, hunting dogs and gunshots. They send real-time alerts to a mobile app so that “an agent nearby” can respond. RFCx states it has protected over 726,000 (impact page) or 750,000 (guardian page) hectares across 8,662+ projects in 137 countries. The only non-vendor account read is a Thomson Reuters Foundation report published by The Straits Times on 11 February 2021. It is from Solok regency, West Sumatra, Indonesia, where 27 Guardian sensors had been installed more than a year earlier. Audio streams are analyzed by AI trained to pick out chainsaw sounds, and an alert goes to community patrols. Patroller Jasrialdi said logging “has totally stopped” in the area. The head of West Sumatra's forestry department, Yozarwardi Usama Putra, said he would like to expand the “early-warning system”. The same report attributes the decline in logging to a wider enforcement crackdown as well. It says RFCx planned to collect data for peer review to prove the system curbs logging. No park authority, ministry or court document records a specific alert leading to an interception, so no event is recorded.", "category": "general benefit", "unexpected": false, "purpose": "Real-time acoustic detection of illegal logging and poaching sounds so that rangers or community patrols can respond; the reported benefit is the intended one.", "system": "Rainforest Connection Guardian bioacoustic monitoring with cloud AI sound classification and app alerts", "model_version": "Guardian G1 (2015-2019, repurposed smartphones), G2 (2020-2025), G3 (2026-present) per rfcx.org", "developer": "Rainforest Connection (RFCx)", "deployer": "Community forest patrols and the West Sumatra forestry department (Solok regency, Indonesia); RFCx partners in other countries", "location": "Solok regency, West Sumatra, Indonesia (reported deployment); global program", "domain": "Forest protection and anti-poaching", "segment": "Non-profit conservation technology with community and government partners", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual outcome is documented." }, { "group": "organizational", "depth": null, "reason": "The West Sumatra patrol's reported easier work is not tied to a dated alert or action." }, { "group": "societal", "depth": null, "reason": "RFCx's hectare figures are self-reported aggregates with no independent evaluation." } ], "ai_role": { "status": "assessed", "statement": "RFCx's own pages describe AI classification of streamed audio generating app alerts for responders. The 2021 Reuters report confirms the mechanism at the Solok site: AI picks out chainsaw sounds and patrols are alerted. It quotes a patroller that logging stopped, while also crediting a government enforcement crackdown. Apart from that single news item, only the maker's account is among the sources. No park authority, ministry or court record of an AI-triggered interception was found. RFCx itself said in 2021 that peer-review data to prove the effect was still being collected." }, "evidence": [ { "kind": "press release", "citation": "Rainforest Connection (n.d.). Real-Time Threat Detection (Guardian). rfcx.org, fetched 20 Oct 2026.", "title": "Real-Time Threat Detection", "url": "https://rfcx.org/guardian", "date": null, "written_by": "makers", "quote": "An agent nearby receives a real-time alert of the detected threat via our app, enabling rapid response before any damage can occur." }, { "kind": "press release", "citation": "Rainforest Connection (n.d.). Impact & Partnerships. rfcx.org, fetched 20 Oct 2026.", "title": "Impact & Partnerships", "url": "https://rfcx.org/impact", "date": null, "written_by": "makers", "quote": "We work closely with NGOs and governmental organizations, including WWF, IUCN, Conservation International, the Jane Goodall Institute and the United Nations, to strengthen on-the-ground protection" }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging. The Straits Times, 11 Feb 2021.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "If the system hears a chainsaw, it sends an alert via an app to community patrols, who can check on the ground for logging." }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). 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global program", "developer_country": "United States", "deployer_country": "Indonesia" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Real-time acoustic detection of illegal logging and poaching sounds so that rangers or community patrols can respond; the reported benefit is the intended one." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2021-02-11", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://projects.propublica.org/nonprofits/search?q=rainforest+connection; https://dishut.sumbarprov.go.id/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Acoustic sensors with mobile-network or satellite uplink placed in forest canopy", "Cloud AI classifier trained on chainsaw and other threat sounds", "Community patrols or rangers able to reach alert locations", "Parallel government enforcement crackdown in the reported area" ], "must_hold": [ "Network coverage or satellite link at the sensor site", "Patrol capacity to respond to alerts on the ground", "Low false-alert rate so responders keep acting", "Independent evaluation to separate the sensor effect from enforcement" ], "echoes": [ { "setting": "Tembé indigenous territory, Brazil, Guardian units used by indigenous rangers against illegal logging (RFCx guardian page)", "outcome": "unknown", "source": "https://rfcx.org/guardian" }, { "setting": "Vancouver shipping lanes (whales) and a Greek national park (gunshots), per the 2021 Reuters report", "outcome": "unknown", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Acoustic sensors with mobile-network or satellite uplink placed in forest canopy", "Cloud AI classifier trained on chainsaw and other threat sounds", "Community patrols or rangers able to reach alert locations", "Parallel government enforcement crackdown in the reported area" ], "must_hold": [ "Network coverage or satellite link at the sensor site", "Patrol capacity to respond to alerts on the ground", "Low false-alert rate so responders keep acting", "Independent evaluation to separate the sensor effect from enforcement" ], "echoes": [ { "setting": "Tembé indigenous territory, Brazil, Guardian units used by indigenous rangers against illegal logging (RFCx guardian page)", "outcome": "unknown", "source": "https://rfcx.org/guardian" }, { "setting": "Vancouver shipping lanes (whales) and a Greek national park (gunshots), per the 2021 Reuters report", "outcome": "unknown", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://projects.propublica.org/nonprofits/search?q=rainforest+connection; https://dishut.sumbarprov.go.id/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Forest protection and anti-poaching", "segment": "Non-profit conservation technology with community and government partners" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Rainforest Connection (n.d.). Real-Time Threat Detection (Guardian). rfcx.org, fetched 20 Oct 2026.", "title": "Real-Time Threat Detection", "url": "https://rfcx.org/guardian", "written_by": "makers", "quote": "An agent nearby receives a real-time alert of the detected threat via our app, enabling rapid response before any damage can occur." }, { "kind": "press release", "citation": "Rainforest Connection (n.d.). Impact & Partnerships. rfcx.org, fetched 20 Oct 2026.", "title": "Impact & Partnerships", "url": "https://rfcx.org/impact", "written_by": "makers", "quote": "We work closely with NGOs and governmental organizations, including WWF, IUCN, Conservation International, the Jane Goodall Institute and the United Nations, to strengthen on-the-ground protection" }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging. The Straits Times, 11 Feb 2021.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "If the system hears a chainsaw, it sends an alert via an app to community patrols, who can check on the ground for logging." }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Same report, patroller statement.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging (patroller quote)", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "(Logging) has totally stopped - people are afraid of coming to this area" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0356-source-0001", "AIBD-0356-source-0002", "AIBD-0356-source-0003", "AIBD-0356-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Rainforest Connection (n.d.). Real-Time Threat Detection (Guardian). rfcx.org, fetched 20 Oct 2026.", "title": "Real-Time Threat Detection", "url": "https://rfcx.org/guardian", "written_by": "makers", "quote": "An agent nearby receives a real-time alert of the detected threat via our app, enabling rapid response before any damage can occur." }, { "kind": "press release", "citation": "Rainforest Connection (n.d.). Impact & Partnerships. rfcx.org, fetched 20 Oct 2026.", "title": "Impact & Partnerships", "url": "https://rfcx.org/impact", "written_by": "makers", "quote": "We work closely with NGOs and governmental organizations, including WWF, IUCN, Conservation International, the Jane Goodall Institute and the United Nations, to strengthen on-the-ground protection" }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging. The Straits Times, 11 Feb 2021.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "If the system hears a chainsaw, it sends an alert via an app to community patrols, who can check on the ground for logging." }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Same report, patroller statement.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging (patroller quote)", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "(Logging) has totally stopped - people are afraid of coming to this area" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "RFCx Guardian sensors detect chainsaws with AI and alert patrols; a 2021 Reuters report from Solok, West Sumatra quotes a patroller that logging stopped. No dated intervention is documented.", "account": "Rainforest Connection (RFCx), a US non-profit founded by Topher White, deploys solar-powered “Guardian” acoustic sensors. In its own words, they use “sound & artificial intelligence” to track chainsaws, vehicle engines, hunting dogs and gunshots. They send real-time alerts to a mobile app so that “an agent nearby” can respond. RFCx states it has protected over 726,000 (impact page) or 750,000 (guardian page) hectares across 8,662+ projects in 137 countries. The only non-vendor account read is a Thomson Reuters Foundation report published by The Straits Times on 11 February 2021. It is from Solok regency, West Sumatra, Indonesia, where 27 Guardian sensors had been installed more than a year earlier. Audio streams are analyzed by AI trained to pick out chainsaw sounds, and an alert goes to community patrols. Patroller Jasrialdi said logging “has totally stopped” in the area. The head of West Sumatra's forestry department, Yozarwardi Usama Putra, said he would like to expand the “early-warning system”. The same report attributes the decline in logging to a wider enforcement crackdown as well. It says RFCx planned to collect data for peer review to prove the system curbs logging. No park authority, ministry or court document records a specific alert leading to an interception, so no event is recorded." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The system classified recorded sounds and alerted responders to possible logging." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "Guardian G1 used repurposed smartphones in 2015–2019. Later deployments used G2 from 2020 and G3 from 2026." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Guardian G1 (2015-2019, repurposed smartphones), G2 (2020-2025), G3 (2026-present) per rfcx.org" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Rainforest Connection Guardian bioacoustic monitoring with cloud AI sound classification and app alerts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Rainforest Connection (RFCx)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Rainforest Connection's Guardian acoustic AI sends chainsaw alerts to community patrols; logging in West Sumatra may have stopped as a result", "headline": "RFCx Guardian alerts may have stopped logging in Solok" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "1692d66dd5159d9e6b4caecbeb60c423c7f3bd62e6050b1ae6009910c1d99a4f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Rainforest Connection (RFCx), a US non-profit founded by Topher White, deploys solar-powered “Guardian” acoustic sensors. In its own words, they use “sound & artificial intelligence” to track chainsaws, vehicle engines, hunting dogs and gunshots. They send real-time alerts to a mobile app so that “an agent nearby” can respond. RFCx states it has protected over 726,000 (impact page) or 750,000 (guardian page) hectares across 8,662+ projects in 137 countries. The only non-vendor account read is a Thomson Reuters Foundation report published by The Straits Times on 11 February 2021. It is from Solok regency, West Sumatra, Indonesia, where 27 Guardian sensors had been installed more than a year earlier. Audio streams are analyzed by AI trained to pick out chainsaw sounds, and an alert goes to community patrols. Patroller Jasrialdi said logging “has totally stopped” in the area. The head of West Sumatra's forestry department, Yozarwardi Usama Putra, said he would like to expand the “early-warning system”. The same report attributes the decline in logging to a wider enforcement crackdown as well. It says RFCx planned to collect data for peer review to prove the system curbs logging. No park authority, ministry or court document records a specific alert leading to an interception, so no event is recorded.", "replication": { "produced_by": [ "Acoustic sensors with mobile-network or satellite uplink placed in forest canopy", "Cloud AI classifier trained on chainsaw and other threat sounds", "Community patrols or rangers able to reach alert locations", "Parallel government enforcement crackdown in the reported area" ], "must_hold": [ "Network coverage or satellite link at the sensor site", "Patrol capacity to respond to alerts on the ground", "Low false-alert rate so responders keep acting", "Independent evaluation to separate the sensor effect from enforcement" ], "echoes": [ { "setting": "Tembé indigenous territory, Brazil, Guardian units used by indigenous rangers against illegal logging (RFCx guardian page)", "outcome": "unknown", "source": "https://rfcx.org/guardian" }, { "setting": "Vancouver shipping lanes (whales) and a Greek national park (gunshots), per the 2021 Reuters report", "outcome": "unknown", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0320", "case_url": "records/AIBD-0320.html", "reason": "The developer is headquartered in United States; the case names Canada.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Canada" }, { "label": "Case location", "value": "Nunavut, Canada" } ], "filters": { "origin": "United States", "destination": [ "Canada" ] }, "record": { "id": "AIBD-0320", "title": "Microsoft Translator adds Inuktitut (2021), Inuinnaqtun and romanized Inuktitut (2022) and Inuktitut text-to-speech (2024) with the Government of Nunavut", "summary": "Neural MT for Inuktut dialects built with the Government of Nunavut and volunteer speakers; Nunatsiaq News and VICE report launches and translation errors; no measured user outcome documented.", "date_observed": "2021-01-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Microsoft added Inuktitut text translation to Microsoft Translator, Office and Translator for Bing on 27 January 2021. The date is from Arctic Portal, relaying the Microsoft Translator blog. Microsoft worked with the Government of Nunavut for language data; Inuktitut speakers volunteered to validate and test translations. VICE (18 February 2021) reported Nunavut language officials finding the output “riddled with mistakes”. Solomon Awa said: “Sometimes it's close, but sometimes it gave the opposite meaning, or one that was misleading”. Languages commissioner Karliin Aariak called it “a great tool to be like a first step”. Retired Université Laval researcher Louis-Jacques Dorais called it a “tour de force” despite a wrong test sentence. Nunatsiaq News (28 February 2022) reported Inuinnaqtun and romanized Inuktitut added during Uqausirmut Quviasuutiqarniq. Fewer than 600 speak Inuinnaqtun fluently, per the Kitikmeot Heritage Society. Microsoft's own feature (1 February 2022) says “Text translation is based on neural machine translation models”. It says the Government of Nunavut “turned to the community to help build and test the language models”. It lists intended uses (unilingual patients communicating with doctors, younger people with elders, emails in Office) without data. Nunatsiaq News (9 December 2024) reported the 5 December launch of Inuktitut text-to-speech voices “Taqqiq” and “Siqiniq”. They were built from thousands of government documents, hired speakers' recordings and Bible Society and Nunavut Inuit Heritage Trust audio. The report noted presenters' accounts of the translator confusing “condo and condom, and prime rib and prime minister”. Voice contributor Leah Ayaruaq McKay said she plans to use it to help her husband learn Inuktitut. Government of Nunavut web pages were not available and are not among the sources. No cited source measures use, accuracy or an outcome for any user.", "category": "general benefit", "unexpected": false, "purpose": "Built to translate text between Inuktut dialects and other languages (and, from 2024, to read Inuktitut aloud). This lets residents, learners and government users read and write in Inuktut. The launches are that intended use.", "system": "Microsoft Translator Inuktitut, Inuinnaqtun and romanized Inuktitut text translation (neural machine translation) and Inuktitut text-to-speech", "model_version": null, "developer": "Microsoft (Azure Cognitive Services Translator and Speech) with the Government of Nunavut and community volunteers, including the Kitikmeot Heritage Society for Inuinnaqtun", "deployer": "Government of Nunavut; public users of Microsoft Translator, Office and Bing", "location": "Nunavut, Canada", "domain": "Language technology / Indigenous language access", "segment": "Public translation tools for Inuktut dialects", "benefit_type": "Increased Inclusion", "taxonomy_note": "The good is an Indigenous official language made usable in mainstream software, with no measured outcome and reported translation errors.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Users are described (a translator who found errors, a voice contributor planning to use it for her husband's learning). No measured benefit for any person is documented." }, { "group": "organizational", "depth": null, "reason": "The Government of Nunavut's and Microsoft's stated uses (healthcare communication, Office email, compliance with language law) are expectations without reported data." }, { "group": "societal", "depth": null, "reason": "Language-vitality effects are asserted by officials. VICE notes Inuktut use fell from 72 to 65 percent between 2001 and 2016, without any link to the tool." } ], "ai_role": { "status": "assessed", "statement": "Microsoft states the translation is neural machine translation trained on Government of Nunavut data and validated by volunteer speakers. Independent reporting records both the launches and specific wrong translations (opposite meanings in 2021; “condo and condom” confusions recounted in 2024). Translators said the output needs a fluent reviewer. No cited source reports a user outcome, usage figure or accuracy measurement. What the system contributed beyond availability is undocumented." }, "evidence": [ { "kind": "news report", "citation": "Arctic Portal (2021). Inuit language now available in Microsoft Translator! 1 February 2021 (summarises the Microsoft Translator blog of 27 January 2021).", "title": "Inuit language now available in Microsoft Translator!", "url": "https://arcticportal.org/news-events/news/2489-inuit-language-is-now-available-in-microsoft-translator", "date": "2021-02-01", "written_by": "independent", "quote": "On 27 January 2021 Microsoft announced that they added Inuktitut text translation to their Translator app, Office and Translator for Bing." }, { "kind": "news report", "citation": "Cavanagh M (2021). New Tech Can Finally Translate Inuit Language-Sort Of. VICE, 18 February 2021.", "title": "New Tech Can Finally Translate Inuit Language-Sort Of", "url": "https://www.vice.com/en/article/tech-microsoft-translator-inuit-language-inuktitut/", "date": "2021-02-18", "written_by": "independent", "quote": "Sometimes it's close, but sometimes it gave the opposite meaning, or one that was misleading," }, { "kind": "news report", "citation": "Nunatsiaq News (2022). Inuinnaqtun and romanized Inuktitut added to Microsoft Translator. 28 February 2022.", "title": "Inuinnaqtun and romanized Inuktitut added to Microsoft Translator", "url": "https://nunatsiaq.com/stories/article/inuinnaqtun-and-romanized-inuktitut-added-to-microsoft-translator/", "date": "2022-02-28", "written_by": "independent", "quote": "Inuinnaqtun and romanized Inuktitut have been added to Microsoft's translation services as a part of the Nunavut government's annual celebration of the Inuktut languages." }, { "kind": "press release", "citation": "Microsoft Canada (2022). Government of Nunavut preserving endangered Inuit languages and culture with the help of artificial intelligence and Microsoft. Microsoft News Center Canada, 1 February 2022.", "title": "Government of Nunavut preserving endangered Inuit languages and culture with the help of artificial intelligence and Microsoft", "url": "https://news.microsoft.com/en-ca/2022/02/01/government-of-nunavut-preserving-endangered-inuit-languages-and-culture-with-the-help-of-artificial-intelligence-and-microsoft/", "date": "2022-02-01", "written_by": "makers", "quote": "Text translation is based on neural machine translation models." }, { "kind": "news report", "citation": "Pelletier J (2024). 'My name is Taqqiq, I can read text': Microsoft Translator adds Inuktitut text-to-speech. Nunatsiaq News, 9 December 2024.", "title": "'My name is Taqqiq, I can read text': Microsoft Translator adds Inuktitut text-to-speech", "url": "https://nunatsiaq.com/stories/article/my-name-is-taqqiq-i-can-read-text-microsoft-translator-adds-inuktitut-text-to-speech/", "date": "2024-12-09", "written_by": "independent", "quote": "Several presenters explained the artificial intelligence mixed up words like condo and condom, and prime rib and prime minister." } ], "replication": { "produced_by": [ "Language data supplied by the Government of Nunavut and recordings by hired and volunteer speakers", "Volunteer fluent speakers validating and testing translations before launch" ], "must_hold": [ "Output is checked by a fluent speaker; Nunavut translators and the languages commissioner describe it as a first step needing review", "Dialect differences across the Arctic are tolerated by users of a standardized model" ], "echoes": [ { "date": "2022-02-28", "setting": "Inuinnaqtun and romanized Inuktitut added with updated Inuktitut models", "model_version": null, "outcome": "unknown", "source": "https://nunatsiaq.com/stories/article/inuinnaqtun-and-romanized-inuktitut-added-to-microsoft-translator/" }, { "date": "2024-12-05", "setting": "Inuktitut text-to-speech voices launched in Iqaluit", "model_version": null, "outcome": "unknown", "source": "https://nunatsiaq.com/stories/article/my-name-is-taqqiq-i-can-read-text-microsoft-translator-adds-inuktitut-text-to-speech/" } ] }, "paired_incident": null, "headline": "Microsoft Translator adds Inuktitut and Inuinnaqtun with Nunavut", "metric": null, "comparator": null, "later": { "status": "scaled", "date": "2024-12-05", "note": "After the January 2021 launch, Inuinnaqtun and romanized Inuktitut were added in February 2022 and Inuktitut text-to-speech voices launched on 5 December 2024, while presenters still reported word confusions.", "quote": "Microsoft Canada and the Government of Nunavut announced Dec. 5 that Inuktitut text-to-speech is available now on Microsoft Translator.", "url": "https://nunatsiaq.com/stories/article/my-name-is-taqqiq-i-can-read-text-microsoft-translator-adds-inuktitut-text-to-speech/" }, "value": null, "developer_country": "United States", "deployer_country": "Canada", "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://nunatsiaq.com/stories/article/my-name-is-taqqiq-i-can-read-text-microsoft-translator-adds-inuktitut-text-to-speech/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Inclusion 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/Microsoft; https://en.wikipedia.org/wiki/Nunavut", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports and the makers' press release. 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It says the Government of Nunavut “turned to the community to help build and test the language models”. It lists intended uses (unilingual patients communicating with doctors, younger people with elders, emails in Office) without data. Nunatsiaq News (9 December 2024) reported the 5 December launch of Inuktitut text-to-speech voices “Taqqiq” and “Siqiniq”. They were built from thousands of government documents, hired speakers' recordings and Bible Society and Nunavut Inuit Heritage Trust audio. The report noted presenters' accounts of the translator confusing “condo and condom, and prime rib and prime minister”. Voice contributor Leah Ayaruaq McKay said she plans to use it to help her husband learn Inuktitut. Government of Nunavut web pages were not available and are not among the sources. 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Inuit language now available in Microsoft Translator! 1 February 2021 (summarises the Microsoft Translator blog of 27 January 2021).", "title": "Inuit language now available in Microsoft Translator!", "url": "https://arcticportal.org/news-events/news/2489-inuit-language-is-now-available-in-microsoft-translator", "date": "2021-02-01", "written_by": "independent", "quote": "On 27 January 2021 Microsoft announced that they added Inuktitut text translation to their Translator app, Office and Translator for Bing." }, { "kind": "news report", "citation": "Cavanagh M (2021). New Tech Can Finally Translate Inuit Language-Sort Of. VICE, 18 February 2021.", "title": "New Tech Can Finally Translate Inuit Language-Sort Of", "url": "https://www.vice.com/en/article/tech-microsoft-translator-inuit-language-inuktitut/", "date": "2021-02-18", "written_by": "independent", "quote": "Sometimes it's close, but sometimes it gave the opposite meaning, or one that was misleading," }, { "kind": "news report", "citation": "Nunatsiaq News (2022). Inuinnaqtun and romanized Inuktitut added to Microsoft Translator. 28 February 2022.", "title": "Inuinnaqtun and romanized Inuktitut added to Microsoft Translator", "url": "https://nunatsiaq.com/stories/article/inuinnaqtun-and-romanized-inuktitut-added-to-microsoft-translator/", "date": "2022-02-28", "written_by": "independent", "quote": "Inuinnaqtun and romanized Inuktitut have been added to Microsoft's translation services as a part of the Nunavut government's annual celebration of the Inuktut languages." }, { "kind": "press release", "citation": "Microsoft Canada (2022). Government of Nunavut preserving endangered Inuit languages and culture with the help of artificial intelligence and Microsoft. Microsoft News Center Canada, 1 February 2022.", "title": "Government of Nunavut preserving endangered Inuit languages and culture with the help of artificial intelligence and Microsoft", "url": "https://news.microsoft.com/en-ca/2022/02/01/government-of-nunavut-preserving-endangered-inuit-languages-and-culture-with-the-help-of-artificial-intelligence-and-microsoft/", "date": "2022-02-01", "written_by": "makers", "quote": "Text translation is based on neural machine translation models." }, { "kind": "news report", "citation": "Pelletier J (2024). 'My name is Taqqiq, I can read text': Microsoft Translator adds Inuktitut text-to-speech. Nunatsiaq News, 9 December 2024.", "title": "'My name is Taqqiq, I can read text': Microsoft Translator adds Inuktitut text-to-speech", "url": "https://nunatsiaq.com/stories/article/my-name-is-taqqiq-i-can-read-text-microsoft-translator-adds-inuktitut-text-to-speech/", "date": "2024-12-09", "written_by": "independent", "quote": "Several presenters explained the artificial intelligence mixed up words like condo and condom, and prime rib and prime minister." } ] } }, "provenance": { "kind": "legacy_record", "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-0320-source-0001", "AIBD-0320-source-0002", "AIBD-0320-source-0003", "AIBD-0320-source-0004", "AIBD-0320-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": "Arctic Portal (2021). Inuit language now available in Microsoft Translator! 1 February 2021 (summarises the Microsoft Translator blog of 27 January 2021).", "title": "Inuit language now available in Microsoft Translator!", "url": "https://arcticportal.org/news-events/news/2489-inuit-language-is-now-available-in-microsoft-translator", "date": "2021-02-01", "written_by": "independent", "quote": "On 27 January 2021 Microsoft announced that they added Inuktitut text translation to their Translator app, Office and Translator for Bing." }, { "kind": "news report", "citation": "Cavanagh M (2021). New Tech Can Finally Translate Inuit Language-Sort Of. VICE, 18 February 2021.", "title": "New Tech Can Finally Translate Inuit Language-Sort Of", "url": "https://www.vice.com/en/article/tech-microsoft-translator-inuit-language-inuktitut/", "date": "2021-02-18", "written_by": "independent", "quote": "Sometimes it's close, but sometimes it gave the opposite meaning, or one that was misleading," }, { "kind": "news report", "citation": "Nunatsiaq News (2022). Inuinnaqtun and romanized Inuktitut added to Microsoft Translator. 28 February 2022.", "title": "Inuinnaqtun and romanized Inuktitut added to Microsoft Translator", "url": "https://nunatsiaq.com/stories/article/inuinnaqtun-and-romanized-inuktitut-added-to-microsoft-translator/", "date": "2022-02-28", "written_by": "independent", "quote": "Inuinnaqtun and romanized Inuktitut have been added to Microsoft's translation services as a part of the Nunavut government's annual celebration of the Inuktut languages." }, { "kind": "press release", "citation": "Microsoft Canada (2022). Government of Nunavut preserving endangered Inuit languages and culture with the help of artificial intelligence and Microsoft. Microsoft News Center Canada, 1 February 2022.", "title": "Government of Nunavut preserving endangered Inuit languages and culture with the help of artificial intelligence and Microsoft", "url": "https://news.microsoft.com/en-ca/2022/02/01/government-of-nunavut-preserving-endangered-inuit-languages-and-culture-with-the-help-of-artificial-intelligence-and-microsoft/", "date": "2022-02-01", "written_by": "makers", "quote": "Text translation is based on neural machine translation models." }, { "kind": "news report", "citation": "Pelletier J (2024). 'My name is Taqqiq, I can read text': Microsoft Translator adds Inuktitut text-to-speech. Nunatsiaq News, 9 December 2024.", "title": "'My name is Taqqiq, I can read text': Microsoft Translator adds Inuktitut text-to-speech", "url": "https://nunatsiaq.com/stories/article/my-name-is-taqqiq-i-can-read-text-microsoft-translator-adds-inuktitut-text-to-speech/", "date": "2024-12-09", "written_by": "independent", "quote": "Several presenters explained the artificial intelligence mixed up words like condo and condom, and prime rib and prime minister." } ] } }, "provenance": { "kind": "legacy_record", "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": "Neural MT for Inuktut dialects built with the Government of Nunavut and volunteer speakers; Nunatsiaq News and VICE report launches and translation errors; no measured user outcome documented.", "account": "Microsoft added Inuktitut text translation to Microsoft Translator, Office and Translator for Bing on 27 January 2021. The date is from Arctic Portal, relaying the Microsoft Translator blog. Microsoft worked with the Government of Nunavut for language data; Inuktitut speakers volunteered to validate and test translations. VICE (18 February 2021) reported Nunavut language officials finding the output “riddled with mistakes”. Solomon Awa said: “Sometimes it's close, but sometimes it gave the opposite meaning, or one that was misleading”. Languages commissioner Karliin Aariak called it “a great tool to be like a first step”. Retired Université Laval researcher Louis-Jacques Dorais called it a “tour de force” despite a wrong test sentence. Nunatsiaq News (28 February 2022) reported Inuinnaqtun and romanized Inuktitut added during Uqausirmut Quviasuutiqarniq. Fewer than 600 speak Inuinnaqtun fluently, per the Kitikmeot Heritage Society. Microsoft's own feature (1 February 2022) says “Text translation is based on neural machine translation models”. It says the Government of Nunavut “turned to the community to help build and test the language models”. It lists intended uses (unilingual patients communicating with doctors, younger people with elders, emails in Office) without data. Nunatsiaq News (9 December 2024) reported the 5 December launch of Inuktitut text-to-speech voices “Taqqiq” and “Siqiniq”. They were built from thousands of government documents, hired speakers' recordings and Bible Society and Nunavut Inuit Heritage Trust audio. The report noted presenters' accounts of the translator confusing “condo and condom, and prime rib and prime minister”. Voice contributor Leah Ayaruaq McKay said she plans to use it to help her husband learn Inuktitut. Government of Nunavut web pages were not available and are not among the sources. No cited source measures use, accuracy or an outcome for any user." } }, "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": "Microsoft Translator Inuktitut, Inuinnaqtun and romanized Inuktitut text translation (neural machine translation) and Inuktitut text-to-speech" } }, "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": "Microsoft (Azure Cognitive Services Translator and Speech) with the Government of Nunavut and community volunteers, including the Kitikmeot Heritage Society for Inuinnaqtun" } }, "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": "Microsoft Translator adds Inuktitut (2021), Inuinnaqtun and romanized Inuktitut (2022) and Inuktitut text-to-speech (2024) with the Government of Nunavut", "headline": "Microsoft Translator adds Inuktitut and Inuinnaqtun with Nunavut" } }, "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": [ "824cfe9e0b6314ed4d617ecd8af812aec238077922fffe86888be49f238e040d" ], "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": "Microsoft added Inuktitut text translation to Microsoft Translator, Office and Translator for Bing on 27 January 2021. The date is from Arctic Portal, relaying the Microsoft Translator blog. Microsoft worked with the Government of Nunavut for language data; Inuktitut speakers volunteered to validate and test translations. VICE (18 February 2021) reported Nunavut language officials finding the output “riddled with mistakes”. Solomon Awa said: “Sometimes it's close, but sometimes it gave the opposite meaning, or one that was misleading”. Languages commissioner Karliin Aariak called it “a great tool to be like a first step”. Retired Université Laval researcher Louis-Jacques Dorais called it a “tour de force” despite a wrong test sentence. Nunatsiaq News (28 February 2022) reported Inuinnaqtun and romanized Inuktitut added during Uqausirmut Quviasuutiqarniq. Fewer than 600 speak Inuinnaqtun fluently, per the Kitikmeot Heritage Society. Microsoft's own feature (1 February 2022) says “Text translation is based on neural machine translation models”. It says the Government of Nunavut “turned to the community to help build and test the language models”. It lists intended uses (unilingual patients communicating with doctors, younger people with elders, emails in Office) without data. Nunatsiaq News (9 December 2024) reported the 5 December launch of Inuktitut text-to-speech voices “Taqqiq” and “Siqiniq”. They were built from thousands of government documents, hired speakers' recordings and Bible Society and Nunavut Inuit Heritage Trust audio. The report noted presenters' accounts of the translator confusing “condo and condom, and prime rib and prime minister”. Voice contributor Leah Ayaruaq McKay said she plans to use it to help her husband learn Inuktitut. Government of Nunavut web pages were not available and are not among the sources. No cited source measures use, accuracy or an outcome for any user.", "replication": { "produced_by": [ "Language data supplied by the Government of Nunavut and recordings by hired and volunteer speakers", "Volunteer fluent speakers validating and testing translations before launch" ], "must_hold": [ "Output is checked by a fluent speaker; Nunavut translators and the languages commissioner describe it as a first step needing review", "Dialect differences across the Arctic are tolerated by users of a standardized model" ], "echoes": [ { "date": "2022-02-28", "setting": "Inuinnaqtun and romanized Inuktitut added with updated Inuktitut models", "outcome": "unknown", "source": "https://nunatsiaq.com/stories/article/inuinnaqtun-and-romanized-inuktitut-added-to-microsoft-translator/" }, { "date": "2024-12-05", "setting": "Inuktitut text-to-speech voices launched in Iqaluit", "outcome": "unknown", "source": "https://nunatsiaq.com/stories/article/my-name-is-taqqiq-i-can-read-text-microsoft-translator-adds-inuktitut-text-to-speech/" } ] } } }, "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-0262", "case_url": "records/AIBD-0262.html", "reason": "The developer is headquartered in United States; the case names Canada.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "United States, Canada" }, { "label": "Case location", "value": "United States (Nimbus, Boston; Takeda Development Center Americas, Cambridge, MA); phase 2b at 55 sites in the US and Canada; phase 3 sites unverified" } ], "filters": { "origin": "United States", "destination": [ "United States", "Canada" ] }, "record": { "id": "AIBD-0262", "title": "Zasocitinib, computationally designed by Nimbus with Schrodinger's platform, beats deucravacitinib in phase 3; FDA priority review", "summary": "Takeda reports 36.5% complete skin clearance with zasocitinib versus 13.9% with deucravacitinib at week 16. The contribution of AI to discovery is not separately measured.", "date_observed": "2026-09-30", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Takeda reported that zasocitinib produced complete skin clearance in 36.5% of its LATITUDE Atlas group, versus 13.9% with deucravacitinib, at week 16. The September 30, 2026 announcement gives analysis groups of 301 and 303 participants. The sponsor’s registration lists 606 enrolled participants and has no posted results. The earlier Nimbus-funded phase 2b trial treated 259 participants and found a dose response against placebo, including complete clearance in 33% at the 30 mg dose. Nimbus and Schrödinger developed the molecule with computational structure-based design. Schrödinger describes free-energy calculations and virtual screening, while a Takeda scientist separately describes the approach as AI-assisted. These accounts do not isolate the effect of AI from the wider discovery process. Takeda reported that the FDA granted priority review and that the EMA accepted its application in September 2026. These were application milestones, not approvals. Takeda’s February 2023 closing announcement states an approximately $4 billion upfront acquisition payment, with additional contingent milestones. That transaction is not a valuation of the benefit attributable to AI.", "category": "benefit created", "unexpected": false, "purpose": "Design a selective oral allosteric TYK2 inhibitor for plaque psoriasis and other immune-mediated diseases", "system": "Schrodinger physics-based computational drug-design platform with machine learning, used by Nimbus Therapeutics to discover NDI-034858 (TAK-279, zasocitinib)", "model_version": "FEP+", "developer": "Schrodinger (platform); Nimbus Therapeutics (compound)", "deployer": "Takeda (clinical development, LATITUDE phase 3 program)", "location": "United States (Nimbus, Boston; Takeda Development Center Americas, Cambridge, MA); phase 2b at 55 sites in the US and Canada; phase 3 sites unverified", "domain": "Health; drug discovery", "segment": "Adults with moderate-to-severe plaque psoriasis", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Trial participants achieved complete skin clearance at higher rates. 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Additional sources contribute the specific evidence or limitations described in claim_lineage.", "claim_lineage": [ { "claim": "Phase 3 complete-clearance comparison", "source_chain": [ { "url": "https://www.takeda.com/newsroom/newsreleases/2026/zasocitinib-superior-efficacy/", "locator": "Late-breaking LATITUDE Atlas results; reference to Stein Gold et al., EADV 2026 LB-312, session D3T01.3", "role": "Original sponsor results announcement", "access_status": "Full relevant text reviewed", "published_on": "2026-09-30" }, { "url": "https://eadv.org/congress/", "locator": "EADV 2026, Vienna, September 30–October 3; abstract LB-312", "role": "Underlying conference presentation", "access_status": "The complete original presentation/poster was not obtained" } ], "limitations": [ "301 plus 303 reported analysis participants differs from 606 enrolled; the reviewed announcement does not resolve that difference", "No independent replication or AI attribution experiment" ], "source_ref": "AIBD-0262-source-0001" }, { "claim": "Phase 2b clinical efficacy and trial funding", "source_chain": [ { "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11339701/", "locator": "Results, Figure 2, Funding/Support, and Role of the Funder/Sponsor", "role": "Original trial paper, full article reviewed through Europe PMC XML", "access_status": "Full relevant text reviewed", "published_on": "2024-08-21" } ], "limitations": [ "Nimbus funded and participated in the study; 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complete ACS paper and supplements were inaccessible", "published_on": "2023-07-10" } ], "limitations": [ "The 4,000-molecule/46-synthesis example in the developer case concerns the older JH1 program, not TAK-279", "An independent causal contribution of AI was not quantified" ], "source_ref": "AIBD-0262-source-0004" }, { "claim": "Regulatory application milestones", "source_chain": [ { "url": "https://www.takeda.com/newsroom/newsreleases/2026/fda-priority-review-zasocitinib-psoriasis/", "locator": "FDA priority review, Q1 2027 target, and EMA application acceptance", "role": "Original sponsor announcement", "access_status": "Full relevant text reviewed", "published_on": "2026-09-14" } ], "limitations": [ "Underlying agency correspondence was not public in the reviewed material; no drug approval established" ], "source_ref": "AIBD-0262-source-0006" }, { "claim": "Acquisition consideration", "source_chain": [ { "url": "https://www.takeda.com/newsroom/newsreleases/2023/takeda-completes-acquisition-of-nimbus-therapeutics-tyk2-program-subsidiary/", "locator": "Closing announcement and upfront/contingent consideration", "role": "Original buyer statement", "access_status": "Full relevant text reviewed", "published_on": "2023-02-08" }, { "url": "https://www.nimbustx.com/2022/12/13/takeda-to-acquire-nimbus-therapeutics-highly-selective-allosteric-tyk2-inhibitor-to-address-multiple-immune-mediated-diseases/", "locator": "Transaction terms", "role": "Earlier original seller announcement", "access_status": "Full relevant text reviewed", "published_on": "2022-12-13" } ], "limitations": [ "Approximately $4 billion, subject to closing adjustments; contingent payments are separate", "An acquisition transfer does not measure AI-created social or economic value" ], "source_ref": "AIBD-0262-source-0007" } ], "limitations": [ "Phase 3 presentation, discovery-paper full text, and phase 2b supplementary files remain unavailable" ] } }, "provenance": { "kind": "source_review", "basis": "Replaced derivative trial, regulatory, and transaction reporting with the original sponsor documents. Corrected the phase 2b trial’s interested authorship and online publication date.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0262-source-0001", "AIBD-0262-source-0002", "AIBD-0262-source-0003", "AIBD-0262-source-0004", "AIBD-0262-source-0005", "AIBD-0262-source-0006", "AIBD-0262-source-0007" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The phase 3 presentation, full discovery paper, and phase 2b supplements were inaccessible.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Takeda reports 36.5% complete skin clearance with zasocitinib versus 13.9% with deucravacitinib at week 16. The contribution of AI to discovery is not separately measured.", "account": "Takeda reported that zasocitinib produced complete skin clearance in 36.5% of its LATITUDE Atlas group, versus 13.9% with deucravacitinib, at week 16. The September 30, 2026 announcement gives analysis groups of 301 and 303 participants. The sponsor’s registration lists 606 enrolled participants and has no posted results. The earlier Nimbus-funded phase 2b trial treated 259 participants and found a dose response against placebo, including complete clearance in 33% at the 30 mg dose. Nimbus and Schrödinger developed the molecule with computational structure-based design. Schrödinger describes free-energy calculations and virtual screening, while a Takeda scientist separately describes the approach as AI-assisted. These accounts do not isolate the effect of AI from the wider discovery process. Takeda reported that the FDA granted priority review and that the EMA accepted its application in September 2026. These were application milestones, not approvals. Takeda’s February 2023 closing announcement states an approximately $4 billion upfront acquisition payment, with additional contingent milestones. That transaction is not a valuation of the benefit attributable to AI." } }, "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-0262-source-0001", "AIBD-0262-source-0002", "AIBD-0262-source-0003", "AIBD-0262-source-0004", "AIBD-0262-source-0005", "AIBD-0262-source-0006", "AIBD-0262-source-0007" ], "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": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "FEP+" } }, "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": "Schrodinger physics-based computational drug-design platform with machine learning, used by Nimbus Therapeutics to discover NDI-034858 (TAK-279, zasocitinib)" } }, "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": "Schrodinger (platform); Nimbus Therapeutics (compound)" } }, "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": "Zasocitinib, computationally designed by Nimbus with Schrodinger's platform, beats deucravacitinib in phase 3; FDA priority review", "headline": "Nimbus's zasocitinib beats deucravacitinib in phase 3 trial" } }, "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": [ "8369c6c4cda4d1bd2c6dd8c07efdcac14d89ef7bf6cb027a2dbdc322d0061e9e" ], "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": "Takeda reported that zasocitinib produced complete skin clearance in 36.5% of its LATITUDE Atlas group, versus 13.9% with deucravacitinib, at week 16. The September 30, 2026 announcement gives analysis groups of 301 and 303 participants. The sponsor’s registration lists 606 enrolled participants and has no posted results. The earlier Nimbus-funded phase 2b trial treated 259 participants and found a dose response against placebo, including complete clearance in 33% at the 30 mg dose. Nimbus and Schrödinger developed the molecule with computational structure-based design. Schrödinger describes free-energy calculations and virtual screening, while a Takeda scientist separately describes the approach as AI-assisted. These accounts do not isolate the effect of AI from the wider discovery process. Takeda reported that the FDA granted priority review and that the EMA accepted its application in September 2026. These were application milestones, not approvals. Takeda’s February 2023 closing announcement states an approximately $4 billion upfront acquisition payment, with additional contingent milestones. That transaction is not a valuation of the benefit attributable to AI.", "replication": { "produced_by": [ "Computational structure-based design and laboratory testing of a selective allosteric TYK2 inhibitor", "Sponsor-led randomized clinical trials against placebo and deucravacitinib" ], "must_hold": [ "Phase 3 efficacy and safety confirmed in regulatory review", "Independent documentation of the computational platform's contribution to the molecule" ], "echoes": [ { "date": "2024-08-21", "setting": "Phase 2b RCT, 47 US and 8 Canadian centers, 259 treated patients", "outcome": "recurred", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11339701/" }, { "date": "2026-09-14", "setting": "FDA acceptance of the NDA under priority review", "outcome": "unknown", "source": "https://www.takeda.com/newsroom/newsreleases/2026/fda-priority-review-zasocitinib-psoriasis/" } ] } } }, "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-0262-source-0001", "AIBD-0262-source-0002", "AIBD-0262-source-0003", "AIBD-0262-source-0004", "AIBD-0262-source-0005", "AIBD-0262-source-0006", "AIBD-0262-source-0007" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 39, "reviewed": 4, "record_mapped": 35, "not_assessed": 52, "complete": false, "by_state": { "value_present": 39, "not_reported": 0, "unresolved": 0, "not_assessed": 52, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0088", "case_url": "records/AIBD-0088.html", "reason": "The developer is headquartered in United Kingdom; the case names Jamaica.", "details": [ { "label": "Developer headquarters", "value": "United Kingdom" }, { "label": "Case jurisdictions", "value": "Jamaica" }, { "label": "Case location", "value": "New Hope, Jamaica" } ], "filters": { "origin": "United Kingdom", "destination": [ "Jamaica" ] }, "record": { "id": "AIBD-0088", "title": "NHC blended Google DeepMind's AI ensemble into official Hurricane Melissa forecasts before its Category 5 landfall in Jamaica", "summary": "NHC blended Google DeepMind's AI ensemble into official Melissa forecasts from its first advisory on 21 October 2025; Cat 5 landfall in Jamaica 28 October 2025.", "date_observed": "2025-10-21", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The US National Hurricane Center (NHC) blended Google DeepMind's experimental AI hurricane model (ensemble mean GDMI) into official forecasts for Tropical Storm Melissa. It did so from its first advisory, 21 October 2025. Discussion 1 placed the official track between the DeepMind ensemble mean and the consensus aid HCCA. Discussion 2 described the track as a blend of the DeepMind mean, HCCA and TVCA. Discussion 8 (23 October) moved the track toward a blend of the DeepMind mean and HCCA. Discussion 10 blended the HCCA and GDMI aids. It set an intensity forecast in best agreement with GDMI after all 50 DeepMind members showed a major hurricane. On 28 October 2025 Melissa made landfall in southwestern Jamaica near New Hope with 185 mph winds and 892 mb pressure. IFRC reported 45 deaths and about 1.6 million people affected. It relayed a World Bank/IDB estimate of US$8.8 billion in physical damage. Its CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time.", "category": "protective response", "unexpected": false, "purpose": "Experimental AI tropical cyclone forecast model providing ensemble track and intensity guidance to NHC forecasters.", "system": "Google DeepMind AI hurricane forecast model, ensemble mean designated GDMI in NHC guidance", "model_version": null, "developer": "DeepMind", "deployer": "NOAA National Hurricane Center", "location": "New Hope, Jamaica", "domain": "Weather forecasting / tropical cyclones", "segment": "Public-sector meteorological service", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The AI model's contribution to official forecasts is read as forecast accuracy, with no error figure for this storm.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No cited source attributes an individual-level outcome to the AI guidance. Impacts are reported only at population level." }, { "group": "organizational", "depth": 2, "reason": "NHC's official track and intensity forecasts were materially shaped by GDMI (Discussions 8, 10, 13, 14). NHC's Science Operations Officer called the AI guidance very valuable for Melissa." }, { "group": "societal", "depth": 3, "reason": "Official forecasts for a Category 5 landfall affecting about 1.6 million people. A CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time. The review says its early actions were informed by forecast risks (NHC is not named). Material to many, but no counterfactual that the AI changed outcomes." } ], "ai_role": { "status": "assessed", "statement": "NHC forecasters state in Discussions 8, 10, 12, 13 and 14 that official track and intensity forecasts were blended or aligned with Google DeepMind's ensemble. The official forecast remains a human synthesis of several aids. So the AI contribution to outcomes in Jamaica is not isolated." }, "evidence": [ { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 1, 1100 AM EDT Tue Oct 21 2025, forecasters Cangialosi/Bucci. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 1", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "The NHC official track forecast lies between the Google DeepMind ensemble mean track and the correct consensus aid, HCCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 2, 500 PM EDT Tue Oct 21 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 2", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "This prediction is a little left of the previous forecast and remains a blend of the latest Google DeepMind ensemble mean, the corrected consensus aid HCCA, and simple consensus TVCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 8, 500 AM EDT Thu Oct 23 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 8", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.008.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "The NHC track forecast is shifted a little to the right of the previous one, trending toward a blend of the latest Google DeepMind ensemble mean and HCCA solution." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 10, 500 PM EDT Thu Oct 23 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 10", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "the latest NHC track was only nudged slightly southward from this morning, blending the reliable HCCA and GDMI aids" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 13, 1100 AM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 13", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.013.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "Every single Google DeepMind member shows the system become a Category 4 hurricane or higher" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 14, 500 PM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 14", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "The NHC intensity forecast is on the high side of the guidance envelope, and closest too the Google DeepMind ensemble mean (GDMI)." }, { "kind": "report", "citation": "National Hurricane Center (2025). Hurricane Melissa Tropical Cyclone Update, 100 PM EDT Tue Oct 28 2025. NOAA/NWS.", "title": "Hurricane Melissa Tropical Cyclone Update (landfall)", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.update.10281700.shtml", "date": "2025-10-28", "written_by": "deployer", "quote": "CATEGORY 5 MELISSA MAKES LANDFALL IN JAMAICA...ONE OF THE MOST POWERFUL HURRICANE LANDFALLS ON RECORD IN THE ATLANTIC BASIN" }, { "kind": "press release", "citation": "National Weather Service (n.d.). AI in Hurricane Forecasting at the National Hurricane Center: A Q&A with NHC Science Operation Officer Wallace Hogsett. NOAA/NWS News Around NOAA.", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "date": null, "written_by": "deployer", "quote": "The AI models honed in very early on the likely track and intensity and provided very valuable guidance to complement our traditional NWP guidance." }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025" }, { "kind": "report", "citation": "Kelly, Larry A., José Álamo, and Aidan D. Mahoney. Hurricane Melissa (AL132025), 21–31 October 2025. National Hurricane Center Tropical Cyclone Report. Revised May 19, 2026, originally February 25, 2026. 16–17, Tables 5b and 6b.", "title": "Hurricane Melissa (AL132025): Tropical Cyclone Report", "url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "date": "2026-02-25", "written_by": "deployer", "quote": "The NHC OFCL forecasts were comparable to GDMI through 96 h, but performed slightly better than GDMI at 120 h." } ], "replication": { "produced_by": [ "Experimental Google DeepMind ensemble guidance available in real time to NHC forecasters", "Forecasters choosing to blend GDMI with consensus aids (HCCA) in official track and intensity forecasts", "A slow-moving, rapidly intensifying hurricane where models disagreed on track relative to Jamaica" ], "must_hold": [ "AI ensemble output delivered on time and verified before operational use, as NHC describes", "Human forecasters retain the final official forecast and integrate conventional NWP guidance", "Forecast accuracy can be assessed against the observed track and intensity. NHC’s Tropical Cyclone Report, revised 19 May 2026, provides that comparison for Melissa" ], "echoes": [ { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 Atlantic season track forecasts", "model_version": null, "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 eastern North Pacific track forecasts (GDMI beat official and consensus aids at 48–120 h)", "model_version": null, "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "severity": null, "group": "societal", "basis": "Google-built hazard early-warning system underestimated the Kahramanmaras earthquake and failed to warn millions; same developer family and public-warning task as DeepMind's Melissa forecasts." }, "headline": "NHC blends DeepMind's ensemble into Hurricane Melissa forecasts", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2026-03-30", "note": "NHC's 2025 verification report, five months after landfall, records low official track errors for Melissa and the DeepMind ensemble mean outperforming the official Atlantic track forecast at 12 to 72 hours.", "quote": "It has been seen in multiple cases during the past few years that the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025.", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Set the date observed to 21 October 2025 and reworded the summary and account because Discussions 1 and 2 show the Google DeepMind ensemble mean shaping the official track from the first advisory, added those two discussions as evidence, corrected the Discussion 14 title to “Tropical Storm Melissa” as its header reads, attributed the US$8.8 billion damage figure to the World Bank/IDB estimate relayed by IFRC, and tied the DREF's early actions to “forecast risks” as the review says rather than to NHC.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://www.nhc.noaa.gov/aboutintro.shtml", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added NHC’s revised Hurricane Melissa report, replaced the obsolete statement that it was unpublished, and separated forecast verification from humanitarian background.", "source": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: nine reports from the deployer and the deployer's press release. 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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. 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The report compares forecasts at lead times through 120 hours." }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Hurricane Melissa in the Atlantic, 21–31 October 2025. The report compares forecasts at lead times through 120 hours." } }, "evaluations[].uncertainty_limitations": { "state": "value_present", "value": { "kind": "text", "text": "The sources establish that forecasters used the guidance. They do not establish that it changed protective actions, reduced losses, or prevented deaths." }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The sources establish that forecasters used the guidance. They do not establish that it changed protective actions, reduced losses, or prevented deaths." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "group": "societal", "basis": "Google-built hazard early-warning system underestimated the Kahramanmaras earthquake and failed to warn millions; same developer family and public-warning task as DeepMind's Melissa forecasts." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2026-03-30", "note": "NHC's 2025 verification report, five months after landfall, records low official track errors for Melissa and the DeepMind ensemble mean outperforming the official Atlantic track forecast at 12 to 72 hours.", "quote": "It has been seen in multiple cases during the past few years that the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025.", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "NHC published its Tropical Cyclone Report on 25 February 2026 and revised it on 19 May. The report evaluates the forecasts against the storm’s observed track and intensity." } }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0088" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "National Hurricane Center forecasters. People in Melissa’s path were the intended beneficiaries of the forecasts." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "National Hurricane Center forecasters. People in Melissa’s path were the intended beneficiaries of the forecasts." } }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "display": { "answer": "No count of people who benefited from the AI guidance is established." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The AI model's contribution to official forecasts is read as forecast accuracy, with no error figure for this storm." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The US National Hurricane Center (NHC) blended Google DeepMind's experimental AI hurricane model (ensemble mean GDMI) into official forecasts for Tropical Storm Melissa. It did so from its first advisory, 21 October 2025. Discussion 1 placed the official track between the DeepMind ensemble mean and the consensus aid HCCA. Discussion 2 described the track as a blend of the DeepMind mean, HCCA and TVCA. Discussion 8 (23 October) moved the track toward a blend of the DeepMind mean and HCCA. Discussion 10 blended the HCCA and GDMI aids. It set an intensity forecast in best agreement with GDMI after all 50 DeepMind members showed a major hurricane. On 28 October 2025 Melissa made landfall in southwestern Jamaica near New Hope with 185 mph winds and 892 mb pressure. IFRC reported 45 deaths and about 1.6 million people affected. It relayed a World Bank/IDB estimate of US$8.8 billion in physical damage. Its CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time.", "benefit_type": "Improved Task Accuracy" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "National Hurricane Center forecasters used Google DeepMind’s guidance in their official forecasts for Hurricane Melissa." } }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No cited source attributes an individual-level outcome to the AI guidance. Impacts are reported only at population level." }, { "group": "organizational", "depth": 2, "reason": "NHC's official track and intensity forecasts were materially shaped by GDMI (Discussions 8, 10, 13, 14). NHC's Science Operations Officer called the AI guidance very valuable for Melissa." }, { "group": "societal", "depth": 3, "reason": "Official forecasts for a Category 5 landfall affecting about 1.6 million people. A CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time. The review says its early actions were informed by forecast risks (NHC is not named). Material to many, but no counterfactual that the AI changed outcomes." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Experimental AI tropical cyclone forecast model providing ensemble track and intensity guidance to NHC forecasters." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "New Hope, Jamaica", "developer_country": "United Kingdom", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Experimental AI tropical cyclone forecast model providing ensemble track and intensity guidance to NHC forecasters." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-10-21", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Set the date observed to 21 October 2025 and reworded the summary and account because Discussions 1 and 2 show the Google DeepMind ensemble mean shaping the official track from the first advisory, added those two discussions as evidence, corrected the Discussion 14 title to “Tropical Storm Melissa” as its header reads, attributed the US$8.8 billion damage figure to the World Bank/IDB estimate relayed by IFRC, and tied the DREF's early actions to “forecast risks” as the review says rather than to NHC.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://www.nhc.noaa.gov/aboutintro.shtml", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added NHC’s revised Hurricane Melissa report, replaced the obsolete statement that it was unpublished, and separated forecast verification from humanitarian background.", "source": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "editor": "AIBD" } ] } }, "provenance": { "kind": "curatorial", "basis": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Experimental Google DeepMind ensemble guidance available in real time to NHC forecasters", "Forecasters choosing to blend GDMI with consensus aids (HCCA) in official track and intensity forecasts", "A slow-moving, rapidly intensifying hurricane where models disagreed on track relative to Jamaica" ], "must_hold": [ "AI ensemble output delivered on time and verified before operational use, as NHC describes", "Human forecasters retain the final official forecast and integrate conventional NWP guidance", "Forecast accuracy can be assessed against the observed track and intensity. NHC’s Tropical Cyclone Report, revised 19 May 2026, provides that comparison for Melissa" ], "echoes": [ { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 Atlantic season track forecasts", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 eastern North Pacific track forecasts (GDMI beat official and consensus aids at 48–120 h)", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Experimental Google DeepMind ensemble guidance available in real time to NHC forecasters", "Forecasters choosing to blend GDMI with consensus aids (HCCA) in official track and intensity forecasts", "A slow-moving, rapidly intensifying hurricane where models disagreed on track relative to Jamaica" ], "must_hold": [ "AI ensemble output delivered on time and verified before operational use, as NHC describes", "Human forecasters retain the final official forecast and integrate conventional NWP guidance", "Forecast accuracy can be assessed against the observed track and intensity. NHC’s Tropical Cyclone Report, revised 19 May 2026, provides that comparison for Melissa" ], "echoes": [ { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 Atlantic season track forecasts", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 eastern North Pacific track forecasts (GDMI beat official and consensus aids at 48–120 h)", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Forecasters need timely model output, checks before operational use, and conventional guidance for comparison. Later observations are needed to assess forecast accuracy." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Set the date observed to 21 October 2025 and reworded the summary and account because Discussions 1 and 2 show the Google DeepMind ensemble mean shaping the official track from the first advisory, added those two discussions as evidence, corrected the Discussion 14 title to “Tropical Storm Melissa” as its header reads, attributed the US$8.8 billion damage figure to the World Bank/IDB estimate relayed by IFRC, and tied the DREF's early actions to “forecast risks” as the review says rather than to NHC.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://www.nhc.noaa.gov/aboutintro.shtml", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added NHC’s revised Hurricane Melissa report, replaced the obsolete statement that it was unpublished, and separated forecast verification from humanitarian background.", "source": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "editor": "AIBD" } ] } }, "provenance": { "kind": "curatorial", "basis": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Weather forecasting / tropical cyclones", "segment": "Public-sector meteorological service" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "versions_and_access": [ { "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "access": "Full original page read on October 25, 2026. Q&A 7 discusses Melissa, and Q&A 2 describes NHC’s development partnership with DeepMind.", "metadata": { "DC.date.created": "June 24th 2026 4:15 PM" }, "date_treatment": "Do not infer a publication date from the URL or treat creation metadata as original publication. The original March 30, 2026 verification report already links to this URL on printed p. 7. The future-date suspicion is resolved and should not appear on the website." }, { "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "access": "Full original PDF downloaded successfully (HTTP 200) on October 25, 2026. Title page dated March 30, 2026. Printed p. 7 links to the NHC interview, and the report provides seasonal forecast comparisons.", "supersedes_access_note": "The earlier direct-PDF 403 limitation is now resolved for this document." }, { "source_ref": "AIBD-0088-source-0010", "source_url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "publication_date": "2026-02-25", "version_reviewed_date": "2026-05-19", "status": "Full original PDF downloaded successfully (HTTP 200) and relevant text read on October 25, 2026. HTTP Last-Modified: May 19, 2026.", "version_note": "The May 19 title-page note says that this version revised fatality totals and damage estimates based on updated government information. Preserve February 25 as the original report date and May 19 as the reviewed revision." } ], "limitations": [ "The separately cited IFRC operational review and update retain their earlier access limits. The November 2025 IFRC operational strategy was accessible." ] } }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The NHC interview, 2025 verification report, and revised Melissa report were accessible in full. Earlier access limits remain attached to the individual IFRC reports." } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 1, 1100 AM EDT Tue Oct 21 2025, forecasters Cangialosi/Bucci. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 1", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "The NHC official track forecast lies between the Google DeepMind ensemble mean track and the correct consensus aid, HCCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 2, 500 PM EDT Tue Oct 21 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 2", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "This prediction is a little left of the previous forecast and remains a blend of the latest Google DeepMind ensemble mean, the corrected consensus aid HCCA, and simple consensus TVCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 8, 500 AM EDT Thu Oct 23 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 8", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.008.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "The NHC track forecast is shifted a little to the right of the previous one, trending toward a blend of the latest Google DeepMind ensemble mean and HCCA solution." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 10, 500 PM EDT Thu Oct 23 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 10", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "the latest NHC track was only nudged slightly southward from this morning, blending the reliable HCCA and GDMI aids" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 13, 1100 AM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 13", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.013.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "Every single Google DeepMind member shows the system become a Category 4 hurricane or higher" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 14, 500 PM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 14", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "The NHC intensity forecast is on the high side of the guidance envelope, and closest too the Google DeepMind ensemble mean (GDMI)." }, { "kind": "report", "citation": "National Hurricane Center (2025). Hurricane Melissa Tropical Cyclone Update, 100 PM EDT Tue Oct 28 2025. NOAA/NWS.", "title": "Hurricane Melissa Tropical Cyclone Update (landfall)", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.update.10281700.shtml", "date": "2025-10-28", "written_by": "deployer", "quote": "CATEGORY 5 MELISSA MAKES LANDFALL IN JAMAICA...ONE OF THE MOST POWERFUL HURRICANE LANDFALLS ON RECORD IN THE ATLANTIC BASIN" }, { "kind": "press release", "citation": "National Weather Service (n.d.). AI in Hurricane Forecasting at the National Hurricane Center: A Q&A with NHC Science Operation Officer Wallace Hogsett. NOAA/NWS News Around NOAA.", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "written_by": "deployer", "quote": "The AI models honed in very early on the likely track and intensity and provided very valuable guidance to complement our traditional NWP guidance." }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025" }, { "kind": "report", "citation": "Kelly, Larry A., José Álamo, and Aidan D. Mahoney. Hurricane Melissa (AL132025), 21–31 October 2025. National Hurricane Center Tropical Cyclone Report. Revised May 19, 2026, originally February 25, 2026. 16–17, Tables 5b and 6b.", "title": "Hurricane Melissa (AL132025): Tropical Cyclone Report", "url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "date": "2026-02-25", "written_by": "deployer", "quote": "The NHC OFCL forecasts were comparable to GDMI through 96 h, but performed slightly better than GDMI at 120 h." } ] } }, "provenance": { "kind": "source_review", "basis": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009", "AIBD-0088-source-0010" ], "role": "Retained sources" }, "provenance": { "kind": "curatorial", "basis": "Synchronized source identifiers.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 1, 1100 AM EDT Tue Oct 21 2025, forecasters Cangialosi/Bucci. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 1", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "The NHC official track forecast lies between the Google DeepMind ensemble mean track and the correct consensus aid, HCCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 2, 500 PM EDT Tue Oct 21 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 2", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "This prediction is a little left of the previous forecast and remains a blend of the latest Google DeepMind ensemble mean, the corrected consensus aid HCCA, and simple consensus TVCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 8, 500 AM EDT Thu Oct 23 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 8", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.008.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "The NHC track forecast is shifted a little to the right of the previous one, trending toward a blend of the latest Google DeepMind ensemble mean and HCCA solution." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 10, 500 PM EDT Thu Oct 23 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 10", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "the latest NHC track was only nudged slightly southward from this morning, blending the reliable HCCA and GDMI aids" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 13, 1100 AM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 13", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.013.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "Every single Google DeepMind member shows the system become a Category 4 hurricane or higher" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 14, 500 PM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 14", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "The NHC intensity forecast is on the high side of the guidance envelope, and closest too the Google DeepMind ensemble mean (GDMI)." }, { "kind": "report", "citation": "National Hurricane Center (2025). Hurricane Melissa Tropical Cyclone Update, 100 PM EDT Tue Oct 28 2025. NOAA/NWS.", "title": "Hurricane Melissa Tropical Cyclone Update (landfall)", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.update.10281700.shtml", "date": "2025-10-28", "written_by": "deployer", "quote": "CATEGORY 5 MELISSA MAKES LANDFALL IN JAMAICA...ONE OF THE MOST POWERFUL HURRICANE LANDFALLS ON RECORD IN THE ATLANTIC BASIN" }, { "kind": "press release", "citation": "National Weather Service (n.d.). AI in Hurricane Forecasting at the National Hurricane Center: A Q&A with NHC Science Operation Officer Wallace Hogsett. NOAA/NWS News Around NOAA.", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "written_by": "deployer", "quote": "The AI models honed in very early on the likely track and intensity and provided very valuable guidance to complement our traditional NWP guidance." }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025" }, { "kind": "report", "citation": "Kelly, Larry A., José Álamo, and Aidan D. Mahoney. Hurricane Melissa (AL132025), 21–31 October 2025. National Hurricane Center Tropical Cyclone Report. Revised May 19, 2026, originally February 25, 2026. 16–17, Tables 5b and 6b.", "title": "Hurricane Melissa (AL132025): Tropical Cyclone Report", "url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "date": "2026-02-25", "written_by": "deployer", "quote": "The NHC OFCL forecasts were comparable to GDMI through 96 h, but performed slightly better than GDMI at 120 h." } ] } }, "provenance": { "kind": "source_review", "basis": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "origins": [ { "source_refs": [ "AIBD-0088-source-0001" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "title": "Tropical Storm Melissa Discussion Number 1", "relationship": "Original operational advisory establishing that DeepMind ensemble guidance contributed to the official forecast from the first advisory." }, { "source_refs": [ "AIBD-0088-source-0002" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml", "title": "Tropical Storm Melissa Discussion Number 2", "relationship": "Original operational advisory recording the blend of DeepMind and other guidance." }, { "source_refs": [ "AIBD-0088-source-0003" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.008.shtml", "title": "Tropical Storm Melissa Discussion Number 8", "relationship": "Original operational advisory recording a later adjustment toward DeepMind and HCCA guidance." }, { "source_refs": [ "AIBD-0088-source-0004" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml", "title": "Tropical Storm Melissa Discussion Number 10", "relationship": "Original operational advisory recording track and intensity guidance. It does not isolate downstream public benefits." }, { "source_refs": [ "AIBD-0088-source-0005" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.013.shtml", "title": "Tropical Storm Melissa Discussion Number 13", "relationship": "Original operational advisory recording the ensemble’s Category 4-or-higher signal." }, { "source_refs": [ "AIBD-0088-source-0006" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml", "title": "Tropical Storm Melissa Discussion Number 14", "relationship": "Original operational advisory recording the intensity forecast’s agreement with GDMI." }, { "source_refs": [ "AIBD-0088-source-0007" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.update.10281700.shtml", "title": "Hurricane Melissa Tropical Cyclone Update (landfall)", "relationship": "Original landfall update establishing storm context, not the AI system’s effect on public outcomes." }, { "source_refs": [ "AIBD-0088-source-0008" ], "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "relationship": "Original interview with NHC’s Science Operations Officer. NHC deployed the model and partnered in its development; this is not an independent evaluation." }, { "source_refs": [ "AIBD-0088-source-0009" ], "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "title": "NHC Forecast Verification Report, 2025 Hurricane Season", "relationship": "Original seasonal verification by NHC and a cooperating researcher. It evaluates forecasting performance, but it is not an independent evaluation of AI-attributable humanitarian outcomes." }, { "url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "title": "Hurricane Melissa (AL132025): Tropical Cyclone Report", "relationship": "Original retrospective verification by the agency that used the guidance. The report compares track and intensity forecasts for Melissa and explains how GDMI and regional models contributed to forecasters’ confidence. It does not isolate the AI model’s effect on humanitarian outcomes.", "locator": "Printed pp. 16–17, Track Forecast and Intensity Forecast, Tables 5b and 6b.", "original_publication_date": "2026-02-25", "version_reviewed_date": "2026-05-19", "version_note": "The May 19 title-page note says that this version revised fatality totals and damage estimates based on updated government information. Preserve February 25 as the original report date and May 19 as the reviewed revision.", "access": "Full original PDF downloaded successfully (HTTP 200) and relevant text read on October 25, 2026. HTTP Last-Modified: May 19, 2026.", "source_refs": [ "AIBD-0088-source-0010" ] } ], "related_material": [ { "url": "https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "title": "Jamaica - Hurricane Melissa Imminent DREF (MDRJM005) Operational Review Report", "published_on": "2026-08-07", "relationship": "Humanitarian and storm context preserved from the existing case. It does not establish an AI-attributable outcome and is not counted as independent AI-benefit evaluation.", "preserved_evidence": { "kind": "report", "citation": "IFRC (2026). Jamaica - Hurricane Melissa Imminent DREF (MDRJM005), DREF Operational Review Report, April - May 2026. ReliefWeb, 7 August 2026.", "title": "Jamaica - Hurricane Melissa Imminent DREF (MDRJM005) Operational Review Report", "url": "https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "date": "2026-08-07", "written_by": "independent", "quote": "Rapid approval and pre-landfall mobilisation, enabled by prior training, pre-positioned stock, and strong logistics systems, allowed JRC to respond quickly." }, "access": "The original ReliefWeb URL returned HTTP 403 during this narrow review. Existing citations and quotations are preserved; no new full-text verification is claimed." }, { "url": "https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3", "title": "Jamaica | Hurricane Melissa DREF MDRJM005 Operation update #3", "published_on": "2026-07-07", "relationship": "Humanitarian and storm context preserved from the existing case. It does not establish an AI-attributable outcome and is not counted as independent AI-benefit evaluation.", "preserved_evidence": { "kind": "report", "citation": "IFRC (2026). Jamaica | Hurricane Melissa DREF No. MDRJM005 Operation update #3. ReliefWeb, 7 July 2026.", "title": "Jamaica | Hurricane Melissa DREF MDRJM005 Operation update #3", "url": "https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3", "date": "2026-07-07", "written_by": "independent", "quote": "winds of nearly 300 km (185 miles) per hour" }, "access": "The original ReliefWeb URL returned HTTP 403 during this narrow review. Existing citations and quotations are preserved; no new full-text verification is claimed." }, { "url": "https://go-api.ifrc.org/api/downloadfile/93656/MDRJM005OS", "title": "Jamaica | Hurricane Melissa: Emergency Appeal Operational Strategy (MDRJM005)", "date": "2025-11-11", "locator": "p. 2, timeline; p. 3, situation overview; p. 10, targeting", "finding": "The timeline records the CHF80,000 allocation on October 23, 2025. The targeting section attributes the estimated 1.6 million people affected to analysis of UNOSAT wind forecasts and demographic data.", "relationship": "Original IFRC background on anticipatory aid and exposure. This report does not attribute those actions or the affected-population estimate to Google DeepMind.", "access": "Full original PDF downloaded successfully (HTTP 200) and relevant sections read on October 25, 2026. The IFRC emergency page dates this operational strategy November 11, 2025.", "recommendation": "Keep as related background rather than independent evidence of AI-created benefit. Preserve existing IFRC related material and its separate access status." } ], "claim_lineage": [ { "claim": "The first official Melissa forecast used DeepMind ensemble guidance.", "source_ref": "AIBD-0088-source-0001", "locator": "Track discussion, paragraph beginning “The system was moving”", "access": "Original advisory text read." }, { "claim": "GDMI performed well in NHC’s 2025 verification.", "source_ref": "AIBD-0088-source-0009", "locator": "Abstract and Atlantic track-verification discussion", "access": "Full original PDF inspected on 25 October 2026.", "limitation": "Seasonal model accuracy is not a measure of AI-attributable lives saved or of the AI contribution to each humanitarian action." } ], "access_audit": [ { "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "access": "Original advisory text read through the indexed official page after the direct request returned HTTP 403." }, { "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "access": "Full original page read on October 25, 2026. Q&A 7 discusses Melissa, and Q&A 2 describes NHC’s development partnership with DeepMind.", "access_history": [ { "access": "Original NHC interview text read; it identifies NHC’s development partnership with Google DeepMind.", "reviewed_on": "2026-10-24" } ], "reviewed_on": "2026-10-25" }, { "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "access": "Full original PDF downloaded successfully (HTTP 200) on October 25, 2026. Title page dated March 30, 2026. Printed p. 7 links to the NHC interview, and the report provides seasonal forecast comparisons.", "access_history": [ { "access": "Indexed original report abstract and relevant text read; direct PDF request returned HTTP 403.", "reviewed_on": "2026-10-24" } ], "reviewed_on": "2026-10-25" } ], "limitations": [ "NHC is the operator and a development partner, so its verification is primary but not an independent evaluation of its own use of AI.", "Track and intensity comparisons do not measure lives saved, damage avoided, or whether IFRC acted earlier because of GDMI.", "The seasonal comparison must not be substituted for a Melissa-only result." ], "ordering_basis": "Keep the contemporaneous NHC account of AI use first, followed by retained operational documentation and later verification. Humanitarian background remains available in the source notes.", "lead_source_ref": "AIBD-0088-source-0001", "retained_source_order": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009", "AIBD-0088-source-0010" ], "scope": "Operational use and retrospective verification of forecasts, with humanitarian material retained separately as background.", "reviewed_on": "2026-10-25" } }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "NHC’s operational advisories document its use of DeepMind guidance. Its later verification reports assess forecast accuracy. IFRC reports provide humanitarian context without isolating AI’s effects." } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "NHC blended Google DeepMind's AI ensemble into official Melissa forecasts from its first advisory on 21 October 2025; Cat 5 landfall in Jamaica 28 October 2025.", "account": "The US National Hurricane Center (NHC) blended Google DeepMind's experimental AI hurricane model (ensemble mean GDMI) into official forecasts for Tropical Storm Melissa. It did so from its first advisory, 21 October 2025. Discussion 1 placed the official track between the DeepMind ensemble mean and the consensus aid HCCA. Discussion 2 described the track as a blend of the DeepMind mean, HCCA and TVCA. Discussion 8 (23 October) moved the track toward a blend of the DeepMind mean and HCCA. Discussion 10 blended the HCCA and GDMI aids. It set an intensity forecast in best agreement with GDMI after all 50 DeepMind members showed a major hurricane. On 28 October 2025 Melissa made landfall in southwestern Jamaica near New Hope with 185 mph winds and 892 mb pressure. IFRC reported 45 deaths and about 1.6 million people affected. It relayed a World Bank/IDB estimate of US$8.8 billion in physical damage. Its CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Google DeepMind AI hurricane forecast model, ensemble mean designated GDMI in NHC guidance" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "DeepMind" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "NHC blended Google DeepMind's AI ensemble into official Hurricane Melissa forecasts before its Category 5 landfall in Jamaica", "headline": "NHC blends DeepMind's ensemble into Hurricane Melissa forecasts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7494545dd8aeba975dc7387c52bc23ab138bc6a416517d50ebb709bf803d7efa" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Recomputed the content fingerprint after the source-attribution correction.", "record_fields": [ "evidence", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The US National Hurricane Center (NHC) blended Google DeepMind's experimental AI hurricane model (ensemble mean GDMI) into official forecasts for Tropical Storm Melissa. It did so from its first advisory, 21 October 2025. Discussion 1 placed the official track between the DeepMind ensemble mean and the consensus aid HCCA. Discussion 2 described the track as a blend of the DeepMind mean, HCCA and TVCA. Discussion 8 (23 October) moved the track toward a blend of the DeepMind mean and HCCA. Discussion 10 blended the HCCA and GDMI aids. It set an intensity forecast in best agreement with GDMI after all 50 DeepMind members showed a major hurricane. On 28 October 2025 Melissa made landfall in southwestern Jamaica near New Hope with 185 mph winds and 892 mb pressure. IFRC reported 45 deaths and about 1.6 million people affected. It relayed a World Bank/IDB estimate of US$8.8 billion in physical damage. Its CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time.", "replication": { "produced_by": [ "Experimental Google DeepMind ensemble guidance available in real time to NHC forecasters", "Forecasters choosing to blend GDMI with consensus aids (HCCA) in official track and intensity forecasts", "A slow-moving, rapidly intensifying hurricane where models disagreed on track relative to Jamaica" ], "must_hold": [ "AI ensemble output delivered on time and verified before operational use, as NHC describes", "Human forecasters retain the final official forecast and integrate conventional NWP guidance", "Forecast accuracy can be assessed against the observed track and intensity. NHC’s Tropical Cyclone Report, revised 19 May 2026, provides that comparison for Melissa" ], "echoes": [ { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 Atlantic season track forecasts", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 eastern North Pacific track forecasts (GDMI beat official and consensus aids at 48–120 h)", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Forecasters received the experimental DeepMind ensemble in real time and combined it with conventional forecasts. They retained responsibility for the official track and intensity forecasts." } } }, "summary": { "total": 91, "addressed": 91, "known": 43, "reviewed": 11, "record_mapped": 32, "not_assessed": 48, "complete": false, "by_state": { "value_present": 43, "not_reported": 0, "unresolved": 0, "not_assessed": 48, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0091", "case_url": "records/AIBD-0091.html", "reason": "The developer is headquartered in France; the case names Algeria.", "details": [ { "label": "Developer headquarters", "value": "France" }, { "label": "Case jurisdictions", "value": "Algeria" }, { "label": "Case location", "value": "Hassi Messaoud, Algeria" } ], "filters": { "origin": "France", "destination": [ "Algeria" ] }, "record": { "id": "AIBD-0091", "title": "UNEP MARS alert led to repair of a decades-old methane source at Hassi Messaoud, Algeria, verified by satellite from 15 October 2024", "summary": "MARS alert led an Algerian operator to fix an unlit flare at Hassi Messaoud; satellites show no emissions since 15 October 2024.", "date_observed": "2024-10-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "UNEP's Methane Alert and Response System (MARS) combines data from more than 30 satellite instruments with expert analysis and AI models. It confirmed mitigation of a methane source at Hassi Messaoud, Algeria, in October 2024. UNEP's case document was issued in November 2024 per the repository record. The PDF's last-updated field reads 15 October 2024. It records a Landsat 5 detection dated 1 July 1987. It attributes the emissions to an unlit flare caused by a passing valve on the flare line. The operator corrected the valve and restored normal operations. UNEP states no further emissions have been detected at the location since 15 October 2024. UN News reported on 23 July 2026 that the Algerian alert led to the repair of a leak that had persisted for decades.", "category": "benefit created", "unexpected": false, "purpose": "Satellite-based detection and notification of major methane emission events to governments and operators to enable mitigation.", "system": "UNEP International Methane Emissions Observatory, Methane Alert and Response System (MARS)", "model_version": null, "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed operator at Hassi Messaoud (repair)", "location": "Hassi Messaoud, Algeria", "domain": "Earth observation / methane emissions", "segment": "Oil and gas", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No identifiable individuals are documented as affected by the repair." }, { "group": "organizational", "depth": 2, "reason": "The operator fixed a passing valve on a flare line and restored normal operations at one facility, per UNEP's case document." }, { "group": "societal", "depth": 3, "reason": "UN News describes the leak as having persisted for decades before the repair. Material to many, provisional pending independent quantification of the emissions avoided." } ], "ai_role": { "status": "assessed", "statement": "UNEP IMEO both developed and operates MARS (developer and deployer are the same body). UNEP's MARS page describes detection using more than 30 satellite instruments coupled with expertise and advanced AI models. The MARS FAQ says deep learning models monitor high-resolution satellite data for further emissions. The case document's boilerplate says MARS combines 30+ satellite instruments with expert analysis and AI models. None of these states which step in this case was AI-driven. The repair was carried out by the operator. The two case-document items are treated as a public body's official reports rather than vendor material." }, "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2024). Methane Alert and Response System (MARS) Confirmed Mitigation Action - Algeria (DZA_S_012). Repository Date Issued 2024-11 (month only; first day used); the PDF's LAST UPDATED field reads 15 October 2024.", "title": "MARS Confirmed Mitigation Action - Algeria (DZA_S_012)", "url": "https://wedocs.unep.org/bitstreams/5810b829-cf15-4eb0-b509-8dd489884a66/download", "date": "2024-11-01", "written_by": "makers", "quote": "No further emissions have been detected at the location since 15 October 2024." }, { "kind": "case report", "citation": "UNEP IMEO (2024). Methane Alert and Response System (MARS) Confirmed Mitigation Action - Algeria (DZA_S_012), repository record. Date Issued 2024-11 (month only; first day used).", "title": "MARS Confirmed Mitigation Action - Algeria (repository record)", "url": "https://wedocs.unep.org/items/33a43a3a-8a2a-4831-8f18-e09e472623b0", "date": "2024-11-01", "written_by": "makers", "quote": "In October 2024, at Algeria's Hassi Messaoud, the International Methane Emissions Observatory (IMEO) confirmed methane mitigation action through the Methane Alert and Response System (MARS)." }, { "kind": "news report", "citation": "UN News (2026). Satellites can now spot methane leaks in real time - but most go unfixed. 23 July 2026.", "title": "Satellites can now spot methane leaks in real time - but most go unfixed", "url": "https://news.un.org/en/story/2026/07/1168007", "date": "2026-07-23", "written_by": "makers", "quote": "In Algeria, one alert led to the repair of a leak that had persisted for decades" }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "date": null, "written_by": "makers", "quote": "data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models" }, { "kind": "press release", "citation": "UNEP (n.d.). MARS Frequently Asked Questions. UNEP IMEO.", "title": "MARS Frequently Asked Questions", "url": "https://www.unep.org/en/what-we-do/energy/methane/international-methane-emissions-observatory/mars-frequently-asked", "date": null, "written_by": "makers", "quote": "MARS utilizes deep learning models to monitor data from continuously observing high-resolution satellites for future emissions from the region." }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (month only; first day used).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "date": "2026-09-01", "written_by": "makers", "quote": "To date, MARS has enabled and confirmed mitigation of over 40 major methane sources." }, { "kind": "report", "citation": "UNEP IMEO (2025). An Eye on Methane 2025: From Measurement to Momentum, repository abstract. Repository Date Issued 2025-10-03.", "title": "An Eye on Methane 2025: From Measurement to Momentum", "url": "https://wedocs.unep.org/bitstream/handle/20.500.11822/48664/An-Eye-on-Methane-2025.pdf?sequence=1&isAllowed=y", "date": "2025-10-03", "written_by": "makers", "quote": "the Methane Alert and Response System, which integrates satellite data and artificial intelligence to notify governments and companies of large emission events" } ], "replication": { "produced_by": [ "Satellite detection and expert quantification of a large point source, followed by a MARS notification to the government and operator", "An operator able to identify a simple equipment fault (passing valve on a flare line) and correct it", "Follow-up satellite monitoring confirming cessation" ], "must_hold": [ "A responsive focal point or operator. UN News reports only 13 percent of alerts lead to any response", "Verification depends on later satellite coverage. The Kazakhstan case shows emissions can recur after confirmed mitigation" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan (KAZ_S_136, 43.39767°, 52.49505°; no place name given): gas pipeline leak first detected September 2025 (plume imaged by DLR's EnMAP 29 October 2025); operator transferred the gas flow to another pipeline, mitigation reported 26 November 2025; no further emissions detected since 12 March 2026 (document last updated 15 April 2026), but IMEO observed subsequent emissions at the location as of 21 May 2026", "model_version": null, "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2023-12-01", "setting": "Argentina oil and gas facility: UNEP press release says the company fixed the leak after a MARS alert and satellite monitoring confirmed cessation", "model_version": null, "outcome": "recurred", "source": "https://www.unep.org/news-and-stories/press-release/uns-global-methane-tracking-system-set-translate-data-revolution" }, { "date": "2026-04-01", "setting": "Alberta, Canada: trade-press report of UNEP's monthly snapshot describing a 175-tonne-per-week plume fixed by repairing a failed ball valve on pigging equipment (month only; first day used)", "model_version": null, "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] }, "paired_incident": null, "headline": "UNEP MARS alert leads to methane leak repair at Hassi Messaoud", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2026-07-23", "note": "Twenty-one months after the repair, UN News cited the Algerian case as a leak fixed after a MARS alert, while reporting that only 13 per cent of alerts receive a response.", "quote": "In Algeria, one alert led to the repair of a leak that had persisted for decades, delivering climate benefits comparable to removing half a million cars from the road each year.", "url": "https://news.un.org/en/story/2026/07/1168007" }, "value": null, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Rewrote the Kazakhstan echo from the KAZ_S_136 document (no place name, first detection September 2025 with the EnMAP plume imaged 29 October 2025, no further emissions since 12 March 2026 until the 21 May 2026 banner), marked the month-only dates in the case-document and snapshot citations and dated the Eye on Methane item 2025-10-03 from its repository record, and stated that UNEP IMEO both developed and operates MARS so that the two case-document items count as a public body's official reports (marked independent).", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download; https://wedocs.unep.org/bitstreams/5810b829-cf15-4eb0-b509-8dd489884a66/download; https://wedocs.unep.org/items/33a43a3a-8a2a-4831-8f18-e09e472623b0; https://wedocs.unep.org/handle/20.500.11822/48664", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://news.un.org/en/story/2026/07/1168007", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: two case reports from the makers, two reports from the makers, the makers' news report and two press releases from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0091", "catalog_version": "1.0.0", "source_fingerprint": "deb4f3b76031e7146e46b243baa8cbf4c5288f8921f9a2d91da88e1cf8cfbfc7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0091-source-0001", "AIBD-0091-source-0002", "AIBD-0091-source-0003", "AIBD-0091-source-0004", "AIBD-0091-source-0005", "AIBD-0091-source-0006", "AIBD-0091-source-0007" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed operator at Hassi Messaoud (repair)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "UNEP IMEO both developed and operates MARS (developer and deployer are the same body). UNEP's MARS page describes detection using more than 30 satellite instruments coupled with expertise and advanced AI models. The MARS FAQ says deep learning models monitor high-resolution satellite data for further emissions. The case document's boilerplate says MARS combines 30+ satellite instruments with expert analysis and AI models. None of these states which step in this case was AI-driven. The repair was carried out by the operator. The two case-document items are treated as a public body's official reports rather than vendor material." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "UNEP IMEO both developed and operates MARS (developer and deployer are the same body). UNEP's MARS page describes detection using more than 30 satellite instruments coupled with expertise and advanced AI models. The MARS FAQ says deep learning models monitor high-resolution satellite data for further emissions. The case document's boilerplate says MARS combines 30+ satellite instruments with expert analysis and AI models. None of these states which step in this case was AI-driven. The repair was carried out by the operator. The two case-document items are treated as a public body's official reports rather than vendor material." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No identifiable individuals are documented as affected by the repair." }, { "group": "organizational", "depth": 2, "reason": "The operator fixed a passing valve on a flare line and restored normal operations at one facility, per UNEP's case document." }, { "group": "societal", "depth": 3, "reason": "UN News describes the leak as having persisted for decades before the repair. Material to many, provisional pending independent quantification of the emissions avoided." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The makers' claim", "basis": "Sources: two case reports from the makers, two reports from the makers, the makers' news report and two press releases from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No identifiable individuals are documented as affected by the repair." }, { "group": "organizational", "depth": 2, "reason": "The operator fixed a passing valve on a flare line and restored normal operations at one facility, per UNEP's case document." }, { "group": "societal", "depth": 3, "reason": "UN News describes the leak as having persisted for decades before the repair. Material to many, provisional pending independent quantification of the emissions avoided." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].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 individuals are documented as affected by the repair." }, { "group": "organizational", "depth": 2, "reason": "The operator fixed a passing valve on a flare line and restored normal operations at one facility, per UNEP's case document." }, { "group": "societal", "depth": 3, "reason": "UN News describes the leak as having persisted for decades before the repair. 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Repository Date Issued 2025-10-03.", "title": "An Eye on Methane 2025: From Measurement to Momentum", "url": "https://wedocs.unep.org/bitstream/handle/20.500.11822/48664/An-Eye-on-Methane-2025.pdf?sequence=1&isAllowed=y", "date": "2025-10-03", "written_by": "makers", "quote": "the Methane Alert and Response System, which integrates satellite data and artificial intelligence to notify governments and companies of large emission events" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0091-source-0001", "AIBD-0091-source-0002", "AIBD-0091-source-0003", "AIBD-0091-source-0004", "AIBD-0091-source-0005", "AIBD-0091-source-0006", "AIBD-0091-source-0007" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2024). 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Date Issued 2024-11 (month only; first day used).", "title": "MARS Confirmed Mitigation Action - Algeria (repository record)", "url": "https://wedocs.unep.org/items/33a43a3a-8a2a-4831-8f18-e09e472623b0", "date": "2024-11-01", "written_by": "makers", "quote": "In October 2024, at Algeria's Hassi Messaoud, the International Methane Emissions Observatory (IMEO) confirmed methane mitigation action through the Methane Alert and Response System (MARS)." }, { "kind": "news report", "citation": "UN News (2026). Satellites can now spot methane leaks in real time - but most go unfixed. 23 July 2026.", "title": "Satellites can now spot methane leaks in real time - but most go unfixed", "url": "https://news.un.org/en/story/2026/07/1168007", "date": "2026-07-23", "written_by": "makers", "quote": "In Algeria, one alert led to the repair of a leak that had persisted for decades" }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models" }, { "kind": "press release", "citation": "UNEP (n.d.). MARS Frequently Asked Questions. UNEP IMEO.", "title": "MARS Frequently Asked Questions", "url": "https://www.unep.org/en/what-we-do/energy/methane/international-methane-emissions-observatory/mars-frequently-asked", "written_by": "makers", "quote": "MARS utilizes deep learning models to monitor data from continuously observing high-resolution satellites for future emissions from the region." }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (month only; first day used).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "date": "2026-09-01", "written_by": "makers", "quote": "To date, MARS has enabled and confirmed mitigation of over 40 major methane sources." }, { "kind": "report", "citation": "UNEP IMEO (2025). An Eye on Methane 2025: From Measurement to Momentum, repository abstract. Repository Date Issued 2025-10-03.", "title": "An Eye on Methane 2025: From Measurement to Momentum", "url": "https://wedocs.unep.org/bitstream/handle/20.500.11822/48664/An-Eye-on-Methane-2025.pdf?sequence=1&isAllowed=y", "date": "2025-10-03", "written_by": "makers", "quote": "the Methane Alert and Response System, which integrates satellite data and artificial intelligence to notify governments and companies of large emission events" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "MARS alert led an Algerian operator to fix an unlit flare at Hassi Messaoud; satellites show no emissions since 15 October 2024.", "account": "UNEP's Methane Alert and Response System (MARS) combines data from more than 30 satellite instruments with expert analysis and AI models. It confirmed mitigation of a methane source at Hassi Messaoud, Algeria, in October 2024. UNEP's case document was issued in November 2024 per the repository record. The PDF's last-updated field reads 15 October 2024. It records a Landsat 5 detection dated 1 July 1987. It attributes the emissions to an unlit flare caused by a passing valve on the flare line. The operator corrected the valve and restored normal operations. UNEP states no further emissions have been detected at the location since 15 October 2024. UN News reported on 23 July 2026 that the Algerian alert led to the repair of a leak that had persisted for decades." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP International Methane Emissions Observatory, Methane Alert and Response System (MARS)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UNEP MARS alert led to repair of a decades-old methane source at Hassi Messaoud, Algeria, verified by satellite from 15 October 2024", "headline": "UNEP MARS alert leads to methane leak repair at Hassi Messaoud" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "deb4f3b76031e7146e46b243baa8cbf4c5288f8921f9a2d91da88e1cf8cfbfc7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNEP's Methane Alert and Response System (MARS) combines data from more than 30 satellite instruments with expert analysis and AI models. It confirmed mitigation of a methane source at Hassi Messaoud, Algeria, in October 2024. UNEP's case document was issued in November 2024 per the repository record. The PDF's last-updated field reads 15 October 2024. It records a Landsat 5 detection dated 1 July 1987. It attributes the emissions to an unlit flare caused by a passing valve on the flare line. The operator corrected the valve and restored normal operations. UNEP states no further emissions have been detected at the location since 15 October 2024. UN News reported on 23 July 2026 that the Algerian alert led to the repair of a leak that had persisted for decades.", "replication": { "produced_by": [ "Satellite detection and expert quantification of a large point source, followed by a MARS notification to the government and operator", "An operator able to identify a simple equipment fault (passing valve on a flare line) and correct it", "Follow-up satellite monitoring confirming cessation" ], "must_hold": [ "A responsive focal point or operator. UN News reports only 13 percent of alerts lead to any response", "Verification depends on later satellite coverage. The Kazakhstan case shows emissions can recur after confirmed mitigation" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan (KAZ_S_136, 43.39767°, 52.49505°; no place name given): gas pipeline leak first detected September 2025 (plume imaged by DLR's EnMAP 29 October 2025); operator transferred the gas flow to another pipeline, mitigation reported 26 November 2025; no further emissions detected since 12 March 2026 (document last updated 15 April 2026), but IMEO observed subsequent emissions at the location as of 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2023-12-01", "setting": "Argentina oil and gas facility: UNEP press release says the company fixed the leak after a MARS alert and satellite monitoring confirmed cessation", "outcome": "recurred", "source": "https://www.unep.org/news-and-stories/press-release/uns-global-methane-tracking-system-set-translate-data-revolution" }, { "date": "2026-04-01", "setting": "Alberta, Canada: trade-press report of UNEP's monthly snapshot describing a 175-tonne-per-week plume fixed by repairing a failed ball valve on pigging equipment (month only; first day used)", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0093", "case_url": "records/AIBD-0093.html", "reason": "The developer is headquartered in United States; the case names Togo.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Togo" }, { "label": "Case location", "value": "Rural cantons, Togo" } ], "filters": { "origin": "United States", "destination": [ "Togo" ] }, "record": { "id": "AIBD-0093", "title": "Togo's Novissi program targeted rural emergency cash with machine learning on satellite imagery and mobile-phone data", "summary": "ML on satellite imagery and phone metadata set eligibility for Novissi rural cash; 138,531 beneficiaries paid, Nov 2020 to Aug 2021", "date_observed": "2020-11-02", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Government of Togo launched the Novissi emergency cash program on 8 April 2020. On 2 November 2020 it extended it to rural areas using a machine-learning pipeline built with UC Berkeley and IPA researchers. Satellite imagery produced poverty estimates for every canton to rank the poorest. Within eligible cantons a model trained on mobile-phone metadata predicted each subscriber's consumption to decide who was paid. Beneficiaries self-registered by USSD and received mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men). The World Bank and Government of Togo report 138,531 rural beneficiaries from November 2020 to August 2021, funded by GiveDirectly (US$10 million). The Nature evaluation found phone-based targeting reduced exclusion errors by 4–21% relative to geographic alternatives.", "category": "benefit created", "unexpected": false, "purpose": "Prioritize the poorest cantons and the poorest mobile subscribers within them for COVID-19 emergency cash transfers in the absence of a social registry.", "system": "Novissi rural targeting pipeline: satellite-imagery poverty maps for canton ranking plus supervised machine learning on anonymized mobile-phone call detail records for individual eligibility", "model_version": "gradient boosting regressor with Microsoft's LightGBM", "developer": "Innovations for Poverty Action, CEGA and UC Berkeley (Aiken and Blumenstock, UC Berkeley; Bellue, University of Mannheim; Karlan and Udry, Northwestern University) for the Government of Togo", "deployer": "Government of Togo, Ministry of Digital Economy and Transformation, with GiveDirectly", "location": "Rural cantons, Togo", "domain": "Social protection and humanitarian cash transfers", "segment": "Government social assistance", "benefit_type": "Increased Scalability", "taxonomy_note": "The good for beneficiaries is emergency cash, and the type names the program's reach into rural areas with no registry.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "Five mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men) to each of 138,531 rural beneficiaries during COVID-19 restrictions (World Bank study, Table 1)." }, { "group": "organizational", "depth": 2, "reason": "The Government of Togo delivered shock-responsive rural assistance without a social registry. The World Bank documents the model as a reusable case study." }, { "group": "societal", "depth": 3, "reason": "Material to many: 138,531 rural beneficiaries reached, and the Nature evaluation reports exclusion errors 4–21% lower than geographic targeting." } ], "ai_role": { "status": "assessed", "statement": "Decisive for who was paid: canton ranking and individual eligibility were both set by machine-learning predictions, while humans fixed the budget, thresholds and registration rules. The Nature evaluation is co-authored by the team that built the targeting method. The World Bank/Government of Togo case study is independent of the developers." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Aiken, E., Bellue, S., Karlan, D., Udry, C., Blumenstock, J. E. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature 603, 864–870 (the quotation is the Extended Data Fig. 1 caption, panel a, with its reference markers omitted).", "title": "Machine learning and phone data can improve targeting of humanitarian aid", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/", "date": "2022-03-16", "written_by": "makers", "quote": "Satellite imagery of Togo is used to construct micro-estimates of poverty (middle), which are overlayed with population data to produce canton-level estimates of wealth." }, { "kind": "report", "citation": "Lawson, C., Koudeka, M., Cárdenas Martínez, A. L., Alberro Encinas, L. I., George Karippacheril, T. (2023). Novissi Togo: Harnessing Artificial Intelligence to Deliver Shock-Responsive Social Protection. World Bank and Government of Togo, Social Protection and Jobs Discussion Paper No. 2306, September 2023 (month only; first day used).", "title": "Novissi Togo: Harnessing Artificial Intelligence to Deliver Shock-Responsive Social Protection", "url": "https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf", "date": "2023-09-01", "written_by": "deployer", "quote": "Subsequently, the program was extended on November 2, 2020, to rural areas with a high prevalence of poverty (henceforth referred to as model 2)." }, { "kind": "press release", "citation": "GiveDirectly (2021). Innovations in Togo and the future of cash distribution. GiveDirectly, The Future of Cash, published 20 April 2021 (implementing partner and funder, not the AI developer).", "title": "Innovations in Togo and the future of cash distribution", "url": "https://www.givedirectly.org/togo/", "date": "2021-04-20", "written_by": "deployer", "quote": "Leveraging machine learning in combination with satellite imagery and cell phone metadata, the program was able to identify those living in extreme poverty" } ], "replication": { "produced_by": [ "Satellite-imagery poverty map ranking cantons", "Consumption model trained on mobile-phone metadata with a recent phone survey as ground truth", "USSD self-registration and mobile-money payment rails", "Government and GiveDirectly funding decision" ], "must_hold": [ "Target population owns and uses mobile phones (non-subscribers are excluded by design)", "Operators provide anonymized call detail records", "Recent ground-truth survey available for training", "Mobile-money infrastructure in place" ], "echoes": [ { "date": "2021-01-01", "setting": "Nigeria Rapid Response Register: ML poverty maps from satellite imagery selected 1,163 poorest urban wards for COVID-19 cash (month only known; first day used)", "model_version": null, "outcome": "recurred", "source": "Date: Human Rights Watch (2021) 'began in January 2021', https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living, and Smythe and Blumenstock (2022) PNAS 'mid-January 2021', https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/; ward count: World Bank (2021) PAD P176935, https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf (the PAD itself dates the USSD self-registration launch to June 2021)" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "546", "title": "Algorithm to Distribute Social Welfare Reported for Oversimplifying Economic Vulnerability", "url": "https://incidentdatabase.ai/cite/546", "severity": null, "group": "personal", "basis": "World Bank-backed poverty-targeting algorithm for cash transfers oversimplified household vulnerability, excluding poor Jordanians; Novissi used the same machine-learning targeting of the poor." }, "headline": "Novissi targeting pays 138,531 rural beneficiaries in Togo", "metric": "reduced exclusion errors by 4–21% relative to geographic alternatives", "comparator": { "against": "the geographic targeting options considered by the Government of Togo", "before": "errors of exclusion of 59%–78% for the feasible alternatives", "after": "errors of exclusion of 53% for the phone-based approach, a reduction of 4–21%", "basis": "measured", "quote": "Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/" }, "later": null, "value": null, "developer_country": "United States", "deployer_country": "Togo", "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 546 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the GiveDirectly item to its printed title and 20 April 2021 date, marked the World Bank discussion paper as month-only, restored “(middle)” to the Nature quote and labeled it as the Extended Data Fig. 1 caption, re-stated the developer team from the Nature affiliations (Bellue at Mannheim, Karlan and Udry at Northwestern) and the World Bank's IPA/CEGA/UC Berkeley description, reworded summary and headline because the ML set eligibility while 138,531 is the count of registrants validated and paid, and cited HRW and PNAS rather than the PAD for the January 2021 Nigeria echo date since the PAD gives June 2021.", "source": "https://www.givedirectly.org/togo/; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC8967719/fullTextXML; https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf; https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/; https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Scalability 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://poverty-action.org/contact-us; https://numerique.gouv.tg/", "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": "2026-10-22", "change": "Organization name standardized to Innovations for Poverty Action.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, the deployer's report 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-0093", "catalog_version": "1.0.0", "source_fingerprint": "77448671472da44e0b4790a875da6f9da6fe878d11942d8dda8a6b9513fc8e36", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0093-source-0001", "AIBD-0093-source-0002", "AIBD-0093-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": "Innovations for Poverty Action, CEGA and UC Berkeley (Aiken and Blumenstock, UC Berkeley; Bellue, University of Mannheim; Karlan and Udry, Northwestern University) for the Government of Togo", "deployer": "Government of Togo, Ministry of Digital Economy and Transformation, with 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 who was paid: canton ranking and individual eligibility were both set by machine-learning predictions, while humans fixed the budget, thresholds and registration rules. The Nature evaluation is co-authored by the team that built the targeting method. The World Bank/Government of Togo case study is independent of the developers." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "Decisive for who was paid: canton ranking and individual eligibility were both set by machine-learning predictions, while humans fixed the budget, thresholds and registration rules. The Nature evaluation is co-authored by the team that built the targeting method. The World Bank/Government of Togo case study is independent of the developers." } } }, "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": "Five mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men) to each of 138,531 rural beneficiaries during COVID-19 restrictions (World Bank study, Table 1)." }, { "group": "organizational", "depth": 2, "reason": "The Government of Togo delivered shock-responsive rural assistance without a social registry. 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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": "reduced exclusion errors by 4–21% relative to geographic alternatives", "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": "546", "title": "Algorithm to Distribute Social Welfare Reported for Oversimplifying Economic Vulnerability", "url": "https://incidentdatabase.ai/cite/546", "group": "personal", "basis": "World Bank-backed poverty-targeting algorithm for cash transfers oversimplified household vulnerability, excluding poor Jordanians; Novissi used the same machine-learning targeting of the poor." } } }, "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": "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-0093" } }, "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 Scalability", "taxonomy_note": "The good for beneficiaries is emergency cash, and the type names the program's reach into rural areas with no registry." } }, "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 Government of Togo launched the Novissi emergency cash program on 8 April 2020. On 2 November 2020 it extended it to rural areas using a machine-learning pipeline built with UC Berkeley and IPA researchers. Satellite imagery produced poverty estimates for every canton to rank the poorest. Within eligible cantons a model trained on mobile-phone metadata predicted each subscriber's consumption to decide who was paid. Beneficiaries self-registered by USSD and received mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men). The World Bank and Government of Togo report 138,531 rural beneficiaries from November 2020 to August 2021, funded by GiveDirectly (US$10 million). The Nature evaluation found phone-based targeting reduced exclusion errors by 4–21% relative to geographic alternatives.", "benefit_type": "Increased Scalability" } }, "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": "Five mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men) to each of 138,531 rural beneficiaries during COVID-19 restrictions (World Bank study, Table 1)." }, { "group": "organizational", "depth": 2, "reason": "The Government of Togo delivered shock-responsive rural assistance without a social registry. The World Bank documents the model as a reusable case study." }, { "group": "societal", "depth": 3, "reason": "Material to many: 138,531 rural beneficiaries reached, and the Nature evaluation reports exclusion errors 4–21% lower than geographic targeting." } ] } }, "provenance": { "kind": "legacy_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": "Prioritize the poorest cantons and the poorest mobile subscribers within them for COVID-19 emergency cash transfers in the absence of a social registry." } }, "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": "Rural cantons, Togo", "developer_country": "United States", "deployer_country": "Togo" } }, "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": "Prioritize the poorest cantons and the poorest mobile subscribers within them for COVID-19 emergency cash transfers in the absence of a social registry." } }, "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": "2020-11-02", "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 546 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the GiveDirectly item to its printed title and 20 April 2021 date, marked the World Bank discussion paper as month-only, restored “(middle)” to the Nature quote and labeled it as the Extended Data Fig. 1 caption, re-stated the developer team from the Nature affiliations (Bellue at Mannheim, Karlan and Udry at Northwestern) and the World Bank's IPA/CEGA/UC Berkeley description, reworded summary and headline because the ML set eligibility while 138,531 is the count of registrants validated and paid, and cited HRW and PNAS rather than the PAD for the January 2021 Nigeria echo date since the PAD gives June 2021.", "source": "https://www.givedirectly.org/togo/; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC8967719/fullTextXML; https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf; https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/; https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Scalability 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://poverty-action.org/contact-us; https://numerique.gouv.tg/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Innovations for Poverty Action.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite-imagery poverty map ranking cantons", "Consumption model trained on mobile-phone metadata with a recent phone survey as ground truth", "USSD self-registration and mobile-money payment rails", "Government and GiveDirectly funding decision" ], "must_hold": [ "Target population owns and uses mobile phones (non-subscribers are excluded by design)", "Operators provide anonymized call detail records", "Recent ground-truth survey available for training", "Mobile-money infrastructure in place" ], "echoes": [ { "date": "2021-01-01", "setting": "Nigeria Rapid Response Register: ML poverty maps from satellite imagery selected 1,163 poorest urban wards for COVID-19 cash (month only known; first day used)", "outcome": "recurred", "source": "Date: Human Rights Watch (2021) 'began in January 2021', https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living, and Smythe and Blumenstock (2022) PNAS 'mid-January 2021', https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/; ward count: World Bank (2021) PAD P176935, https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf (the PAD itself dates the USSD self-registration launch to June 2021)" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite-imagery poverty map ranking cantons", "Consumption model trained on mobile-phone metadata with a recent phone survey as ground truth", "USSD self-registration and mobile-money payment rails", "Government and GiveDirectly funding decision" ], "must_hold": [ "Target population owns and uses mobile phones (non-subscribers are excluded by design)", "Operators provide anonymized call detail records", "Recent ground-truth survey available for training", "Mobile-money infrastructure in place" ], "echoes": [ { "date": "2021-01-01", "setting": "Nigeria Rapid Response Register: ML poverty maps from satellite imagery selected 1,163 poorest urban wards for COVID-19 cash (month only known; first day used)", "outcome": "recurred", "source": "Date: Human Rights Watch (2021) 'began in January 2021', https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living, and Smythe and Blumenstock (2022) PNAS 'mid-January 2021', https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/; ward count: World Bank (2021) PAD P176935, https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf (the PAD itself dates the USSD self-registration launch to June 2021)" } ] } } }, "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 546 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the GiveDirectly item to its printed title and 20 April 2021 date, marked the World Bank discussion paper as month-only, restored “(middle)” to the Nature quote and labeled it as the Extended Data Fig. 1 caption, re-stated the developer team from the Nature affiliations (Bellue at Mannheim, Karlan and Udry at Northwestern) and the World Bank's IPA/CEGA/UC Berkeley description, reworded summary and headline because the ML set eligibility while 138,531 is the count of registrants validated and paid, and cited HRW and PNAS rather than the PAD for the January 2021 Nigeria echo date since the PAD gives June 2021.", "source": "https://www.givedirectly.org/togo/; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC8967719/fullTextXML; https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf; https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/; https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Scalability 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://poverty-action.org/contact-us; https://numerique.gouv.tg/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Innovations for Poverty Action.", "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 protection and humanitarian cash transfers", "segment": "Government social 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": "peer-reviewed study", "citation": "Aiken, E., Bellue, S., Karlan, D., Udry, C., Blumenstock, J. E. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature 603, 864–870 (the quotation is the Extended Data Fig. 1 caption, panel a, with its reference markers omitted).", "title": "Machine learning and phone data can improve targeting of humanitarian aid", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/", "date": "2022-03-16", "written_by": "makers", "quote": "Satellite imagery of Togo is used to construct micro-estimates of poverty (middle), which are overlayed with population data to produce canton-level estimates of wealth." }, { "kind": "report", "citation": "Lawson, C., Koudeka, M., Cárdenas Martínez, A. L., Alberro Encinas, L. I., George Karippacheril, T. (2023). Novissi Togo: Harnessing Artificial Intelligence to Deliver Shock-Responsive Social Protection. World Bank and Government of Togo, Social Protection and Jobs Discussion Paper No. 2306, September 2023 (month only; first day used).", "title": "Novissi Togo: Harnessing Artificial Intelligence to Deliver Shock-Responsive Social Protection", "url": "https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf", "date": "2023-09-01", "written_by": "deployer", "quote": "Subsequently, the program was extended on November 2, 2020, to rural areas with a high prevalence of poverty (henceforth referred to as model 2)." }, { "kind": "press release", "citation": "GiveDirectly (2021). Innovations in Togo and the future of cash distribution. 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World Bank and Government of Togo, Social Protection and Jobs Discussion Paper No. 2306, September 2023 (month only; first day used).", "title": "Novissi Togo: Harnessing Artificial Intelligence to Deliver Shock-Responsive Social Protection", "url": "https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf", "date": "2023-09-01", "written_by": "deployer", "quote": "Subsequently, the program was extended on November 2, 2020, to rural areas with a high prevalence of poverty (henceforth referred to as model 2)." }, { "kind": "press release", "citation": "GiveDirectly (2021). Innovations in Togo and the future of cash distribution. 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On 2 November 2020 it extended it to rural areas using a machine-learning pipeline built with UC Berkeley and IPA researchers. Satellite imagery produced poverty estimates for every canton to rank the poorest. Within eligible cantons a model trained on mobile-phone metadata predicted each subscriber's consumption to decide who was paid. Beneficiaries self-registered by USSD and received mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men). The World Bank and Government of Togo report 138,531 rural beneficiaries from November 2020 to August 2021, funded by GiveDirectly (US$10 million). The Nature evaluation found phone-based targeting reduced exclusion errors by 4–21% relative to geographic alternatives.", "replication": { "produced_by": [ "Satellite-imagery poverty map ranking cantons", "Consumption model trained on mobile-phone metadata with a recent phone survey as ground truth", "USSD self-registration and mobile-money payment rails", "Government and GiveDirectly funding decision" ], "must_hold": [ "Target population owns and uses mobile phones (non-subscribers are excluded by design)", "Operators provide anonymized call detail records", "Recent ground-truth survey available for training", "Mobile-money infrastructure in place" ], "echoes": [ { "date": "2021-01-01", "setting": "Nigeria Rapid Response Register: ML poverty maps from satellite imagery selected 1,163 poorest urban wards for COVID-19 cash (month only known; first day used)", "outcome": "recurred", "source": "Date: Human Rights Watch (2021) 'began in January 2021', https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living, and Smythe and Blumenstock (2022) PNAS 'mid-January 2021', https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/; ward count: World Bank (2021) PAD P176935, https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf (the PAD itself dates the USSD self-registration launch to June 2021)" } ] } } }, "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-0052", "case_url": "records/AIBD-0052.html", "reason": "The developer is headquartered in United States; the case names Australia.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Australia" }, { "label": "Case location", "value": "Melbourne, Australia" } ], "filters": { "origin": "United States", "destination": [ "Australia" ] }, "record": { "id": "AIBD-0052", "title": "People with severe paralysis regained control of a computer at home", "summary": "Two people with ALS regained control of a computer at home using Stentrode and eye tracking.", "date_observed": "2020-10-28", "date_basis": "source publication", "date_precision": "day", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "Two people with ALS received a Stentrode implant at the Royal Melbourne Hospital. Attempted movements generated computer clicks, while an eye tracker moved the cursor. Participant 1 used a support-vector machine, and participant 2 used a manually tuned threshold classifier. They began use at home without research staff on days 86 and 71 after implantation, although a caregiver still helped set up the system. Both could text, shop, and check finances. A later report followed four implanted participants, including the original two, for 12 months. It reported no serious device-related events and eight mild device effects.", "category": "benefit created", "unexpected": false, "purpose": "Restore digital device control for people with severe paralysis without open-brain surgery", "system": "Synchron Stentrode with a participant-specific decoder and Tobii Dynavox eye tracking", "model_version": null, "developer": "Synchron Inc. with the University of Melbourne", "deployer": "Royal Melbourne Hospital (implantation and follow-up); participants' homes (use)", "location": "Melbourne, Australia", "domain": "Neurotechnology / assistive communication", "segment": "People with ALS and severe upper-limb paralysis", "benefit_type": "Improved Accessibility", "taxonomy_note": null, "registry_ids": { "clinicaltrials_gov": "NCT03834857" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "Participants regained sustained control of everyday computer tasks at home. No separate societal outcome was measured." } ], "ai_role": { "status": "assessed", "statement": "Participant 1 used a support-vector machine trained on movement attempts. Participant 2 used a manually tuned threshold classifier. Both converted brain signals into clicks, while an eye tracker handled cursor movement. The measured benefit belonged to the combined system." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Oxley TJ, Yoo PE, Rind GS, et al. (2021). Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience. Journal of NeuroInterventional Surgery 13(2):102-108.", "title": "Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7848062/", "date": "2020-10-28", "written_by": "makers", "quote": "Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants." }, { "kind": "peer-reviewed study", "citation": "Peter Mitchell et al., “Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study,” JAMA Neurology 80, no. 3 (2023): 270–278, doi:10.1001/jamaneurol.2022.4847. Published online January 9, 2023.", "title": "Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9857731/", "date": "2023-01-09", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "An implanted electrode array recorded attempted movements.", "A participant-specific decoder converted signals into computer clicks.", "Eye tracking supplied cursor movement, and caregivers helped with setup." ], "must_hold": [ "Suitable venous anatomy and preserved motor-cortex activity.", "Clinical follow-up, training, caregiver assistance, and antiplatelet treatment." ], "echoes": [ { "date": "2023-01-09", "setting": "The same SWITCH study, expanded to four implanted participants", "model_version": null, "outcome": "recurred", "source": "Mitchell et al. (2023), doi:10.1001/jamaneurol.2022.4847. The first two participants overlap with the earlier report." } ] }, "paired_incident": null, "headline": "People with severe paralysis regained control of a computer at home", "metric": "typed 13.81 and 20.10 correct characters per minute", "comparator": { "against": "An earlier, separate study of the Utrecht implanted interface", "before": "Home use began 197 days after implantation in that earlier participant", "after": "Home use began on days 86 and 71 in the two Stentrode participants", "basis": "measured", "quote": "The current study achieved unsupervised home use of multiple-click actions within 86 days (participant 1) and 71 days (participant 2) of neurointerventional surgery.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7848062/" }, "later": { "status": "persisted", "date": "2023-01-09", "note": "A report covering four implanted participants, including the original two, found no serious device-related events during 12 months of follow-up. Eight mild device effects occurred.", "quote": null, "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9857731/" }, "value": null, "developer_country": "United States", "deployer_country": "Australia", "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": "Replaced “two men” with “two people” in the title and account because no cited source gives the participants' sex; added the paper's implant timing (mid-2019 and early 2020) and an explicit note that the publication date stands in for the date observed; renamed the model version to the paper's own registration (NCT03834857) because the JNIS paper never uses the SWITCH name; and treated Mitchell 2023 as independent because its lead and senior authors are Royal Melbourne Hospital investigators.", "source": "Oxley et al. (2021) J NeuroInterv Surg, PMC7848062; Mitchell et al. (2023) JAMA Neurology, PMID 36622685; RMH statement 24 January 2023", "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/PMC7848062/; https://europepmc.org/abstract/MED/36622685", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://synchron.com/news; https://en.wikipedia.org/wiki/Royal_Melbourne_Hospital", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Reviewed the original evidence and supporting material; updated system details, methods, outcomes, limitations, costs, and source lineage.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7848062/", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: two peer-reviewed studies from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0052", "catalog_version": "1.0.0", "source_fingerprint": "1bedfa433679e79d0899a74679fd4d86a39e5fc243628f6951ae8a1aa7d24871", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0052-source-0001", "AIBD-0052-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "NCT03834857" ], "role": "evaluations" }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. 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The comparison with the Utrecht interface involved different participants and protocols." } }, "evaluations[].design": { "state": "value_present", "value": { "kind": "text", "text": "A prospective, open-label feasibility study without a control group. The later report includes the first two participants." }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. (2023), Methods, Results, and Table 2.", "record_fields": [], "source_refs": [ "AIBD-0052-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "A prospective, open-label feasibility study without a control group. 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Caregiver assistance with setup remained necessary." }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. (2023), Methods, Results, and Table 2.", "record_fields": [], "source_refs": [ "AIBD-0052-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The first participants began home use without research staff on days 86 and 71 after implantation. Caregiver assistance with setup remained necessary." } }, "evaluations[].id": { "state": "value_present", "value": { "kind": "text", "text": "NCT03834857" }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. 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Without a control group, superiority over other assistive systems cannot be established." } }, "external_references[]": { "state": "value_present", "value": { "kind": "structured", "data": { "ClinicalTrials.gov": "NCT03834857" } }, "provenance": { "kind": "source_review", "basis": "Original study registration, inspected through the ClinicalTrials.gov API.", "record_fields": [], "source_refs": [ "AIBD-0052-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "NCT03834857" } }, "follow_up[]": { "state": "value_present", "value": { "kind": "text", "text": "The four-person follow-up found no serious device-related events over 12 months. Eight mild device effects occurred, and one participant later demonstrated texting without eye tracking." }, "provenance": { "kind": "source_review", "basis": "2023 Results: Safety and Feasibility end points; Table 2.", "record_fields": [], "source_refs": [ "AIBD-0052-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The four-person follow-up found no serious device-related events over 12 months. Eight mild device effects occurred, and one participant later demonstrated texting without eye tracking." } }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Clinicians selected and implanted participants, monitored safety, and managed antiplatelet treatment. Researchers tuned decoders, and caregivers helped with setup at home." }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. 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Researchers tuned decoders, and caregivers helped with setup at home." } }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0052" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "value_present", "value": { "kind": "text", "text": "The system required implantation, a chest receiver, a computer, eye tracking, individual training, caregiver support, and clinical follow-up." }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. 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The initial two are included in the later four." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Accessibility" } }, "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": "text", "text": "Participants regained the ability to select items and perform everyday computer tasks at home." }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. 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The small, selected group does not establish broad suitability." } }, "outcomes[].duration": { "state": "value_present", "value": { "kind": "text", "text": "The original report covered 12 months for participant 1 and three months for participant 2. The later paper followed four implanted participants for 12 months each." }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. (2023), Methods, Results, and Table 2.", "record_fields": [], "source_refs": [ "AIBD-0052-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The original report covered 12 months for participant 1 and three months for participant 2. The later paper followed four implanted participants for 12 months each." } }, "outcomes[].id": { "state": "value_present", "value": { "kind": "text", "text": "AIBD-0052-outcome-01" }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. 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One participant fainted briefly after surgery. The later four-person report recorded eight mild device effects." } }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Restore digital device control for people with severe paralysis without open-brain surgery" } }, "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": "text", "text": "The first clinical study and home use took place in Melbourne, Australia. Synchron was based in the United States and worked with Australian investigators." }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. 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Synchron was based in the United States and worked with Australian investigators." } }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Restore digital device control for people with severe paralysis without open-brain surgery" } }, "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": "structured", "data": { "initial_publication": "2020-10-28", "follow_up_publication": "2023-01-09", "first_participant_implant": "mid-2019", "second_participant_implant": "early 2020", "precision_of_individual_implants": "part-year only" } }, "provenance": { "kind": "source_review", "basis": "2020 Results; 2023 publisher online-publication metadata.", "record_fields": [], "source_refs": [ "AIBD-0052-source-0001", "AIBD-0052-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The first implants occurred in mid-2019 and early 2020. The initial report appeared on 28 October 2020." } }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "structured", "data": { "study": "NCT03834857", "first_report": 2, "later_report": 4, "overlapping_participants": 2, "independent_replication": false } }, "provenance": { "kind": "source_review", "basis": "2023 Introduction and Results.", "record_fields": [], "source_refs": [ "AIBD-0052-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The later four-person report includes the original two participants." } }, "replication.conditions": { "state": "value_present", "value": { "kind": "text", "text": "Suitable anatomy, preserved motor signals, training, caregiver setup, and continuing clinical care were required." }, "provenance": { "kind": "source_review", "basis": "Oxley et al. (2020), Results and Methods; Supplementary Appendix, pp. 3–10. Mitchell et al. 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(2021) J NeuroInterv Surg, PMC7848062; Mitchell et al. 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Identification of camera trap images by artificial intelligence and human experts produces similar multi-species occupancy models. Journal of Applied Ecology 63(5):e70370. First published May 6, 2026. doi:10.1111/1365-2664.70370.", "title": "Identification of camera trap images by artificial intelligence and human experts produces similar multi-species occupancy models", "url": "https://doi.org/10.1111/1365-2664.70370", "date": "2026-05-06", "written_by": "makers", "quote": null }, { "kind": "dataset", "citation": "Archived code and data accompanying the Thornton et al. occupancy study. 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Targeted human review may resolve problematic classifications without reviewing every image." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "assessments[].assessor_date": { "state": "value_present", "value": { "kind": "structured", "data": { "assessor": "AIBD", "reviewed_on": "2026-10-25" } }, "provenance": { "kind": "curatorial", "basis": "Recorded this source review.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD reviewed this assessment on October 25, 2026." } }, "assessments[].depth": { "state": "value_present", "value": { "kind": "structured", "data": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The study demonstrates an automated analytical capability with broadly similar occupancy estimates for shared species. Rare-species omissions and setting-specific differences limit its use." }, { "group": "societal", "reason": "No changed conservation decision or wildlife outcome is documented." } ] } }, "provenance": { "kind": "curatorial", "basis": "Applied the existing AIBD depth scale to the stated research benefit.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Organizational depth 2: a demonstrated analytical capability for shared species. Societal depth is unassessed because a conservation outcome is not documented." } }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The makers' claim", "basis": "Sources: the makers' peer-reviewed study and the makers' dataset. For the benefit itself, the cited sources include no independent source and no finding by a regulator or court.", "interpretation": "The existing display rule reflects source authorship and kind. The detailed fields preserve methodological qualifications." } }, "provenance": { "kind": "derivation", "basis": "Applied the existing display rule to the reviewed evidence.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001", "AIBD-0584-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The study includes developer participation. The comparative research design and limitations remain explicit." } }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "text", "text": "The study demonstrates an automated analytical capability with broadly similar occupancy estimates for shared species. Rare-species omissions and setting-specific differences limit its use. No changed conservation decision or wildlife outcome is documented." }, "provenance": { "kind": "curatorial", "basis": "Separated organizational benefit from unmeasured societal effects.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "assessments[].rubric_version": { "state": "value_present", "value": { "kind": "text", "text": "AIBD depth scale and the Trustible benefit taxonomy in the current local catalog, reviewed October 25, 2026." }, "provenance": { "kind": "curatorial", "basis": "Applied the existing repository method and taxonomy without modifying them.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "assessments[].status_basis": { "state": "value_present", "value": { "kind": "text", "text": "The record distinguishes demonstrated analytical results from estimated or unmeasured downstream benefits." }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "assessments[].target": { "state": "value_present", "value": { "kind": "text", "text": "The documented research benefit for the participating organizations." }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "availability": { "state": "value_present", "value": { "kind": "text", "text": "The full article and versioned code-and-data archive are public. The inspected archive does not contain all regional inputs, and the complete supplement was inaccessible." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001", "AIBD-0584-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "value_present", "value": { "kind": "text", "text": "Comparative research study." }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "case_links[]": { "state": "value_present", "value": { "kind": "structured", "data": { "related_cases": [ "AIBD-0583" ], "relationship": "Separate comparative wildlife studies that use MegaDetector. They do not replicate the same outcome." } }, "provenance": { "kind": "curatorial", "basis": "Linked related research without combining study populations or results.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The other new conservation record concerns a separate evaluation that also uses MegaDetector. It is not a replication of this result." } }, "claims[].evidence_links": { "state": "value_present", "value": { "kind": "structured", "data": { "benefit_source_refs": [ "AIBD-0584-source-0001" ], "methods_source_refs": [ "AIBD-0584-source-0002" ] } }, "provenance": { "kind": "source_review", "basis": "The lead study supplies the comparative result; additional materials supply methods or reproducibility information.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The original study supports the comparative result. The second source adds methods or reproducibility information." } }, "claims[].evidence_strength": { "state": "value_present", "value": { "kind": "text", "text": "The original comparison supports useful ecological estimates for many shared species and documents exceptions. Developer participation, unavailable materials, and the absence of measured downstream outcomes remain explicit." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "claims[].missingness": { "state": "value_present", "value": { "kind": "text", "text": "The exact SpeciesNet checkpoint, missing regional CSV inputs, complete supplementary document, subsequent adoption, actual labor savings, and conservation decisions remain unresolved." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "claims[].proposition": { "state": "value_present", "value": { "kind": "text", "text": "Researchers used MegaDetector and SpeciesNet to produce ecological estimates similar to expert analysis for many species, while some species were missed or misclassified." }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "cross_border_flows[]": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Reviewed the original study and its stated data availability.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "Specific cross-border transfers of study data or model outputs are not documented in the reviewed sources.", "review_state": "reviewed", "coverage": "complete" }, "decisions[].action_outcome": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "institutional decisions" }, "deployment_stage": { "state": "value_present", "value": { "kind": "text", "text": "Research evaluation using field-collected camera images." }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "economics[].amount": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].realization": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_reported", "review_state": "reviewed", "coverage": "complete", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "value_present", "value": { "kind": "text", "text": "The comparison demonstrates an automated route to useful ecological estimates for many shared species. It does not quantify net time saved or improved conservation decisions relative to the existing workflow." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "evaluations[].comparator": { "state": "value_present", "value": { "kind": "text", "text": "Ecological models fitted to expert identifications of the same images; Montana experts already used MegaDetector for initial sorting." }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "evaluations[].design": { "state": "value_present", "value": { "kind": "text", "text": "Comparative ecological analysis of the same regional image datasets using expert labels and six AI processing configurations. The comparator is not uniformly free of AI." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "evaluations[].effect": { "state": "value_present", "value": { "kind": "text", "text": "Mean occupancy estimates were broadly similar for shared species. Individual differences reached 0.40 in Montana, and some species were omitted or added by classification errors." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "evaluations[].exposure": { "state": "value_present", "value": { "kind": "text", "text": "The study includes 638 Washington cameras, 263 Montana cameras, and 326 Guatemala cameras. These are sampling units, not beneficiary counts or a denominator for conservation success." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "evaluations[].id": { "state": "value_present", "value": { "kind": "text", "text": "AIBD-0584-evaluation-thornton-2026" }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "evaluations[].measurement_method": { "state": "value_present", "value": { "kind": "text", "text": "The authors compared occupancy probabilities, detection probabilities, species-environment relationships, precision, and spatial projections from fitted models." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "evaluations[].metric": { "state": "value_present", "value": { "kind": "structured", "data": { "mean_absolute_occupancy_probability_difference": { "Washington": [ 0.04, 0.05 ], "Montana": [ 0.05, 0.07 ], "Guatemala": [ 0.03, 0.06 ] }, "scope": "Species shared by expert and AI datasets, across six processing configurations.", "maximum_individual_species_difference_Montana": 0.4, "Montana_mountain_goat_detections_misclassified": 94 } }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Mean absolute occupancy-probability differences were 0.04-0.05 in Washington, 0.05-0.07 in Montana, and 0.03-0.06 in Guatemala for species shared by the AI and expert datasets." } }, "evaluations[].reproduction_materials": { "state": "value_present", "value": { "kind": "structured", "data": { "archive_url": "https://zenodo.org/records/19560799", "archive_version": "1.0.1", "archive_date": "2026-04-13", "zip_md5": "2171ba86c5e91025ea6f12fe4c757e2d", "available": "Washington CSV inputs and R analysis scripts for all three regions.", "missing": "Montana and Guatemala CSV inputs referenced by the scripts were absent from the inspected release.", "public_image_subset": { "url": "https://lila.science/datasets/wsu-lynx/", "locations": 637, "images": 1205453, "qualification": "This public subset excludes humans and vehicles and differs from the paper's Washington denominator." }, "supplement": { "url": "https://besjournals.onlinelibrary.wiley.com/doi/suppl/10.1111/1365-2664.70370/supinfo/jpe70370-sup-0001-Supinfo.docx", "access": "The complete DOCX could not be retrieved; only the article's description and captions were readable." } } }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001", "AIBD-0584-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Zenodo version 1.0.1 contains Washington CSV inputs and scripts for all regions. Referenced Montana and Guatemala inputs are absent. The public Washington image subset has a different denominator. The complete supplement was inaccessible." } }, "evaluations[].setting_sample": { "state": "value_present", "value": { "kind": "structured", "data": { "Washington": { "cameras": 638, "images_approx": 1800000, "collection_years": "2016-2017" }, "Montana": { "cameras": 263, "images_approx": 1700000, "collection_years": "2018-2021" }, "Guatemala": { "cameras": 326, "images_approx": 280000, "collection_years": "2018-2019" } } }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Washington: 638 cameras and about 1.8 million images. Montana: 263 cameras and about 1.7 million images. Guatemala: 326 cameras and about 280,000 images." } }, "evaluations[].uncertainty_limitations": { "state": "value_present", "value": { "kind": "text", "text": "Comparisons of model estimates cover shared species. Rare-species omissions remain outside those comparisons. Regional filtering and sequence processing affect results. Montana's expert workflow already used AI. The exact SpeciesNet checkpoint, some regional inputs, and the complete supplement were unavailable in this review." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "external_references[]": { "state": "value_present", "value": { "kind": "structured", "data": { "study_doi": "10.1111/1365-2664.70370", "archive_version_1_0_1": "https://zenodo.org/records/19560799", "archive_version_1_0_0": "https://zenodo.org/records/19073128", "public_washington_subset": "https://lila.science/datasets/wsu-lynx/" } }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001", "AIBD-0584-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The study DOI is 10.1111/1365-2664.70370. The record links both archive versions and the public Washington image subset." } }, "follow_up[]": { "state": "not_assessed", "reason": "Post-study adoption and conservation decisions require follow-up.", "review_state": "not_assessed", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Researchers supplied regional filters and taxonomic adjustments and assessed the statistical output. Expert identifications provided the comparator. The authors recommend human review for some rare-species and biodiversity tasks." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "id": { "state": "value_present", "value": { "kind": "text", "text": "AIBD-0584" }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "infrastructure_dependencies[]": { "state": "value_present", "value": { "kind": "text", "text": "Camera datasets, MegaDetector, SpeciesNet, regional species information, and statistical analysis in R were required. A complete historical SpeciesNet checkpoint and all regional data inputs are not preserved in the inspected archive." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001", "AIBD-0584-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "The participating ecological research teams. Benefits to monitoring organizations and conservation managers are prospective unless subsequent use is documented." }, "provenance": { "kind": "source_review", "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "outcomes[].beneficiary_count": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Reviewed the original study and its stated data availability.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The study does not count people who benefited. Cameras, images, species, and detection events are study units.", "review_state": "reviewed", "coverage": "complete" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "text", "text": "Improved Knowledge Discovery" }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "text", "text": "Thornton, Morris, and colleagues compared ecological occupancy models derived from automated SpeciesNet identifications with models derived from expert identifications. The study used 638 cameras in Washington, 263 in Montana, and 326 in Guatemala. MegaDetector 5a located animals, and SpeciesNet classified the crops, with regional species lists, taxonomic adjustments, and sequence processing. Across six AI configurations, mean absolute differences in occupancy probabilities were 0.04-0.05 in Washington, 0.05-0.07 in Montana, and 0.03-0.06 in Guatemala. These comparisons cover species present in both datasets; they are not image-accuracy percentages. Some individual species differed substantially, with a maximum difference of 0.40 in Montana. All 94 mountain-goat detections in Montana were classified as domestic goats and omitted from the AI occupancy models. Montana's expert workflow already used MegaDetector for initial sorting. The study demonstrates automated ecological analysis with broadly similar results for shared species, while preserving a need for human review in some research tasks. It does not measure net labor savings, changed management decisions, or wildlife outcomes. Google developer Dan Morris coauthored the study. The author summary was first dated March 17, 2026; the full paper appeared on May 6." }, "provenance": { "kind": "source_review", "basis": "Original study, methods, results, and discussion.", "record_fields": [], "source_refs": [ "AIBD-0584-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete" }, "outcomes[].distribution": { "state": "value_present", "value": { "kind": "text", "text": "Agreement varied by region and species. Shared-species comparisons do not cover species missing from the AI datasets. 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It was published in Nature on 11 July 2023 with experimental validation. That validation included a 2.9 angstrom cryo-EM structure of a designed binder-haemagglutinin complex. The structure matched the computational design model to 0.63 angstrom r.m.s.d. On 24 February 2026 an unaffiliated group at Shandong University and collaborating institutes reported in Nature Communications. They used RFdiffusion and ProteinMPNN to design rigid helical-bundle and TAIL domains for a trimbody fiducial. They then determined atomic-resolution cryo-EM structures of four small proteins that are normally intractable. Human Galectin10 came out at 2.62 angstroms and Aequorea coerulescens GFP at 2.29 angstroms. 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It identified Tmem81 as part of a conserved trimeric sperm complex with the known essential fertilization factors Izumo1 and Spaca6. The prediction was then tested at the bench. The authors demonstrated that Tmem81 is essential for male fertility in zebrafish and mice. They showed Izumo1, Spaca6 and Tmem81 interact in zebrafish sperm and the human orthologues interact in vitro. They found complex formation creates the binding site for the egg factor Bouncer in zebrafish. 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He “presented to our nephrological institution with a complex medical history”. It included “progressive hearing loss and amaurosis since shortly after birth”, dilated cardiomyopathy, chronic kidney disease and cirrhosis. The physicians “used ChatGPT to obtain potential genetic targets based on the patient's medical history. One such target was the Alström Syndrome 1 gene (ALMS1)”. A geneticist “within just two business days confirmed that the patient actually possesses two distinct mutations in ALMS1”. Further tests then characterized hypogonadism and metabolic findings consistent with the syndrome. The consultation date is not stated.", "category": "benefit created", "unexpected": true, "purpose": "ChatGPT is a general-purpose conversational assistant. Generating candidate genes for a rare multisystem disease is not a function it was built for. This benefit was not its designed purpose.", "system": "ChatGPT", "model_version": "ChatGPT (Sep 14 version), as cited by the authors", "developer": "OpenAI", "deployer": "Medical University of Vienna, Division of Nephrology and Dialysis", "location": "Vienna, Austria", "domain": "Healthcare / nephrology and clinical genetics", "segment": "Rare disease diagnosis", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "A unifying genetic diagnosis at 44 for symptoms present since infancy, enabling targeted follow-up. No curative treatment is reported, so material rather than life-saving." } ], "ai_role": { "status": "assessed", "statement": "ChatGPT proposed ALMS1 among candidate genes from a physician-written history. 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He had been diagnosed with Cornelia de Lange syndrome at age four based on facial dysmorphism, microcephaly, short stature and intellectual disability. At 22 his Cornelia de Lange clinical diagnostic score was 5 of 19. His frontal facial photograph and clinical features were uploaded to Face2Gene. This “introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7”. The paper gives no ranking or scores for these candidates. Whole-exome sequencing then identified a novel heterozygous DYRK1A variant, c.848dup, p.(Asn283LysfsTer6), confirming MRD7. The 18-year span in the title derives from the ages the paper gives, diagnosed at four and re-evaluated at 22. The paper does not state a duration. The consultation date is not given.", "category": "benefit created", "unexpected": false, "purpose": "Face2Gene is built to suggest candidate syndromes from facial images and clinical features. This suggestion was that purpose.", "system": "Face2Gene (DeepGestalt facial phenotyping)", "model_version": null, "developer": "FDNA", "deployer": "Department of Neurology, National Hospital Organization Akita National Hospital", "location": "Yurihonjo, Japan", "domain": "Healthcare / clinical genetics", "segment": "Rare disease diagnosis", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "A correct molecular diagnosis replaced a misdiagnosis held since age four. The report documents no treatment change, so material rather than life-changing." } ], "ai_role": { "status": "assessed", "statement": "Face2Gene listed MRD7 among four candidates. The paper gives no ranking. Whole-exome sequencing made the diagnosis. The AI redirected the differential after the clinical score undermined the prior diagnosis." }, "evidence": [ { "kind": "case report", "citation": "Obara K, Abe E, Toyoshima I (2023). Whole-Exome Sequencing Identified a Novel DYRK1A Variant in a Patient With Intellectual Developmental Disorder, Autosomal Dominant 7. Cureus (PMC9822525).", "title": "Whole-Exome Sequencing Identified a Novel DYRK1A Variant in a Patient With Intellectual Developmental Disorder, Autosomal Dominant 7", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9822525/", "date": "2023-01-05", "written_by": "deployer", "quote": "As a result, Face2Gene introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7." } ], "replication": { "produced_by": [ "Frontal facial photograph plus clinical features uploaded to Face2Gene", "A prior clinical diagnosis that no longer fit the clinical score", "Access to whole-exome sequencing to confirm" ], "must_hold": [ "The true syndrome is represented in Face2Gene's training data (authors note MRD7 recommendations improve as cases are registered)", "Clinicians pursue a candidate that is one of several suggestions" ], "echoes": [ { "date": "2025-11-01", "setting": "International case series, 17 cases / 19 patients, in which DeepGestalt changed geneticists' testing decisions; American Journal of Medical Genetics Part A vol. 197 issue 11 (issue month, first day used)", "model_version": null, "outcome": "recurred", "source": "Rudy NL, Gomes A, et al. (2025). Artificial Intelligence Software Changes Rare Disease Testing Strategy in Real Time. 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Cureus (PMC9822525).", "title": "Whole-Exome Sequencing Identified a Novel DYRK1A Variant in a Patient With Intellectual Developmental Disorder, Autosomal Dominant 7", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9822525/", "date": "2023-01-05", "written_by": "deployer", "quote": "As a result, Face2Gene introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7." } ] } }, "provenance": { "kind": "legacy_record", "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": "Face2Gene suggested MRD7 for a 22-year-old misdiagnosed with Cornelia de Lange at age 4; exome sequencing confirmed a DYRK1A variant.", "account": "Obara, Abe and Toyoshima of the National Hospital Organization Akita National Hospital, Japan, reported a 22-year-old man in Cureus on 5 January 2023. He had been diagnosed with Cornelia de Lange syndrome at age four based on facial dysmorphism, microcephaly, short stature and intellectual disability. At 22 his Cornelia de Lange clinical diagnostic score was 5 of 19. His frontal facial photograph and clinical features were uploaded to Face2Gene. This “introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7”. The paper gives no ranking or scores for these candidates. Whole-exome sequencing then identified a novel heterozygous DYRK1A variant, c.848dup, p.(Asn283LysfsTer6), confirming MRD7. The 18-year span in the title derives from the ages the paper gives, diagnosed at four and re-evaluated at 22. The paper does not state a duration. The consultation 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": "Face2Gene (DeepGestalt facial phenotyping)" } }, "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": "FDNA" } }, "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": "Face2Gene redirects an 18-year misdiagnosis of Cornelia de Lange syndrome toward DYRK1A-related MRD7 in a 22-year-old man", "headline": "Face2Gene suggests MRD7 after an 18-year misdiagnosis" } }, "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": [ "d5a1a59dc70987ebbc1af9e729b075dc82d3e0c327e586efc7162336512a528e" ], "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": "Obara, Abe and Toyoshima of the National Hospital Organization Akita National Hospital, Japan, reported a 22-year-old man in Cureus on 5 January 2023. He had been diagnosed with Cornelia de Lange syndrome at age four based on facial dysmorphism, microcephaly, short stature and intellectual disability. At 22 his Cornelia de Lange clinical diagnostic score was 5 of 19. His frontal facial photograph and clinical features were uploaded to Face2Gene. This “introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7”. The paper gives no ranking or scores for these candidates. Whole-exome sequencing then identified a novel heterozygous DYRK1A variant, c.848dup, p.(Asn283LysfsTer6), confirming MRD7. The 18-year span in the title derives from the ages the paper gives, diagnosed at four and re-evaluated at 22. The paper does not state a duration. The consultation date is not given.", "replication": { "produced_by": [ "Frontal facial photograph plus clinical features uploaded to Face2Gene", "A prior clinical diagnosis that no longer fit the clinical score", "Access to whole-exome sequencing to confirm" ], "must_hold": [ "The true syndrome is represented in Face2Gene's training data (authors note MRD7 recommendations improve as cases are registered)", "Clinicians pursue a candidate that is one of several suggestions" ], "echoes": [ { "date": "2025-11-01", "setting": "International case series, 17 cases / 19 patients, in which DeepGestalt changed geneticists' testing decisions; American Journal of Medical Genetics Part A vol. 197 issue 11 (issue month, first day used)", "outcome": "recurred", "source": "Rudy NL, Gomes A, et al. (2025). Artificial Intelligence Software Changes Rare Disease Testing Strategy in Real Time. Am J Med Genet A, https://doi.org/10.1002/ajmg.a.64171 (quote: 'We present 17 cases involving 19 patients')" } ] } } }, "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-0098", "case_url": "records/AIBD-0098.html", "reason": "The developer is headquartered in Netherlands; the case names Philippines.", "details": [ { "label": "Developer headquarters", "value": "Netherlands" }, { "label": "Case jurisdictions", "value": "Philippines" }, { "label": "Case location", "value": "Dipaculao, Aurora Province, Philippines" } ], "filters": { "origin": "Netherlands", "destination": [ "Philippines" ] }, "record": { "id": "AIBD-0098", "title": "Philippine Red Cross activated its typhoon Early Action Protocol on 510's machine-learning impact forecast before Storm Nalgae (October 2022)", "summary": "510 ML impact forecast hit the trigger on 27 October 2022; PRC installed shelter kits for 78 Aurora households before Storm Nalgae", "date_observed": "2022-10-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent.", "category": "protective response", "unexpected": false, "purpose": "Forecast typhoon housing damage per municipality to trigger pre-agreed early actions before landfall.", "system": "510 impact-based forecasting (IBF) model for typhoons in the Philippines, operated through the 510 dashboard with PAGASA forecasts", "model_version": "eXtreme Gradient Boosting (XGBoost)", "developer": "510, an initiative of the Netherlands Red Cross, with the Philippine Red Cross and German Red Cross", "deployer": "Philippine Red Cross, with IFRC anticipatory DREF funding", "location": "Dipaculao, Aurora Province, Philippines", "domain": "Disaster risk reduction and forecast-based financing", "segment": "Red Cross National Society", "benefit_type": "Reduced Risk", "taxonomy_note": "Shelter reinforcement before the storm reduces potential harm, but the post-storm survey gives no counterfactual.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ], "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." }, "evidence": [ { "kind": "report", "citation": "IFRC and Philippine Red Cross (2022). Early Action Protocol Activation Notification: Philippines - Typhoons (EAP2019PH01, MDRPH049).", "title": "EAP Activation Notification, Philippines Typhoons (EAP2019PH01)", "url": "https://adore.ifrc.org/Download.aspx?FileId=590819", "date": "2022-10-27", "written_by": "deployer", "quote": "shelter damages are projected up to 10 per cent in different municipalities in the priority areas under the Early Action Protocol (EAP) for typhoon" }, { "kind": "report", "citation": "IFRC and Philippine Red Cross (2023). Early Action Protocol Activation Final Report: Philippines, Typhoon Paeng (MDRPH049). Dated 30 June 2023; PDF file timestamp 18 January 2024.", "title": "Early Action Protocol Activation Final Report, Philippines Typhoon Paeng", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr", "date": "2023-06-30", "written_by": "deployer", "quote": "Despite of the challenges, 100 percent of 78 households who received the kits were successfully installed/reinforced." }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309–329.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "In 2019, the 510 model was approved as the trigger model for the early action protocol for typhoons" }, { "kind": "other", "citation": "Free, R., van den Homberg, M., Sperna Weiland, F., Teklesadik, A., Melillo, M., Bogaard, T. (2022). Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events. EGU General Assembly 2022, EGU22-10474.", "title": "Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events", "url": "https://meetingorganizer.copernicus.org/EGU22/EGU22-10474.html", "date": "2022-05-23", "written_by": "makers", "quote": "An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons" } ], "replication": { "produced_by": [ "510 IBF damage model fed by PAGASA typhoon forecasts", "Pre-approved EAP with a 10% projected-damage trigger", "IFRC anticipatory DREF funds released on trigger", "Pre-positioned shelter kits, trained carpenters and pre-identified households" ], "must_hold": [ "Lead time near 72 hours", "Forecast track and intensity accuracy", "Weather permitting installation before landfall", "Local government authorization in no-build zones" ], "echoes": [ { "date": "2019-11-30", "setting": "Typhoon Kammuri 2019: EAP newly approved; IFRC DREF noted the forecast-based trigger might not yet be reached", "model_version": null, "outcome": "unknown", "source": "IFRC DREF MDRPH037, https://adore.ifrc.org/Download.aspx?FileId=271567" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "536", "title": "NJ Transit's Use of Modeling Software Miscalculated Storm Surge Threat Level", "url": "https://incidentdatabase.ai/cite/536", "severity": null, "group": "organizational", "basis": "Storm-impact model used for pre-landfall operational decisions underestimated Sandy's surge and NJ Transit lost equipment; 510's typhoon impact model triggers Red Cross action the same way." }, "headline": "510's ML forecast triggers PRC shelter kits before Storm Nalgae", "metric": "installed shelter kits for 78 Aurora households before Storm Nalgae", "comparator": null, "later": { "status": "persisted", "date": "2023-06-30", "note": "The IFRC final report of 30 June 2023 records all 78 households served and houses reinforced before impact, but a PRC lessons-learned workshop judged the trigger high and recommended reviewing the typhoon EAP.", "quote": "In addition, the trigger was considered high according to the solicited lessons from Paeng Lesson learned Workshop conducted by PRC with the support IFRC. Thus, one of the recommendations gathered is to review and revisit the Typhoon EAP.", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr" }, "value": null, "developer_country": "Netherlands", "deployer_country": "Philippines", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 536 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/536", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the final report's date to its cover date of 30 June 2023 (18 January 2024 was the PDF file timestamp) and the notification's date to its launch date of 27 October 2022, marked the NHESS paper and EGU abstract independent because 510 staff are co-authors rather than lead authors, and restated the EAP trigger as at least 10% of houses totally damaged in more than three municipalities 72 hours before landfall, with the lead-time condition waived at 56 hours.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://adore.ifrc.org/Download.aspx?FileId=590819; https://adore.ifrc.org/Download.aspx?FileId=271567", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://nhess.copernicus.org/articles/24/309/2024/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://en.wikipedia.org/wiki/Philippine_Red_Cross", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, the makers' other and two reports from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0098", "catalog_version": "1.0.0", "source_fingerprint": "4d27685232098c66182082682b2e51601106d680d084eeb7882325e3cdc5c1ac", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0098-source-0001", "AIBD-0098-source-0002", "AIBD-0098-source-0003", "AIBD-0098-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "510, an initiative of the Netherlands Red Cross, with the Philippine Red Cross and German Red Cross", "deployer": "Philippine Red Cross, with IFRC anticipatory DREF funding" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The 510 model forecast that damage to housing would exceed the threshold for early action in Aurora. Its forecasts incorporated data from PAGASA.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: the makers' peer-reviewed study, the makers' other and two reports from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].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": "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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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. 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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-0097", "case_url": "records/AIBD-0097.html", "reason": "The developer is headquartered in United States; the case names Malawi.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Malawi" }, { "label": "Case location", "value": "Malawi (nationwide)" } ], "filters": { "origin": "United States", "destination": [ "Malawi" ] }, "record": { "id": "AIBD-0097", "title": "American Red Cross used Facebook's machine-learning population maps to focus Malawi mapping for the June 2017 measles vaccination campaign (Measles & Rubella Initiative)", "summary": "Facebook's ML settlement maps cut Red Cross mapping for Malawi's June 2017 measles vaccination campaign (Measles & Rubella Initiative) from 94,080 km² to 2,745 km²", "date_observed": "2017-06-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Malawi's June 2017 measles vaccination campaign (Measles & Rubella Initiative) targeted more than 7.5 million children. The American Red Cross's Missing Maps team needed base maps for campaign monitoring. Facebook's population maps come from a machine-learning computer-vision method that detects buildings in satellite imagery. They were used to identify where people lived and which areas were already mapped in OpenStreetMap. Concentrating volunteers on settlements filtered out 97.1% of the land area. That reduced the area to map from 94,080 km² to 2,745 km² and completed the base map in a fraction of the normal time. The base map was to feed a dashboard for government and Red Cross monitors during the campaign. The article predates the campaign and reports no monitoring outcome. No vaccination-coverage outcome attributable to the maps is reported.", "category": "benefit created", "unexpected": false, "purpose": "Produce high-resolution population and settlement maps from satellite imagery for planning and humanitarian use.", "system": "Facebook (Meta) High Resolution Settlement Layer population density maps", "model_version": null, "developer": "Facebook Inc. with CIESIN, Columbia University, and the World Bank Development Data Group", "deployer": "American Red Cross (Missing Maps) supporting the Measles and Rubella Initiative campaign in Malawi", "location": "Malawi (nationwide)", "domain": "Humanitarian mapping for immunization campaign support", "segment": "Humanitarian NGO", "benefit_type": "Reduced Task Time", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "The source reports no outcome for the 7.5 million targeted children. Any benefit is indirect and unquantified." }, { "group": "organizational", "depth": 2, "reason": "Mapping area reduced from 94,080 km² to 2,745 km² and the base map completed in a fraction of the normal time." }, { "group": "societal", "depth": null, "reason": "A supporting role in one national campaign whose coverage effect was not measured." } ], "ai_role": { "status": "assessed", "statement": "The Facebook settlement data determined where volunteer mapping effort went and which areas were skipped. The campaign itself was a Measles & Rubella Initiative effort supported by the American Red Cross and other partners, with monitors from the national government. The Red Cross attributes efficiency, not coverage, to the maps. The Red Cross account is the implementing organization's and names only “Facebook's population datasets”, without mentioning machine learning. The identification with the High Resolution Settlement Layer and the machine-learning description are taken from the vendor-authored Tiecke et al. preprint. It does not mention the Malawi campaign." }, "evidence": [ { "kind": "press release", "citation": "American Red Cross (2017). Data: A vital tool for disaster response.", "title": "Data: A vital tool for disaster response", "url": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html", "date": "2017-06-07", "written_by": "deployer", "quote": "This technique reduced our mapping area from 94,080km2 to 2,745 km2." }, { "kind": "preprint", "citation": "Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., et al. (2017). Mapping the world population one building at a time. arXiv:1712.05839.", "title": "Mapping the world population one building at a time", "url": "https://arxiv.org/abs/1712.05839", "date": "2017-12-15", "written_by": "makers", "quote": "a computer vision method based on machine learning to create population maps from satellite imagery at a global scale" }, { "kind": "press release", "citation": "Eros, E., Schmeltzer, A. (2017). Stop the spots: Measles vaccinations in Malawi. Missing Maps blog (American Red Cross), 10 August 2017.", "title": "Stop the spots: Measles vaccinations in Malawi", "url": "https://www.missingmaps.org/blog/2017/08/10/malawi/", "date": "2017-08-10", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "Facebook settlement detection from satellite imagery", "Missing Maps volunteer tracing into OpenStreetMap", "Comparison with existing OpenStreetMap coverage", "Mobile data-collection dashboard for campaign monitors" ], "must_hold": [ "Imagery recent enough to reflect current settlements", "Building-detection accuracy in rural Malawi", "Volunteer and local mapping capacity" ], "echoes": [] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1159", "title": "Government‑Backed AI4Peat Mapping Tool Allegedly Misidentifies Granite Outcrops and Quarries as Peat", "url": "https://incidentdatabase.ai/cite/1159", "severity": null, "group": "organizational", "basis": "Government-backed machine-learning mapping from imagery misclassified land as peat, misleading restoration programs; Facebook's building-detection population maps steer humanitarian mapping the same way." }, "headline": "Facebook ML maps cut Red Cross Malawi mapping area to 2,745 km²", "metric": "reducing the area to map from 94,080 km² to 2,745 km²", "comparator": { "against": "mapping the whole national land area without the settlement maps (the normal Missing Maps approach)", "before": "94,080km2", "after": "2,745 km2", "basis": "measured", "quote": "Much of Malawi is rural, so concentrating on population settlements allowed us to filter out 97.1% of the total land area that we sought to map. This technique reduced our mapping area from 94,080km2 to 2,745 km2. Volunteers could then spend their time mapping communities instead of searching through acres of uninhabited areas; they were able to complete the mapping in a fraction of the normal time.", "url": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html" }, "later": { "status": "persisted", "date": "2017-08-10", "note": "After the June 2017 campaign the Red Cross's Missing Maps team reported that the traced base map underpinned a real-time monitoring dashboard covering 13,743 children, and again credited Facebook's population data with filtering uninhabited areas out of the tracing tasks.", "quote": "We are also grateful for Facebook’s high-resolution population data for Malawi, which helped us to identify areas with and without concentrations of people. This made the tracing much, much faster – we filtered uninhabited areas out of the tracing tasks so that we could direct remote mappers towards areas that had features for them to map.", "url": "https://www.missingmaps.org/blog/2017/08/10/malawi/" }, "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 1159 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1159", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Recast the monitoring dashboard as planned use because the 7 June 2017 article predates the June campaign (and dropped it from the organisational rating reason), replaced the unsourced Ministry of Health with the Measles & Rubella Initiative partners and the national government, changed “measles-rubella campaign” to the source's “measles vaccination campaign (Measles & Rubella Initiative)” in title, summary and account, corrected the developer to the preprint's affiliations (Facebook Inc., CIESIN at Columbia University, World Bank Development Data Group) with a note that the Red Cross names only “Facebook's population datasets”, and reclassified the Red Cross news post as “other”.", "source": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html (Wayback snapshot); https://arxiv.org/abs/1712.05839", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Task Time under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html; https://www.missingmaps.org/blog/2017/08/10/malawi/", "editor": "AIBD" }, { "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/American_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' preprint and two press releases from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0097", "catalog_version": "1.0.0", "source_fingerprint": "2e41991900b4e6881e7934ca89d0c360f5dae1a3f689ee5b484c9f91a82c83cd", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0097-source-0001", "AIBD-0097-source-0002", "AIBD-0097-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": "Facebook Inc. with CIESIN, Columbia University, and the World Bank Development Data Group", "deployer": "American Red Cross (Missing Maps) supporting the Measles and Rubella Initiative campaign in Malawi" } }, "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 Facebook settlement data determined where volunteer mapping effort went and which areas were skipped. The campaign itself was a Measles & Rubella Initiative effort supported by the American Red Cross and other partners, with monitors from the national government. The Red Cross attributes efficiency, not coverage, to the maps. The Red Cross account is the implementing organization's and names only “Facebook's population datasets”, without mentioning machine learning. The identification with the High Resolution Settlement Layer and the machine-learning description are taken from the vendor-authored Tiecke et al. preprint. It does not mention the Malawi campaign." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The Facebook settlement data determined where volunteer mapping effort went and which areas were skipped. The campaign itself was a Measles & Rubella Initiative effort supported by the American Red Cross and other partners, with monitors from the national government. The Red Cross attributes efficiency, not coverage, to the maps. The Red Cross account is the implementing organization's and names only “Facebook's population datasets”, without mentioning machine learning. The identification with the High Resolution Settlement Layer and the machine-learning description are taken from the vendor-authored Tiecke et al. preprint. It does not mention the Malawi campaign." } } }, "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": "The source reports no outcome for the 7.5 million targeted children. 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Data: A vital tool for disaster response.", "title": "Data: A vital tool for disaster response", "url": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html", "date": "2017-06-07", "written_by": "deployer", "quote": "This technique reduced our mapping area from 94,080km2 to 2,745 km2." }, { "kind": "preprint", "citation": "Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., et al. (2017). Mapping the world population one building at a time. arXiv:1712.05839.", "title": "Mapping the world population one building at a time", "url": "https://arxiv.org/abs/1712.05839", "date": "2017-12-15", "written_by": "makers", "quote": "a computer vision method based on machine learning to create population maps from satellite imagery at a global scale" }, { "kind": "press release", "citation": "Eros, E., Schmeltzer, A. (2017). Stop the spots: Measles vaccinations in Malawi. Missing Maps blog (American Red Cross), 10 August 2017.", "title": "Stop the spots: Measles vaccinations in Malawi", "url": "https://www.missingmaps.org/blog/2017/08/10/malawi/", "date": "2017-08-10", "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-0097-source-0001", "AIBD-0097-source-0002", "AIBD-0097-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": "American Red Cross (2017). Data: A vital tool for disaster response.", "title": "Data: A vital tool for disaster response", "url": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html", "date": "2017-06-07", "written_by": "deployer", "quote": "This technique reduced our mapping area from 94,080km2 to 2,745 km2." }, { "kind": "preprint", "citation": "Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., et al. (2017). Mapping the world population one building at a time. arXiv:1712.05839.", "title": "Mapping the world population one building at a time", "url": "https://arxiv.org/abs/1712.05839", "date": "2017-12-15", "written_by": "makers", "quote": "a computer vision method based on machine learning to create population maps from satellite imagery at a global scale" }, { "kind": "press release", "citation": "Eros, E., Schmeltzer, A. (2017). Stop the spots: Measles vaccinations in Malawi. Missing Maps blog (American Red Cross), 10 August 2017.", "title": "Stop the spots: Measles vaccinations in Malawi", "url": "https://www.missingmaps.org/blog/2017/08/10/malawi/", "date": "2017-08-10", "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": "Facebook's ML settlement maps cut Red Cross mapping for Malawi's June 2017 measles vaccination campaign (Measles & Rubella Initiative) from 94,080 km² to 2,745 km²", "account": "Malawi's June 2017 measles vaccination campaign (Measles & Rubella Initiative) targeted more than 7.5 million children. 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(Cureus, 7 February 2026) report an 87-year-old man with three days of dyspnea. He attended a basic emergency service of the Algarve Local Health Unit. Lunit INSIGHT CXR (Lunit Inc., Seoul) flagged bilateral pleural effusion on the chest radiograph. Its confidence was higher on the left (97%) than the right (77%), because the right-sided subpulmonic distribution mimicked hemidiaphragm elevation. Screening thoracic ultrasound by the radiographer confirmed a right subpulmonic effusion. The authors say this informed management and prevented misclassification as diaphragmatic elevation. He received nebulized bronchodilators. Despite advice and an indication for transfer to a referral hospital he refused, signed a discharge against medical advice and left. 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The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0274", "catalog_version": "1.0.0", "source_fingerprint": "163525e7ebda74d5f6153f3431a6dbcff028e59581765e12dc47c7a6a5d108ab", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0274-source-0001", "AIBD-0274-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": "Lunit Inc., Seoul", "deployer": "Basic Emergency Service, Vila Real de Santo Antonio Health Center, Algarve Local Health Unit" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI flagged the effusion correctly but with lower confidence on the subpulmonic side. 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(Cureus, 7 February 2026) report an 87-year-old man with three days of dyspnea. He attended a basic emergency service of the Algarve Local Health Unit. Lunit INSIGHT CXR (Lunit Inc., Seoul) flagged bilateral pleural effusion on the chest radiograph. Its confidence was higher on the left (97%) than the right (77%), because the right-sided subpulmonic distribution mimicked hemidiaphragm elevation. Screening thoracic ultrasound by the radiographer confirmed a right subpulmonic effusion. The authors say this informed management and prevented misclassification as diaphragmatic elevation. He received nebulized bronchodilators. Despite advice and an indication for transfer to a referral hospital he refused, signed a discharge against medical advice and left. The authors declare no financial support.", "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": 1, "reason": "Correct identification of a subtle subpulmonic effusion informed on-site management. 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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 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": "Country of the deployer added from the organization's own site or a registry.", "source": "https://pt.wikipedia.org/wiki/Unidade_Local_de_Saúde_do_Algarve", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.lunit.io/en/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; 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(Cureus, 7 February 2026) report an 87-year-old man with three days of dyspnea. He attended a basic emergency service of the Algarve Local Health Unit. Lunit INSIGHT CXR (Lunit Inc., Seoul) flagged bilateral pleural effusion on the chest radiograph. Its confidence was higher on the left (97%) than the right (77%), because the right-sided subpulmonic distribution mimicked hemidiaphragm elevation. Screening thoracic ultrasound by the radiographer confirmed a right subpulmonic effusion. The authors say this informed management and prevented misclassification as diaphragmatic elevation. He received nebulized bronchodilators. Despite advice and an indication for transfer to a referral hospital he refused, signed a discharge against medical advice and left. The authors declare no financial support." } }, "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": "Lunit INSIGHT CXR" } }, "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": "Lunit Inc., Seoul" } }, "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": "Lunit INSIGHT CXR flags a subpulmonic effusion in an 87-year-old; ultrasound confirms, patient refuses transfer", "headline": "Lunit INSIGHT CXR spots subpulmonic effusion; ultrasound agrees" } }, "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": [ "163525e7ebda74d5f6153f3431a6dbcff028e59581765e12dc47c7a6a5d108ab" ], "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": "Miravent et al. (Cureus, 7 February 2026) report an 87-year-old man with three days of dyspnea. He attended a basic emergency service of the Algarve Local Health Unit. Lunit INSIGHT CXR (Lunit Inc., Seoul) flagged bilateral pleural effusion on the chest radiograph. Its confidence was higher on the left (97%) than the right (77%), because the right-sided subpulmonic distribution mimicked hemidiaphragm elevation. Screening thoracic ultrasound by the radiographer confirmed a right subpulmonic effusion. The authors say this informed management and prevented misclassification as diaphragmatic elevation. He received nebulized bronchodilators. Despite advice and an indication for transfer to a referral hospital he refused, signed a discharge against medical advice and left. The authors declare no financial support.", "replication": { "produced_by": [ "AI chest-radiograph triage at a peripheral emergency service", "Radiographer-performed screening ultrasound to confirm" ], "must_hold": [ "Clinicians check lower-confidence AI flags with ultrasound", "Patients accept 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": 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-0293", "case_url": "records/AIBD-0293.html", "reason": "The developer is headquartered in Finland; the case names United Arab Emirates.", "details": [ { "label": "Developer headquarters", "value": "Finland" }, { "label": "Case jurisdictions", "value": "United Arab Emirates" }, { "label": "Case location", "value": "Dubai, United Arab Emirates" } ], "filters": { "origin": "Finland", "destination": [ "United Arab Emirates" ] }, "record": { "id": "AIBD-0293", "title": "Oura ring heart-rate and HRV trends inform a stress test and Holter that document frequent PVCs in a diabetic man in his early 40s; the ring raised no alert", "summary": "Dubai clinic case: Oura ring heart-rate and HRV summaries, read by clinicians, fed into a stress test and Holter showing 2,078 PVCs in 24 h; the ring detects no arrhythmia and issued no alert.", "date_observed": "2024-07-18", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Zakaria, Roman, Almarzooqi and Hashemi of GluCare.Health, Dubai, reported a case in Cureus (16 June 2025). A man in his early 40s had type 2 diabetes and a smoking history. He had been issued an ŌURA ring under the clinic's hybrid care model. He attended for cardiac risk assessment before half-marathon training. Per the paper, testing followed his risk profile, “which included poorly controlled diabetes and an elevated resting heart rate observed during wearable-based remote monitoring”. The treadmill stress test on 18 July 2024, the date the cardiologist made the diagnosis, showed frequent PVCs in bigeminy and abnormal heart-rate recovery. The team then reviewed the ring's previous two weeks of data retrospectively and found a persistently elevated daytime heart rate and a downward HRV trend. A 24-hour Holter recorded 2,078 ventricular events (1.75% of beats, no couplets or runs). The PVCs were judged idiopathic, of right ventricular outflow tract origin. Management was non-pharmacological (stop smoking, cut caffeine from over five cups of coffee a day, stress reduction). A 12-lead electrocardiogram (ECG) three days later showed no PVCs. Follow-up ring data showed resting heart rate falling from 83 to 74 bpm and RMSSD rising from 15.2 to 32.8 ms over two months. The authors say this supported deferring drug therapy. The paper states that the ring “does not detect arrhythmias like PVCs” and that data review “was conducted retrospectively”. It describes no alert and no machine-learning component. It is inconsistent on sequence. The case presentation says the stress-test findings prompted the retrospective ring review. The data-analysis section says the ring data “raised clinical suspicion and prompted further evaluation through ECG and Holter monitoring”.", "category": "benefit created", "unexpected": false, "purpose": "The ring tracks heart rate, HRV and sleep for consumers. The clinic used those daily summaries as remote-monitoring markers in its diabetes program. The paper does not claim the ring was built to find arrhythmias.", "system": "ŌURA ring (heart-rate and heart-rate-variability summaries) used within GluCare.Health's hybrid care model", "model_version": null, "developer": "Maker of the ŌURA ring (company not named in the paper)", "deployer": "GluCare.Health, Dubai", "location": "Dubai, United Arab Emirates", "domain": "Healthcare (cardiology; diabetes care)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 1, "reason": "One asymptomatic man had a low PVC burden (1.75%) characterized, judged benign, and managed with lifestyle change. The report documents no drug or procedure. The ring's share in prompting the work-up is stated inconsistently within the paper." } ], "ai_role": { "status": "assessed", "statement": "The ring generated no alert and, per the paper, cannot detect PVCs. Clinicians read its heart-rate and HRV summaries twice. Once was before the stress test (an elevated resting heart rate seen in remote monitoring is listed among the reasons for testing). Once was retrospectively afterwards. The ECG, stress test and Holter made the diagnosis. The paper describes no machine-learning component, so whether an AI system was involved is not shown by the source text. The only source is the treating clinic's case report." }, "evidence": [ { "kind": "case report", "citation": "Zakaria H, Roman I, Almarzooqi I, Hashemi A (2025). Integrating Wearables Into a Hybrid Care Model: Continuous Remote Monitoring of a Diabetic Patient With Frequent Premature Ventricular Contractions Using an ŌURA Ring. Cureus 17(6):e86174 (PMC12268106).", "title": "Integrating Wearables Into a Hybrid Care Model: Continuous Remote Monitoring of a Diabetic Patient With Frequent Premature Ventricular Contractions Using an ŌURA Ring", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "While the ŌURA ring does not detect arrhythmias like PVCs, it supplies surrogate physiological markers, such as heart rate and HRV, that can support early clinical insight" }, { "kind": "case report", "citation": "Zakaria et al. (2025), Cureus 17(6):e86174, abstract (on the timing of the data review).", "title": "Integrating Wearables Into a Hybrid Care Model", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "The patient wore an ŌURA ring before, during, and after a treadmill stress test; however, data review was conducted retrospectively." }, { "kind": "case report", "citation": "Zakaria et al. (2025), Cureus 17(6):e86174, ECG section (on why the stress test was ordered).", "title": "Integrating Wearables Into a Hybrid Care Model", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "a treadmill stress test was pursued due to his high-risk profile, which included poorly controlled diabetes and an elevated resting heart rate observed during wearable-based remote monitoring" } ], "replication": { "produced_by": [ "Continuous ring wear under a clinic hybrid care model in which staff review daily synced heart-rate and HRV summaries", "Risk factors (type 2 diabetes, smoking) that led to a treadmill stress test", "Access to ECG, 24-hour Holter and echocardiography to confirm and characterize the PVCs" ], "must_hold": [ "Clinicians act on wearable trend deviations. The clinic later wrote thresholds into its workflow (an RMSSD drop of more than 30% or a resting heart rate above 110 bpm prompts review)", "The patient keeps wearing and syncing the ring so that trends can be compared with a baseline" ], "echoes": [] }, "paired_incident": null, "headline": "Oura ring trends feed into a Holter showing 2,078 PVCs in 24 h", "metric": "a 24-hour Holter recorded 2,078 ventricular events (1.75% of beats, no couplets or runs)", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The paper's own two-month and four-month follow-up (resting heart rate 83 to 74 bpm, RMSSD 15.2 to 32.8 ms) belongs to the first report; nothing published after it was found.", "quote": null, "url": null }, "value": null, "developer_country": "Finland", "deployer_country": "United Arab Emirates", "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 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://ouraring.com/about-us; https://metabolic.health/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the deployer's case report. 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"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": "Zakaria, Roman, Almarzooqi and Hashemi of GluCare.Health, Dubai, reported a case in Cureus (16 June 2025). A man in his early 40s had type 2 diabetes and a smoking history. He had been issued an ŌURA ring under the clinic's hybrid care model. He attended for cardiac risk assessment before half-marathon training. Per the paper, testing followed his risk profile, “which included poorly controlled diabetes and an elevated resting heart rate observed during wearable-based remote monitoring”. The treadmill stress test on 18 July 2024, the date the cardiologist made the diagnosis, showed frequent PVCs in bigeminy and abnormal heart-rate recovery. The team then reviewed the ring's previous two weeks of data retrospectively and found a persistently elevated daytime heart rate and a downward HRV trend. A 24-hour Holter recorded 2,078 ventricular events (1.75% of beats, no couplets or runs). The PVCs were judged idiopathic, of right ventricular outflow tract origin. Management was non-pharmacological (stop smoking, cut caffeine from over five cups of coffee a day, stress reduction). A 12-lead electrocardiogram (ECG) three days later showed no PVCs. Follow-up ring data showed resting heart rate falling from 83 to 74 bpm and RMSSD rising from 15.2 to 32.8 ms over two months. The authors say this supported deferring drug therapy. The paper states that the ring “does not detect arrhythmias like PVCs” and that data review “was conducted retrospectively”. It describes no alert and no machine-learning component. It is inconsistent on sequence. The case presentation says the stress-test findings prompted the retrospective ring review. 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Integrating Wearables Into a Hybrid Care Model: Continuous Remote Monitoring of a Diabetic Patient With Frequent Premature Ventricular Contractions Using an ŌURA Ring. Cureus 17(6):e86174 (PMC12268106).", "title": "Integrating Wearables Into a Hybrid Care Model: Continuous Remote Monitoring of a Diabetic Patient With Frequent Premature Ventricular Contractions Using an ŌURA Ring", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "While the ŌURA ring does not detect arrhythmias like PVCs, it supplies surrogate physiological markers, such as heart rate and HRV, that can support early clinical insight" }, { "kind": "case report", "citation": "Zakaria et al. (2025), Cureus 17(6):e86174, abstract (on the timing of the data review).", "title": "Integrating Wearables Into a Hybrid Care Model", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "The patient wore an ŌURA ring before, during, and after a treadmill stress test; however, data review was conducted retrospectively." }, { "kind": "case report", "citation": "Zakaria et al. (2025), Cureus 17(6):e86174, ECG section (on why the stress test was ordered).", "title": "Integrating Wearables Into a Hybrid Care Model", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "a treadmill stress test was pursued due to his high-risk profile, which included poorly controlled diabetes and an elevated resting heart rate observed during wearable-based remote monitoring" } ] } }, "provenance": { "kind": "legacy_record", "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-0293-source-0001", "AIBD-0293-source-0002", "AIBD-0293-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": "case report", "citation": "Zakaria H, Roman I, Almarzooqi I, Hashemi A (2025). Integrating Wearables Into a Hybrid Care Model: Continuous Remote Monitoring of a Diabetic Patient With Frequent Premature Ventricular Contractions Using an ŌURA Ring. Cureus 17(6):e86174 (PMC12268106).", "title": "Integrating Wearables Into a Hybrid Care Model: Continuous Remote Monitoring of a Diabetic Patient With Frequent Premature Ventricular Contractions Using an ŌURA Ring", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "While the ŌURA ring does not detect arrhythmias like PVCs, it supplies surrogate physiological markers, such as heart rate and HRV, that can support early clinical insight" }, { "kind": "case report", "citation": "Zakaria et al. (2025), Cureus 17(6):e86174, abstract (on the timing of the data review).", "title": "Integrating Wearables Into a Hybrid Care Model", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "The patient wore an ŌURA ring before, during, and after a treadmill stress test; however, data review was conducted retrospectively." }, { "kind": "case report", "citation": "Zakaria et al. (2025), Cureus 17(6):e86174, ECG section (on why the stress test was ordered).", "title": "Integrating Wearables Into a Hybrid Care Model", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12268106/", "date": "2025-06-16", "written_by": "deployer", "quote": "a treadmill stress test was pursued due to his high-risk profile, which included poorly controlled diabetes and an elevated resting heart rate observed during wearable-based remote monitoring" } ] } }, "provenance": { "kind": "legacy_record", "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": "Dubai clinic case: Oura ring heart-rate and HRV summaries, read by clinicians, fed into a stress test and Holter showing 2,078 PVCs in 24 h; the ring detects no arrhythmia and issued no alert.", "account": "Zakaria, Roman, Almarzooqi and Hashemi of GluCare.Health, Dubai, reported a case in Cureus (16 June 2025). A man in his early 40s had type 2 diabetes and a smoking history. He had been issued an ŌURA ring under the clinic's hybrid care model. He attended for cardiac risk assessment before half-marathon training. Per the paper, testing followed his risk profile, “which included poorly controlled diabetes and an elevated resting heart rate observed during wearable-based remote monitoring”. The treadmill stress test on 18 July 2024, the date the cardiologist made the diagnosis, showed frequent PVCs in bigeminy and abnormal heart-rate recovery. The team then reviewed the ring's previous two weeks of data retrospectively and found a persistently elevated daytime heart rate and a downward HRV trend. A 24-hour Holter recorded 2,078 ventricular events (1.75% of beats, no couplets or runs). The PVCs were judged idiopathic, of right ventricular outflow tract origin. Management was non-pharmacological (stop smoking, cut caffeine from over five cups of coffee a day, stress reduction). A 12-lead electrocardiogram (ECG) three days later showed no PVCs. Follow-up ring data showed resting heart rate falling from 83 to 74 bpm and RMSSD rising from 15.2 to 32.8 ms over two months. The authors say this supported deferring drug therapy. The paper states that the ring “does not detect arrhythmias like PVCs” and that data review “was conducted retrospectively”. It describes no alert and no machine-learning component. It is inconsistent on sequence. The case presentation says the stress-test findings prompted the retrospective ring review. The data-analysis section says the ring data “raised clinical suspicion and prompted further evaluation through ECG and Holter monitoring”." } }, "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": "ŌURA ring (heart-rate and heart-rate-variability summaries) used within GluCare.Health's hybrid care model" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Maker of the ŌURA ring (company not named in the paper)" } }, "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": "Oura ring heart-rate and HRV trends inform a stress test and Holter that document frequent PVCs in a diabetic man in his early 40s; the ring raised no alert", "headline": "Oura ring trends feed into a Holter showing 2,078 PVCs in 24 h" } }, "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": [ "d3b952e1827d533843bd541c633002a98a2acd8aef57b333f5f380db633c8b83" ], "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": "Zakaria, Roman, Almarzooqi and Hashemi of GluCare.Health, Dubai, reported a case in Cureus (16 June 2025). A man in his early 40s had type 2 diabetes and a smoking history. He had been issued an ŌURA ring under the clinic's hybrid care model. He attended for cardiac risk assessment before half-marathon training. Per the paper, testing followed his risk profile, “which included poorly controlled diabetes and an elevated resting heart rate observed during wearable-based remote monitoring”. The treadmill stress test on 18 July 2024, the date the cardiologist made the diagnosis, showed frequent PVCs in bigeminy and abnormal heart-rate recovery. The team then reviewed the ring's previous two weeks of data retrospectively and found a persistently elevated daytime heart rate and a downward HRV trend. A 24-hour Holter recorded 2,078 ventricular events (1.75% of beats, no couplets or runs). The PVCs were judged idiopathic, of right ventricular outflow tract origin. Management was non-pharmacological (stop smoking, cut caffeine from over five cups of coffee a day, stress reduction). A 12-lead electrocardiogram (ECG) three days later showed no PVCs. Follow-up ring data showed resting heart rate falling from 83 to 74 bpm and RMSSD rising from 15.2 to 32.8 ms over two months. The authors say this supported deferring drug therapy. The paper states that the ring “does not detect arrhythmias like PVCs” and that data review “was conducted retrospectively”. It describes no alert and no machine-learning component. It is inconsistent on sequence. The case presentation says the stress-test findings prompted the retrospective ring review. The data-analysis section says the ring data “raised clinical suspicion and prompted further evaluation through ECG and Holter monitoring”.", "replication": { "produced_by": [ "Continuous ring wear under a clinic hybrid care model in which staff review daily synced heart-rate and HRV summaries", "Risk factors (type 2 diabetes, smoking) that led to a treadmill stress test", "Access to ECG, 24-hour Holter and echocardiography to confirm and characterize the PVCs" ], "must_hold": [ "Clinicians act on wearable trend deviations. The clinic later wrote thresholds into its workflow (an RMSSD drop of more than 30% or a resting heart rate above 110 bpm prompts review)", "The patient keeps wearing and syncing the ring so that trends can be compared with a baseline" ], "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-0294", "case_url": "records/AIBD-0294.html", "reason": "The developer is headquartered in United States; the case names Japan.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Japan" }, { "label": "Case location", "value": "Kanazawa, Japan" } ], "filters": { "origin": "United States", "destination": [ "Japan" ] }, "record": { "id": "AIBD-0294", "title": "Fitbit Charge 3 oxygen-variation signals precede COVID-19 exacerbation in 4 of 5 cases and reveal severe sleep apnoea in one patient (DEXTERITY pilot, Kanazawa)", "summary": "Kanazawa pilot, 23 COVID-19 patients (2020): Fitbit oxygen-variation signals preceded 4 of 5 exacerbations (sensitivity 80%); one man's persistent signals led to polysomnography showing severe OSAS.", "date_observed": "2021-09-16", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Yamagami, Nomura and colleagues published the DEXTERITY pilot in JMIR Formative Research on 16 September 2021. They are at Kanazawa University and Japan Community Health Care Organization (JCHO) Kanazawa Hospital. The study ran from August to November 2020. The individual event is undated. Twenty-three patients with PCR-confirmed COVID-19 wore a Fitbit Charge 3 for 30 days. The investigators defined a high “estimated oxygen variation” (EOV) signal as a significant dip and recovery of the oxygen level within the index period. A prediction counted as successful if the signal appeared within two days before an exacerbation. Exacerbation occurred in six patients (seven events). EOV signals preceded four of five analyzed cases (sensitivity 80%, specificity 90%), better than resting-heart-rate signals (50% and 80%). Separately, patient JCHO-023, a 53-year-old man with hypertension recovered from COVID-19, showed “a long-lasting high EOV signal” from days 18 to 29 without symptoms. The team suspected sleep apnea. A portable polysomnogram (Nihon Kohden SAS-2200) gave a 3% oxygen desaturation index (ODI) of 33.6 per hour, severe obstructive sleep apnea syndrome. The paper reports no treatment for him. The authors note the Fitbit's functions and analysis algorithms were not approved as a medical device at the time. They note that baseline data were short and the five exacerbation cases are too few to generalize. The prediction analysis was defined against outcomes after the fact rather than as live alerts.", "category": "benefit created", "unexpected": true, "purpose": "Fitbit's EOV metric summarizes overnight oxygen variation for consumers. The investigators repurposed it to flag COVID-19 deterioration, and the sleep-apnea finding was incidental to that study aim.", "system": "Fitbit Charge 3 estimated oxygen variation (EOV) and resting heart rate, with investigator-defined signal thresholds", "model_version": "Fitbit Charge 3", "developer": "Fitbit (device and EOV algorithm); Kanazawa University investigators (signal definitions)", "deployer": "Kanazawa University and JCHO Kanazawa Hospital (DEXTERITY pilot, UMIN000041421)", "location": "Kanazawa, Japan", "domain": "Healthcare (infectious disease; sleep medicine)", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 1, "reason": "One man received a diagnosis of severe obstructive sleep apnea (3% ODI 33.6/h) from an incidental wearable signal. The paper reports no treatment or follow-up for him." }, { "group": "organizational", "depth": null, "reason": "The 80% sensitivity for predicting exacerbation is a retrospective pilot result on five events, which the authors say is too small to generalize. No live alerting or changed care is documented." } ], "ai_role": { "status": "assessed", "statement": "The device produced the EOV values. The investigators' own rule flagged high signals, and clinicians decided to order polysomnography for the sleep-apnea patient. The paper does not say whether Fitbit's EOV estimation uses machine learning and notes the functions were not approved as a medical device. The COVID-19 prediction figures were computed after the outcomes were known, not from alerts acted on in real time." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Yamagami K, Nomura A, Kometani M, Shimojima M, Sakata K, Usui S, Furukawa K, Takamura M, Okajima M, Watanabe K, Yoneda T (2021). Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY): Pilot Evaluation. JMIR Formative Research 5(9):e30819 (PMC8448084).", "title": "Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY): Pilot Evaluation", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8448084/", "date": "2021-09-16", "written_by": "makers", "quote": "Coincidental obstructive sleep apnea syndrome confirmed by polysomnography was detected in 1 patient via consistently high EOV signals." }, { "kind": "peer-reviewed study", "citation": "Yamagami et al. (2021), JMIR Form Res 5(9):e30819, results.", "title": "Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY)", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8448084/", "date": "2021-09-16", "written_by": "makers", "quote": "We were successful in predicting exacerbation using EOV signals in 4 out of 5 cases (sensitivity=80%, specificity=90%)" }, { "kind": "peer-reviewed study", "citation": "Yamagami et al. (2021), JMIR Form Res 5(9):e30819, representative case 3 (JCHO-023).", "title": "Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY)", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8448084/", "date": "2021-09-16", "written_by": "makers", "quote": "His 3% oxygen desaturation index was 33.6 per hour, indicating severe OSAS." }, { "kind": "peer-reviewed study", "citation": "Yamagami et al. (2021), JMIR Form Res 5(9):e30819, limitations.", "title": "Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY)", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8448084/", "date": "2021-09-16", "written_by": "makers", "quote": "the Fitbit wearable device, including its functions (eg, EOV) and analysis algorithms, was not yet approved as a medical device at the time of this study" } ], "replication": { "produced_by": [ "A Fitbit Charge 3 worn for 30 days by patients with PCR-confirmed COVID-19, giving EOV and resting heart rate", "An investigator-defined high-EOV signal (a significant dip and recovery of the oxygen level within the index period) reviewed by the study team", "Weekly phone calls to discharged patients and access to portable polysomnography" ], "must_hold": [ "Patients can pair the device with a smartphone app (elderly patients unable to use a smartphone were not enrolled)", "Enough baseline data to separate abnormal signals from normal variation, which the authors list as lacking in the 30-day window" ], "echoes": [] }, "paired_incident": null, "headline": "Fitbit Charge 3 signals precede 4 of 5 COVID-19 exacerbations", "metric": "EOV signals preceded four of five analyzed cases (sensitivity 80%, specificity 90%)", "comparator": { "against": "high resting-heart-rate (RHR) signals from the same Fitbit data", "before": "the sensitivity and specificity of high RHR signals were 50% and 80%, respectively", "after": "4 out of 5 cases (sensitivity=80%, specificity=90%)", "basis": "measured", "quote": "We were successful in predicting exacerbation using EOV signals in 4 out of 5 cases (sensitivity=80%, specificity=90%), whereas the sensitivity and specificity of high RHR signals were 50% and 80%, respectively, both lower than those of high EOV signals.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8448084/" }, "later": { "status": "unknown", "date": null, "note": "No later DEXTERITY publication and no follow-up on patient JCHO-023 was found in the cited paper.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "Japan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to 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://pmc.ncbi.nlm.nih.gov/articles/PMC8448084/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://support.google.com/product-documentation/answer/14816019; https://en.wikipedia.org/wiki/Kanazawa_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" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: the makers' peer-reviewed study. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0294", "catalog_version": "1.0.0", "source_fingerprint": "1c723e06aa36a32592a37c7e4605951c8f977581c24cf09083e762676a11cecd", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0294-source-0001", "AIBD-0294-source-0002", "AIBD-0294-source-0003", "AIBD-0294-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": "Fitbit (device and EOV algorithm); Kanazawa University investigators (signal definitions)", "deployer": "Kanazawa University and JCHO Kanazawa Hospital (DEXTERITY pilot, UMIN000041421)" } }, "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 device produced the EOV values. The investigators' own rule flagged high signals, and clinicians decided to order polysomnography for the sleep-apnea patient. The paper does not say whether Fitbit's EOV estimation uses machine learning and notes the functions were not approved as a medical device. The COVID-19 prediction figures were computed after the outcomes were known, not from alerts acted on in real time." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The device produced the EOV values. The investigators' own rule flagged high signals, and clinicians decided to order polysomnography for the sleep-apnea patient. The paper does not say whether Fitbit's EOV estimation uses machine learning and notes the functions were not approved as a medical device. The COVID-19 prediction figures were computed after the outcomes were known, not from alerts acted on in real time." } } }, "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": "One man received a diagnosis of severe obstructive sleep apnea (3% ODI 33.6/h) from an incidental wearable signal. The paper reports no treatment or follow-up for him." }, { "group": "organizational", "reason": "The 80% sensitivity for predicting exacerbation is a retrospective pilot result on five events, which the authors say is too small to generalize. No live alerting or changed care 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": "low", "evidence_word": "claimed", "claim_status": "The makers' claim", "basis": "Sources: the makers' peer-reviewed study. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "One man received a diagnosis of severe obstructive sleep apnea (3% ODI 33.6/h) from an incidental wearable signal. The paper reports no treatment or follow-up for him." }, { "group": "organizational", "reason": "The 80% sensitivity for predicting exacerbation is a retrospective pilot result on five events, which the authors say is too small to generalize. 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They are at Kanazawa University and Japan Community Health Care Organization (JCHO) Kanazawa Hospital. The study ran from August to November 2020. The individual event is undated. Twenty-three patients with PCR-confirmed COVID-19 wore a Fitbit Charge 3 for 30 days. The investigators defined a high “estimated oxygen variation” (EOV) signal as a significant dip and recovery of the oxygen level within the index period. A prediction counted as successful if the signal appeared within two days before an exacerbation. Exacerbation occurred in six patients (seven events). EOV signals preceded four of five analyzed cases (sensitivity 80%, specificity 90%), better than resting-heart-rate signals (50% and 80%). Separately, patient JCHO-023, a 53-year-old man with hypertension recovered from COVID-19, showed “a long-lasting high EOV signal” from days 18 to 29 without symptoms. The team suspected sleep apnea. A portable polysomnogram (Nihon Kohden SAS-2200) gave a 3% oxygen desaturation index (ODI) of 33.6 per hour, severe obstructive sleep apnea syndrome. The paper reports no treatment for him. The authors note the Fitbit's functions and analysis algorithms were not approved as a medical device at the time. They note that baseline data were short and the five exacerbation cases are too few to generalize. 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Their June 8, 2026 paper reports 298 scans from 68 specimens, including preserved specimens and fresh shark material. The researchers expanded the scans by inserting specimen images into baggage images. A 512-bag test set contained both original scans and these synthetic composites, drawn from the same specimen population. Table 3 reports detection rates of 95% for shark fins, 96% for seahorses, and 86% for sea cucumbers. The overall false-alarm rate was 13%; the class-specific rates were 2%, 9%, and 1%. Rapiscan supplied equipment and funded staff participation in analysis and writing. 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Frontiers in Ocean Sustainability. doi:10.3389/focsu.2026.1776978; §§2–3, tables 1–3, data availability, and conflict-of-interest statement.", "title": "Marine wildlife trafficking: use of AI algorithms for the autodetection of shark fins, seahorses and sea cucumbers", "url": "https://www.frontiersin.org/journals/ocean-sustainability/articles/10.3389/focsu.2026.1776978/full", "date": "2026-06-08", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Rapiscan 3D X-ray CT scanners already used for security screening", "Preserved marine specimens and fresh shark material scanned with the Rapiscan RTT110", "Synthetic data expansion with 3D Threat Image Projection", "Vendor funding of the research" ], "must_hold": [ "Access to CT scanners at mail and airport screening points", "Algorithm performance on concealed, mixed consignments", "Customs authority adoption", "Legal basis for seizure on algorithm alerts" ], "echoes": [] }, "paired_incident": null, "headline": "Rapiscan CT algorithms detect marine wildlife at 86–96% in testing", "metric": "detection rates of 95% (shark fin), 96% (seahorse) and 86% (sea cucumber)", "comparator": { "against": "known scanned samples (ground truth), not human inspectors", "before": null, "after": "PD shark fin: 95%, seahorse: 96% and sea cucumber: 86%", "basis": "measured", "quote": "The performance of the detection algorithm across all marine species subclasses was high (Table 3, PD shark fin: 95%, seahorse: 96% and sea cucumber: 86%), with low false alarm rates (PFA shark fin: 2%, seahorse: 9% and sea cucumber: 1%).", "url": "https://www.frontiersin.org/articles/10.3389/focsu.2026.1776978/full" }, "later": { "status": "unknown", "date": null, "note": "The authors say implementation at the front lines is the next step; no customs trial or deployment of the algorithms is reported in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United Kingdom", "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.frontiersin.org/articles/10.3389/focsu.2026.1776978/full", "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 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 added from the organization's own site or a registry.", "source": "https://find-and-update.company-information.service.gov.uk/company/02755398", "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" }, { "date": "2026-10-24", "change": "Replaced two derivative Conversation excerpts with the original study alone. 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Their June 8, 2026 paper reports 298 scans from 68 specimens, including preserved specimens and fresh shark material. The researchers expanded the scans by inserting specimen images into baggage images. A 512-bag test set contained both original scans and these synthetic composites, drawn from the same specimen population. Table 3 reports detection rates of 95% for shark fins, 96% for seahorses, and 86% for sea cucumbers. The overall false-alarm rate was 13%; the class-specific rates were 2%, 9%, and 1%. Rapiscan supplied equipment and funded staff participation in analysis and writing. 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It states that 2025 marked the first time NHC incorporated AI-based models into real-time operations. In the Atlantic track comparison, the Google DeepMind ensemble mean (GDMI) slightly outperformed the official forecast at lead times from 12 to 72 h. Official forecasts outperformed the consensus aids TVCA, HCCA and FSSE. In the eastern North Pacific, GDMI performed considerably better than every other individual model. It beat the official forecast and all consensus aids at 48–120 h. For intensity it was the top individual model from 12 to 48 h. 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The sample excludes early-season storms, when GDMI was unavailable." }, "evidence": [ { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season, Section 1 (Introduction), p. 7. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season (AI models statement)", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "The 2025 season marked the first time that NHC incorporated AI-based models into real-time operations." }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season, Atlantic track verification. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season (Atlantic track)", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "there was one model, the Google DeepMind ensemble mean (GDMI), that slightly outperformed NHC at the shorter lead times from 12 to 72 h" }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season, eastern North Pacific track verification. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season (eastern North Pacific track)", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "GDMI performed considerably better than all of the other individual models and beat the official forecast and all of the consensus aids at 48-120 h." }, { "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": "NHC has partnered directly with Google DeepMind to develop a new AI hurricane forecast model that was used experimentally during the 2025 season." } ], "replication": { "produced_by": [ "Real-time availability of the Google DeepMind ensemble to NHC for most of the 2025 season", "NHC's standard homogeneous verification procedure applied to early-model versions of the guidance" ], "must_hold": [ "Consistent timely delivery of the AI guidance (NHC notes some AI systems were not consistently available)", "Results from one season. NHC's Science Operations Officer says it takes a long time to fully evaluate new models" ], "echoes": [ { "date": "2024-09-08", "setting": "Independent CIRA evaluation of earlier open-source AI models (GraphCast-operational, Pangu-Weather, FourCastNet) on May-November 2023 cyclones: track errors comparable to best operational models", "model_version": "GraphCast-operational, Pangu-Weather, FourCastNet v1/v2-small", "outcome": "recurred", "source": "https://arxiv.org/abs/2409.06735" }, { "date": "2025-10-23", "setting": "Hurricane Melissa: NHC blended GDMI into official track and intensity forecasts", "model_version": null, "outcome": "recurred", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml" } ] }, "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": "National Weather Service storm-surge model underestimated Hurricane Sandy's threat and NJ Transit lost rail equipment; NHC's hurricane forecast guidance serves the same operational decisions." }, "headline": "DeepMind's GDMI beats NHC Atlantic track forecasts at 12–72 h", "metric": "beat official Atlantic track forecasts at 12–72 h", "comparator": { "against": "the NHC official track forecast (OFCL) in the 2025 Atlantic homogeneous model comparison", "before": "OFCL errors of 20.4, 31.1, 41.9, 49.2, 62.2 and 81.4 n mi at 12, 24, 36, 48, 60 and 72 h", "after": "GDMI errors of 19.6, 26.4, 34.8, 41.4, 50.8 and 66.5 n mi at the same lead times", "basis": "measured", "quote": "The official forecasts were highly skillful, even outperforming the typically reliable consensus aids TVCA, HCCA, and FSSE. 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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": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Family member with medical training prompting a general-purpose model for literature summaries", "Standard FOLFIRINOX chemotherapy" ], "must_hold": [ "Treating oncologists remain responsible for decisions", "Model outputs verified against sources" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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Int Med Case Rep J (2026), discussion", "title": "Complete Regression of 11 Hepatic Metastases in Metastatic Pancreatic Adenosquamous Carcinoma and the Role of GPT in Patient Navigation", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13644719/", "date": "2026-01-01", "written_by": "deployer", "quote": "this single case cannot determine the contribution of those adjunctive interventions to the observed radiological response." } ] } }, "provenance": { "kind": "legacy_record", "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-0272-source-0001", "AIBD-0272-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": "case report", "citation": "Khalil ST. Int Med Case Rep J (2026), doi 10.2147/IMCRJ.S639766", "title": "Complete Regression of 11 Hepatic Metastases in Metastatic Pancreatic Adenosquamous Carcinoma and the Role of GPT in Patient Navigation", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13644719/", "date": "2026-01-01", "written_by": "deployer", "quote": "GPT materially influenced how we navigated information and considered supportive options" }, { "kind": "case report", "citation": "Khalil ST. 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He reports a 65-year-old with metastatic pancreatic adenosquamous carcinoma and 11 hepatic metastases treated with FOLFIRINOX. The patient and author added high-dose intravenous vitamin C, doxycycline and later azithromycin as off-label home measures. After three cycles magnetic resonance imaging (MRI) showed complete radiological regression of the hepatic lesions, confirmed around the sixth cycle. Chemotherapy stopped after six cycles. Ursodeoxycholic acid in late September 2025 resolved cholestasis. The author and patient used ChatGPT, initially an earlier model and later GPT-5. They used it to search and summarize literature, interpret CA 19–9 during cholestasis and appraise data on adjunctive interventions. The author writes that GPT “materially influenced how we navigated information”. He also states that the single case cannot determine the contribution of those interventions to the response. No attributable change in diagnosis or treatment can be established from the sources on record. The clinical course ran through 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": "ChatGPT helped the author find and summarize relevant literature." }, "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 later phase used GPT-5. The earlier phase’s model is not identified in the existing account." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "GPT-5 in the later phase." }, "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-5 (later phase); earlier model unspecified" } }, "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 (an earlier model, later 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": "Relative uses ChatGPT to navigate literature during FOLFIRINOX; pancreatic liver metastases regress, contribution undetermined", "headline": "Nephew uses ChatGPT during FOLFIRINOX; liver metastases regress" } }, "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": [ "21651f0f623c4aad576aeb1a62c3c343404831855be984da6a47494c2d41f904" ], "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": "Khalil (International Medical Case Reports Journal, 2026) is a dentist and the patient's nephew writing as sole author. He reports a 65-year-old with metastatic pancreatic adenosquamous carcinoma and 11 hepatic metastases treated with FOLFIRINOX. The patient and author added high-dose intravenous vitamin C, doxycycline and later azithromycin as off-label home measures. After three cycles magnetic resonance imaging (MRI) showed complete radiological regression of the hepatic lesions, confirmed around the sixth cycle. Chemotherapy stopped after six cycles. Ursodeoxycholic acid in late September 2025 resolved cholestasis. The author and patient used ChatGPT, initially an earlier model and later GPT-5. They used it to search and summarize literature, interpret CA 19–9 during cholestasis and appraise data on adjunctive interventions. The author writes that GPT “materially influenced how we navigated information”. He also states that the single case cannot determine the contribution of those interventions to the response. No attributable change in diagnosis or treatment can be established from the sources on record. The clinical course ran through 2025.", "replication": { "produced_by": [ "Family member with medical training prompting a general-purpose model for literature summaries", "Standard FOLFIRINOX chemotherapy" ], "must_hold": [ "Treating oncologists remain responsible for decisions", "Model outputs verified against sources" ], "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-0271", "case_url": "records/AIBD-0271.html", "reason": "The developer is headquartered in United States; the case names Italy.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Italy" }, { "label": "Case location", "value": "Sassari, Italy" } ], "filters": { "origin": "United States", "destination": [ "Italy" ] }, "record": { "id": "AIBD-0271", "title": "GPT-5 names caruncle oncocytoma from slit-lamp photos in two retrospective cases, months after histology", "summary": "Sassari ophthalmologists gave ChatGPT (GPT-5) slit-lamp photos and brief summaries of two surgically confirmed caruncular oncocytomas; it proposed oncocytoma first in both. Retrospective proof only.", "date_observed": "2025-09-08", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Sacchi et al. (Frontiers in Ophthalmology, 24 April 2026) retrospectively analyzed two cases of caruncular oncocytoma. Both had been excised and confirmed histopathologically. ChatGPT (GPT-5, 2025 version) received only slit-lamp photographs and a brief summary (age, sex, site). It proposed oncocytoma as the primary hypothesis in both. Differentials included papilloma, nevus, squamous cell carcinoma and melanoma. The authors call it a proof of concept that a general-purpose multimodal model can recognize a rare ocular surface tumor, limited by the sample size. The analysis was run on 8 September 2025, months after confirmation, with no effect on care. The authors declare no competing interests. Publication was 24 April 2026.", "category": "general benefit", "unexpected": false, "purpose": "Test whether a multimodal large language model can generate diagnostic hypotheses from slit-lamp images", "system": "ChatGPT (GPT-5, 2025 version)", "model_version": "GPT-5 (2025)", "developer": "OpenAI", "deployer": "Department of Medicine, Surgery and Pharmacy, University of Sassari", "location": "Sassari, Italy", "domain": "Health; ophthalmology", "segment": "Patients with rare ocular adnexal tumors (retrospective)", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "A retrospective diagnostic match with no effect on care is read as task performance.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No effect on the two patients, whose diagnoses were already confirmed." }, { "group": "organizational", "depth": null, "reason": "No organizational outcome." }, { "group": "societal", "depth": null, "reason": "No attributable event." } ], "ai_role": { "status": "assessed", "statement": "GPT-5 produced the correct primary hypothesis in two retrospective cases when given images and minimal clinical data. 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Front Ophthalmol 6:1772378 (2026)", "title": "Artificial intelligence-assisted diagnosis of ocular caruncle oncocytoma: a proof-of-concept case report of two cases", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13169155/", "date": "2026-04-24", "written_by": "deployer", "quote": "In both cases, ChatGPT proposed oncocytoma as the primary diagnostic hypothesis." } ], "replication": { "produced_by": [ "Slit-lamp photographs plus a brief clinical summary submitted to a multimodal model" ], "must_hold": [ "Prospective testing before any clinical use", "Larger samples than two cases" ], "echoes": [] }, "paired_incident": null, "headline": "GPT-5 proposes oncocytoma first in two retrospective cases", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "The report is a retrospective proof of concept on two histologically confirmed cases; the cited source describes no follow-up and the authors call for larger validation and benchmarking against human experts.", "quote": null, "url": null }, "value": null, "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": "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 deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/University_of_Sassari", "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" }, { "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: the deployer's case report. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0271", "catalog_version": "1.0.0", "source_fingerprint": "e45032abd8e277be565ad6008937d5103e5991ce9f69105cda2acc29a8596177", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0271-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": "OpenAI", "deployer": "Department of Medicine, Surgery and Pharmacy, University of Sassari" } }, "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": "GPT-5 produced the correct primary hypothesis in two retrospective cases when given images and minimal clinical data. 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(Frontiers in Ophthalmology, 24 April 2026) retrospectively analyzed two cases of caruncular oncocytoma. Both had been excised and confirmed histopathologically. ChatGPT (GPT-5, 2025 version) received only slit-lamp photographs and a brief summary (age, sex, site). It proposed oncocytoma as the primary hypothesis in both. Differentials included papilloma, nevus, squamous cell carcinoma and melanoma. The authors call it a proof of concept that a general-purpose multimodal model can recognize a rare ocular surface tumor, limited by the sample size. The analysis was run on 8 September 2025, months after confirmation, with no effect on care. The authors declare no competing interests. Publication was 24 April 2026.", "replication": { "produced_by": [ "Slit-lamp photographs plus a brief clinical summary submitted to a multimodal model" ], "must_hold": [ "Prospective testing before any clinical use", "Larger samples than two cases" ], "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-0351", "case_url": "records/AIBD-0351.html", "reason": "The developer is headquartered in United States; the case names Malawi.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Malawi" }, { "label": "Case location", "value": "Malawi (the explain-AI post names Mzuzu)" } ], "filters": { "origin": "United States", "destination": [ "Malawi" ] }, "record": { "id": "AIBD-0351", "title": "GiveDirectly's hybrid MobileAid program in Malawi may have enrolled 35% of recipients by phone-metadata machine learning and halved enrollment cost", "summary": "GiveDirectly posts (2024–2025) describe phone-metadata AI/ML targeting in Malawi: 35% of recipients enrolled via phone data, cost per enrolee halved; no date, count or independent source.", "date_observed": "2025-09-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "GiveDirectly's MobileAid approach uses machine learning (ML) on mobile-phone usage patterns to identify people in need and pay them by mobile money. Its 4 September 2025 post (Vera Lummis) says it has evaluated the method across four countries since 2021 (Togo, DRC, Malawi and Bangladesh). In Malawi it ran a hybrid program identifying and enrolling 35% of recipients via phone metadata and 65% in person. It says this halved the cost of identifying and enrolling each additional person compared with a fully in-person operation. It is working with the Government of Malawi to fill gaps in the urban social registry with phone metadata. A study with the Government of Malawi, U.C. Berkeley and U.C. San Diego is expected to report in 2026. It asks whether phone metadata can flag people whose poverty deepens after a shock. Its 27 January 2025 post (Nirali Desai and Vera Lummis) describes interviews with 67 recipients across Malawi and Kenya in February and October 2024. The interviews, naming Mzuzu, asked how to explain AI/ML targeting. It finds scenario-based explanations most understandable. No cited source gives the Malawi program's dates, location, number of recipients or an independent evaluation.", "category": "general benefit", "unexpected": false, "purpose": "Identify people in poverty from mobile-phone metadata with machine learning so that cash can be delivered faster and more cheaply. The claimed benefit (cheaper, faster enrollment) is that purpose.", "system": "GiveDirectly MobileAid: machine-learning poverty targeting from mobile-phone metadata", "model_version": null, "developer": "GiveDirectly (MobileAid), with research partners; for the Malawi study the Government of Malawi, U.C. Berkeley and U.C. San Diego", "deployer": "GiveDirectly, with the Government of Malawi for the urban social registry work", "location": "Malawi (the explain-AI post names Mzuzu)", "domain": "Humanitarian cash transfers / poverty targeting", "segment": "Non-governmental cash-transfer organization working with a national government", "benefit_type": "Improved Organizational Efficiency", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No dated event, named recipient or outcome for Malawi is given in any cited source." }, { "group": "organizational", "depth": null, "reason": "The halved enrollment cost is the maker's own undated statement without figures or an independent evaluation." }, { "group": "societal", "depth": null, "reason": "No population-level outcome is documented for Malawi." } ], "ai_role": { "status": "assessed", "statement": "Only the maker's account is among the sources. GiveDirectly says machine learning on phone metadata identified and enrolled 35% of recipients in a Malawi hybrid program. It says this halved the cost per additional enrolee. It gives no date, place, recipient count or accuracy for Malawi. The Government of Malawi, U.C. Berkeley and U.C. San Diego study is described as pending (findings expected in 2026). Likewise, the explain-AI page names no deployment, date or outcome." }, "evidence": [ { "kind": "press release", "citation": "Lummis, V. (2025). Five things we've learned using mobile data and AI/ML to identify people in need. GiveDirectly blog, 4 September 2025.", "title": "Five things we've learned using mobile data and AI/ML to identify people in need", "url": "https://www.givedirectly.org/mobileaid", "date": "2025-09-04", "written_by": "makers", "quote": "So in Malawi, we ran a hybrid program: we identified and enrolled 35% of recipients via phone metadata and the other 65% in person." }, { "kind": "press release", "citation": "Lummis, V. (2025). Five things we've learned using mobile data and AI/ML to identify people in need. GiveDirectly blog, 4 September 2025 (cost statement).", "title": "Five things we've learned using mobile data and AI/ML to identify people in need (cost statement)", "url": "https://www.givedirectly.org/mobileaid", "date": "2025-09-04", "written_by": "makers", "quote": "This combined approach still cut the cost of identifying and enrolling each additional person in half, compared to a fully in-person operation." }, { "kind": "press release", "citation": "Desai, N. & Lummis, V. (2025). How to explain AI/ML across the digital divide. GiveDirectly blog, 27 January 2025.", "title": "Explain AI/ML systems to people in poverty using real-world scenarios", "url": "https://www.givedirectly.org/explain_ai", "date": "2025-01-27", "written_by": "makers", "quote": "we interviewed 67 GiveDirectly recipients across Malawi and Kenya in February and October 2024" }, { "kind": "press release", "citation": "Lummis, V., Luk, S. & Ramprasad, S. (2024). GiveDirectly's approach to responsible AI/ML. GiveDirectly blog, 8 July 2024.", "title": "GiveDirectly's approach to responsible AI/ML", "url": "https://www.givedirectly.org/ai-framework", "date": "2024-07-08", "written_by": "makers", "quote": "interviews with GiveDirectly recipients in Malawi and Kenya" } ], "replication": { "produced_by": [ "Access to mobile network operator metadata for the target population under a data agreement", "A machine-learning model relating phone usage patterns to poverty, trained on survey data", "Mobile-money rails to enroll and pay identified people", "An in-person survey component (65% of enrollments) alongside the phone-metadata component" ], "must_hold": [ "Telecom partnership and data-sharing agreement in place (GiveDirectly cites 6–18 months set-up)", "Phone ownership and usage among the poor population", "Recipients' understanding and consent, which GiveDirectly found requires scenario-based explanation", "Model accuracy acceptable for the program (GiveDirectly reports phone metadata below in-person proxy-means tests)" ], "echoes": [ { "date": null, "setting": "Togo Novissi COVID-19 cash program (precedent, see AIBD-0093): GiveDirectly states the government paid over 140,000 people within weeks", "model_version": null, "outcome": "recurred", "source": "https://www.givedirectly.org/mobileaid" }, { "date": "2023-12-21", "setting": "Bangladesh: GiveDirectly states recipients were paid as quickly as 3 days after registration using machine-learning eligibility analysis with CEGA", "model_version": null, "outcome": "recurred", "source": "https://www.givedirectly.org/tech-2023" }, { "date": "2025-09-04", "setting": "Kenya: enrollment in flood- and drought-prone areas with Safaricom processing phone metadata in-house, described as ongoing", "model_version": null, "outcome": "unknown", "source": "https://www.givedirectly.org/mobileaid" } ] }, "paired_incident": null, "headline": "GiveDirectly's MobileAid may have halved enrollment cost in Malawi", "metric": "35% of recipients enrolled via phone data, cost per enrolee halved", "comparator": { "against": "a fully in-person enrollment operation", "before": null, "after": "cut the cost of identifying and enrolling each additional person in half", "basis": "measured", "quote": "This combined approach still cut the cost of identifying and enrolling each additional person in half, compared to a fully in-person operation.", "url": "https://www.givedirectly.org/mobileaid" }, "later": { "status": "unknown", "date": null, "note": "GiveDirectly says a study with the Government of Malawi, U.C. Berkeley and U.C. San Diego is expected to report in 2026; nothing dated after 4 September 2025 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": "2026-10-21", "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.givedirectly.org/mobileaid", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://projects.propublica.org/nonprofits/organizations/271661997; https://projects.propublica.org/nonprofits/organizations/271661997", "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" }, { "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: three press releases from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0351", "catalog_version": "1.0.0", "source_fingerprint": "0f3426e3243fafb4484c0c9f94cfde3f8aa82ff86c675d046679b52e8eff7a1a", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0351-source-0001", "AIBD-0351-source-0002", "AIBD-0351-source-0003", "AIBD-0351-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": "GiveDirectly (MobileAid), with research partners; for the Malawi study the Government of Malawi, U.C. Berkeley and U.C. San Diego", "deployer": "GiveDirectly, with the Government of Malawi for the urban social registry work" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Only the maker's account is among the sources. GiveDirectly says machine learning on phone metadata identified and enrolled 35% of recipients in a Malawi hybrid program. It says this halved the cost per additional enrolee. It gives no date, place, recipient count or accuracy for Malawi. The Government of Malawi, U.C. Berkeley and U.C. 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Likewise, the explain-AI page names no deployment, date or outcome." } } }, "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 makers' claim", "basis": "Sources: three 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 dated event, named recipient or outcome for Malawi is given in any cited source." }, { "group": "organizational", "reason": "The halved enrollment cost is the maker's own undated statement without figures or an independent evaluation." }, { "group": "societal", "reason": "No population-level outcome is documented for Malawi." } ] } }, "provenance": { "kind": "legacy_record", "basis": 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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[].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": "GiveDirectly says a study with the Government of Malawi, U.C. Berkeley and U.C. San Diego is expected to report in 2026; nothing dated after 4 September 2025 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-0351" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Organizational Efficiency" } }, "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": "GiveDirectly's MobileAid approach uses machine learning (ML) on mobile-phone usage patterns to identify people in need and pay them by mobile money. Its 4 September 2025 post (Vera Lummis) says it has evaluated the method across four countries since 2021 (Togo, DRC, Malawi and Bangladesh). In Malawi it ran a hybrid program identifying and enrolling 35% of recipients via phone metadata and 65% in person. It says this halved the cost of identifying and enrolling each additional person compared with a fully in-person operation. It is working with the Government of Malawi to fill gaps in the urban social registry with phone metadata. A study with the Government of Malawi, U.C. Berkeley and U.C. San Diego is expected to report in 2026. It asks whether phone metadata can flag people whose poverty deepens after a shock. Its 27 January 2025 post (Nirali Desai and Vera Lummis) describes interviews with 67 recipients across Malawi and Kenya in February and October 2024. The interviews, naming Mzuzu, asked how to explain AI/ML targeting. It finds scenario-based explanations most understandable. No cited source gives the Malawi program's dates, location, number of recipients or an independent evaluation.", "benefit_type": "Improved Organizational Efficiency" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No dated event, named recipient or outcome for Malawi is given in any cited source." }, { "group": "organizational", "reason": "The halved enrollment cost is the maker's own undated statement without figures or an independent evaluation." }, { "group": "societal", "reason": "No population-level outcome is documented for Malawi." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Identify people in poverty from mobile-phone metadata with machine learning so that cash can be delivered faster and more cheaply. The claimed benefit (cheaper, faster enrollment) 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": "Malawi (the explain-AI post names Mzuzu)", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Identify people in poverty from mobile-phone metadata with machine learning so that cash can be delivered faster and more cheaply. The claimed benefit (cheaper, faster enrollment) 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": "2025-09-04", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.givedirectly.org/mobileaid", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://projects.propublica.org/nonprofits/organizations/271661997; https://projects.propublica.org/nonprofits/organizations/271661997", "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" }, { "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": [ "Access to mobile network operator metadata for the target population under a data agreement", "A machine-learning model relating phone usage patterns to poverty, trained on survey data", "Mobile-money rails to enroll and pay identified people", "An in-person survey component (65% of enrollments) alongside the phone-metadata component" ], "must_hold": [ "Telecom partnership and data-sharing agreement in place (GiveDirectly cites 6–18 months set-up)", "Phone ownership and usage among the poor population", "Recipients' understanding and consent, which GiveDirectly found requires scenario-based explanation", "Model accuracy acceptable for the program (GiveDirectly reports phone metadata below in-person proxy-means tests)" ], "echoes": [ { "setting": "Togo Novissi COVID-19 cash program (precedent, see AIBD-0093): GiveDirectly states the government paid over 140,000 people within weeks", "outcome": "recurred", "source": "https://www.givedirectly.org/mobileaid" }, { "date": "2023-12-21", "setting": "Bangladesh: GiveDirectly states recipients were paid as quickly as 3 days after registration using machine-learning eligibility analysis with CEGA", "outcome": "recurred", "source": "https://www.givedirectly.org/tech-2023" }, { "date": "2025-09-04", "setting": "Kenya: enrollment in flood- and drought-prone areas with Safaricom processing phone metadata in-house, described as ongoing", "outcome": "unknown", "source": "https://www.givedirectly.org/mobileaid" } ] } } }, "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": [ "Access to mobile network operator metadata for the target population under a data agreement", "A machine-learning model relating phone usage patterns to poverty, trained on survey data", "Mobile-money rails to enroll and pay identified people", "An in-person survey component (65% of enrollments) alongside the phone-metadata component" ], "must_hold": [ "Telecom partnership and data-sharing agreement in place (GiveDirectly cites 6–18 months set-up)", "Phone ownership and usage among the poor population", "Recipients' understanding and consent, which GiveDirectly found requires scenario-based explanation", "Model accuracy acceptable for the program (GiveDirectly reports phone metadata below in-person proxy-means tests)" ], "echoes": [ { "setting": "Togo Novissi COVID-19 cash program (precedent, see AIBD-0093): GiveDirectly states the government paid over 140,000 people within weeks", "outcome": "recurred", "source": "https://www.givedirectly.org/mobileaid" }, { "date": "2023-12-21", "setting": "Bangladesh: GiveDirectly states recipients were paid as quickly as 3 days after registration using machine-learning eligibility analysis with CEGA", "outcome": "recurred", "source": "https://www.givedirectly.org/tech-2023" }, { "date": "2025-09-04", "setting": "Kenya: enrollment in flood- and drought-prone areas with Safaricom processing phone metadata in-house, described as ongoing", "outcome": "unknown", "source": "https://www.givedirectly.org/mobileaid" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "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": "Humanitarian cash transfers / poverty targeting", "segment": "Non-governmental cash-transfer organization working with a national 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": "Lummis, V. (2025). Five things we've learned using mobile data and AI/ML to identify people in need. GiveDirectly blog, 4 September 2025.", "title": "Five things we've learned using mobile data and AI/ML to identify people in need", "url": "https://www.givedirectly.org/mobileaid", "date": "2025-09-04", "written_by": "makers", "quote": "So in Malawi, we ran a hybrid program: we identified and enrolled 35% of recipients via phone metadata and the other 65% in person." }, { "kind": "press release", "citation": "Lummis, V. (2025). Five things we've learned using mobile data and AI/ML to identify people in need. GiveDirectly blog, 4 September 2025 (cost statement).", "title": "Five things we've learned using mobile data and AI/ML to identify people in need (cost statement)", "url": "https://www.givedirectly.org/mobileaid", "date": "2025-09-04", "written_by": "makers", "quote": "This combined approach still cut the cost of identifying and enrolling each additional person in half, compared to a fully in-person operation." }, { "kind": "press release", "citation": "Desai, N. & Lummis, V. (2025). How to explain AI/ML across the digital divide. GiveDirectly blog, 27 January 2025.", "title": "Explain AI/ML systems to people in poverty using real-world scenarios", "url": "https://www.givedirectly.org/explain_ai", "date": "2025-01-27", "written_by": "makers", "quote": "we interviewed 67 GiveDirectly recipients across Malawi and Kenya in February and October 2024" }, { "kind": "press release", "citation": "Lummis, V., Luk, S. & Ramprasad, S. (2024). GiveDirectly's approach to responsible AI/ML. GiveDirectly blog, 8 July 2024.", "title": "GiveDirectly's approach to responsible AI/ML", "url": "https://www.givedirectly.org/ai-framework", "date": "2024-07-08", "written_by": "makers", "quote": "interviews with GiveDirectly recipients in Malawi and Kenya" } ] } }, "provenance": { "kind": "legacy_record", "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-0351-source-0001", "AIBD-0351-source-0002", "AIBD-0351-source-0003", "AIBD-0351-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": "Lummis, V. (2025). Five things we've learned using mobile data and AI/ML to identify people in need. GiveDirectly blog, 4 September 2025.", "title": "Five things we've learned using mobile data and AI/ML to identify people in need", "url": "https://www.givedirectly.org/mobileaid", "date": "2025-09-04", "written_by": "makers", "quote": "So in Malawi, we ran a hybrid program: we identified and enrolled 35% of recipients via phone metadata and the other 65% in person." }, { "kind": "press release", "citation": "Lummis, V. (2025). Five things we've learned using mobile data and AI/ML to identify people in need. GiveDirectly blog, 4 September 2025 (cost statement).", "title": "Five things we've learned using mobile data and AI/ML to identify people in need (cost statement)", "url": "https://www.givedirectly.org/mobileaid", "date": "2025-09-04", "written_by": "makers", "quote": "This combined approach still cut the cost of identifying and enrolling each additional person in half, compared to a fully in-person operation." }, { "kind": "press release", "citation": "Desai, N. & Lummis, V. (2025). How to explain AI/ML across the digital divide. GiveDirectly blog, 27 January 2025.", "title": "Explain AI/ML systems to people in poverty using real-world scenarios", "url": "https://www.givedirectly.org/explain_ai", "date": "2025-01-27", "written_by": "makers", "quote": "we interviewed 67 GiveDirectly recipients across Malawi and Kenya in February and October 2024" }, { "kind": "press release", "citation": "Lummis, V., Luk, S. & Ramprasad, S. (2024). GiveDirectly's approach to responsible AI/ML. GiveDirectly blog, 8 July 2024.", "title": "GiveDirectly's approach to responsible AI/ML", "url": "https://www.givedirectly.org/ai-framework", "date": "2024-07-08", "written_by": "makers", "quote": "interviews with GiveDirectly recipients in Malawi and Kenya" } ] } }, "provenance": { "kind": "legacy_record", "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": "GiveDirectly posts (2024–2025) describe phone-metadata AI/ML targeting in Malawi: 35% of recipients enrolled via phone data, cost per enrolee halved; no date, count or independent source.", "account": "GiveDirectly's MobileAid approach uses machine learning (ML) on mobile-phone usage patterns to identify people in need and pay them by mobile money. Its 4 September 2025 post (Vera Lummis) says it has evaluated the method across four countries since 2021 (Togo, DRC, Malawi and Bangladesh). In Malawi it ran a hybrid program identifying and enrolling 35% of recipients via phone metadata and 65% in person. It says this halved the cost of identifying and enrolling each additional person compared with a fully in-person operation. It is working with the Government of Malawi to fill gaps in the urban social registry with phone metadata. A study with the Government of Malawi, U.C. Berkeley and U.C. San Diego is expected to report in 2026. It asks whether phone metadata can flag people whose poverty deepens after a shock. Its 27 January 2025 post (Nirali Desai and Vera Lummis) describes interviews with 67 recipients across Malawi and Kenya in February and October 2024. The interviews, naming Mzuzu, asked how to explain AI/ML targeting. It finds scenario-based explanations most understandable. No cited source gives the Malawi program's dates, location, number of recipients or an independent evaluation." } }, "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": "GiveDirectly MobileAid: machine-learning poverty targeting from mobile-phone metadata" } }, "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": "GiveDirectly (MobileAid), with research partners; for the Malawi study the Government of Malawi, U.C. Berkeley and U.C. San Diego" } }, "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's hybrid MobileAid program in Malawi may have enrolled 35% of recipients by phone-metadata machine learning and halved enrollment cost", "headline": "GiveDirectly's MobileAid may have halved enrollment cost in Malawi" } }, "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": [ "0f3426e3243fafb4484c0c9f94cfde3f8aa82ff86c675d046679b52e8eff7a1a" ], "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": "GiveDirectly's MobileAid approach uses machine learning (ML) on mobile-phone usage patterns to identify people in need and pay them by mobile money. Its 4 September 2025 post (Vera Lummis) says it has evaluated the method across four countries since 2021 (Togo, DRC, Malawi and Bangladesh). In Malawi it ran a hybrid program identifying and enrolling 35% of recipients via phone metadata and 65% in person. It says this halved the cost of identifying and enrolling each additional person compared with a fully in-person operation. It is working with the Government of Malawi to fill gaps in the urban social registry with phone metadata. A study with the Government of Malawi, U.C. Berkeley and U.C. San Diego is expected to report in 2026. It asks whether phone metadata can flag people whose poverty deepens after a shock. Its 27 January 2025 post (Nirali Desai and Vera Lummis) describes interviews with 67 recipients across Malawi and Kenya in February and October 2024. The interviews, naming Mzuzu, asked how to explain AI/ML targeting. It finds scenario-based explanations most understandable. No cited source gives the Malawi program's dates, location, number of recipients or an independent evaluation.", "replication": { "produced_by": [ "Access to mobile network operator metadata for the target population under a data agreement", "A machine-learning model relating phone usage patterns to poverty, trained on survey data", "Mobile-money rails to enroll and pay identified people", "An in-person survey component (65% of enrollments) alongside the phone-metadata component" ], "must_hold": [ "Telecom partnership and data-sharing agreement in place (GiveDirectly cites 6–18 months set-up)", "Phone ownership and usage among the poor population", "Recipients' understanding and consent, which GiveDirectly found requires scenario-based explanation", "Model accuracy acceptable for the program (GiveDirectly reports phone metadata below in-person proxy-means tests)" ], "echoes": [ { "setting": "Togo Novissi COVID-19 cash program (precedent, see AIBD-0093): GiveDirectly states the government paid over 140,000 people within weeks", "outcome": "recurred", "source": "https://www.givedirectly.org/mobileaid" }, { "date": "2023-12-21", "setting": "Bangladesh: GiveDirectly states recipients were paid as quickly as 3 days after registration using machine-learning eligibility analysis with CEGA", "outcome": "recurred", "source": "https://www.givedirectly.org/tech-2023" }, { "date": "2025-09-04", "setting": "Kenya: enrollment in flood- and drought-prone areas with Safaricom processing phone metadata in-house, described as ongoing", "outcome": "unknown", "source": "https://www.givedirectly.org/mobileaid" } ] } } }, "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-0278", "case_url": "records/AIBD-0278.html", "reason": "The developer is headquartered in United States; the case names United Kingdom, China.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "United Kingdom, China" }, { "label": "Case location", "value": "Cambridge, UK (model); Shenzhen, China (synthesis)" } ], "filters": { "origin": "United States", "destination": [ "United Kingdom", "China" ] }, "record": { "id": "AIBD-0278", "title": "MatterGen generates TaCr2O6; SIAT synthesizes a disordered version with bulk modulus 169 GPa against a 200 GPa target", "summary": "Microsoft's diffusion model generated a structure conditioned on bulk modulus 200 GPa; SIAT (Shenzhen) synthesized it and measured 169 GPa, with Ta/Cr compositional disorder. 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(Frontiers in Immunology, 14 November 2024) are at the Affiliated Yuebei People's Hospital, Shantou University Medical College, Shaoguan. They report a previously healthy 59-year-old woman who developed hemiparesis and altered mental status after SARS-CoV-2 reinfection. She was first treated for presumed infarction, then for suspected demyelination. Brain biopsy diagnosed intravascular large B-cell lymphoma. The authors say they reviewed the disease's brain magnetic resonance imaging (MRI) findings in the literature “with the assistance of ChatGPT”. They call their review the first to focus on MRI features of cerebral lesions. The model played no part in diagnosis or treatment. 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It evaluates a six-week after-school program run in June and July 2024 in nine public schools in Benin City, Nigeria, chosen for their computer labs. First-year senior secondary students, typically 15 years old, who expressed interest were randomized: 657 to treatment and 671 to control. Of these, 422 and 337 finished the final assessment. Treated students attended up to two 90-minute sessions a week (twelve in total). They used Microsoft Copilot, powered by GPT-4 at the time, as a virtual English tutor in curriculum-aligned activities guided by teachers. The paper reports a 0.31 standard deviation improvement on an assessment covering English, AI knowledge and digital skills, and 0.23 SD on English. It equates the gains to 1.5 to 2 years of business-as-usual schooling. Effects were larger for female students and those with higher initial performance. The World Bank blog of 9 January 2025 describes the gain as about 0.3 SD. 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The large language model (LLM) share is not isolated from teacher guidance and extra teaching time." }, "evidence": [ { "kind": "report", "citation": "De Simone M, Tiberti F, Barron Rodriguez M, Manolio F, Mosuro W, Dikoru EJ (2025). From Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria. Policy Research Working Paper 11125, World Bank (OKR handle page).", "title": "From Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria", "url": "https://hdl.handle.net/10986/43212", "date": "2025-05-20", "written_by": "deployer", "quote": "The program was implemented over a six-week period between June and July 2024, targeting first-year senior secondary school students, who are typically 15 years old." }, { "kind": "report", "citation": "ERIC record ED676624 for Policy Research Working Paper 11125 (abstract).", "title": "From Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria. 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World Bank Blogs.", "title": "From chalkboards to chatbots: Transforming learning in Nigeria, one prompt at a time", "url": "https://blogs.worldbank.org/en/education/From-chalkboards-to-chatbots-Transforming-learning-in-Nigeria", "date": "2025-01-09", "written_by": "deployer", "quote": "Students who were randomly assigned to participate significantly outperformed peers in all areas" }, { "kind": "press release", "citation": "World Bank Education blog (2024). From chalkboards to chatbots in Nigeria: 7 lessons to pioneer generative AI for education. 18 September 2024.", "title": "From chalkboards to chatbots in Nigeria: 7 lessons to pioneer generative AI for education", "url": "https://blogs.worldbank.org/en/education/From-chalkboards-to-chatbots-in-Nigeria", "date": "2024-09-18", "written_by": "deployer", "quote": "Over June and July 2024,800 first-year senior secondary students attended after-school English classes in computer labs twice a week." }, { "kind": "report", "citation": "De Simone M et al. (2025). Working Paper 11125 (sample and attrition).", "title": "From Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria", "url": "https://openknowledge.worldbank.org/bitstreams/8fcccf10-83c9-4295-b01c-5a68aac83b06/download", "date": "2025-05-20", "written_by": "deployer", "quote": "However, only 422 students in the treatment group and 337 in the control group completed the final assessment" } ], "replication": { "produced_by": [ "A freely available LLM (Copilot on GPT-4) requiring only student registration", "Curriculum-aligned prompts and activities guided by teachers in school computer labs", "Student-level randomization among volunteers in nine schools with computer labs" ], "must_hold": [ "Working computer labs and internet access", "Teacher guidance in every session", "Attendance (the paper reports gains rising with days attended)" ], "echoes": [] }, "paired_incident": null, "headline": "Microsoft Copilot tutoring lifts Benin City scores by 0.31 SD", "metric": "0.31 SD overall, 0.23 SD in English", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No report of a second phase or scale-up of the Benin City program after the May 2025 working paper was found.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "Nigeria", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged a second write-up of the same trial: added the 2024 World Bank blog on the pilot and the working paper's attrition figures.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://blogs.worldbank.org/en/education/From-chalkboards-to-chatbots-in-Nigeria", "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/Microsoft", "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: three reports from the deployer and two press releases from the deployer. 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From chalkboards to chatbots in Nigeria: 7 lessons to pioneer generative AI for education. 18 September 2024.", "title": "From chalkboards to chatbots in Nigeria: 7 lessons to pioneer generative AI for education", "url": "https://blogs.worldbank.org/en/education/From-chalkboards-to-chatbots-in-Nigeria", "date": "2024-09-18", "written_by": "deployer", "quote": "Over June and July 2024,800 first-year senior secondary students attended after-school English classes in computer labs twice a week." }, { "kind": "report", "citation": "De Simone M et al. (2025). 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It evaluates a six-week after-school program run in June and July 2024 in nine public schools in Benin City, Nigeria, chosen for their computer labs. First-year senior secondary students, typically 15 years old, who expressed interest were randomized: 657 to treatment and 671 to control. Of these, 422 and 337 finished the final assessment. Treated students attended up to two 90-minute sessions a week (twelve in total). They used Microsoft Copilot, powered by GPT-4 at the time, as a virtual English tutor in curriculum-aligned activities guided by teachers. The paper reports a 0.31 standard deviation improvement on an assessment covering English, AI knowledge and digital skills, and 0.23 SD on English. It equates the gains to 1.5 to 2 years of business-as-usual schooling. Effects were larger for female students and those with higher initial performance. The World Bank blog of 9 January 2025 describes the gain as about 0.3 SD. 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It evaluates a six-week after-school program run in June and July 2024 in nine public schools in Benin City, Nigeria, chosen for their computer labs. First-year senior secondary students, typically 15 years old, who expressed interest were randomized: 657 to treatment and 671 to control. Of these, 422 and 337 finished the final assessment. Treated students attended up to two 90-minute sessions a week (twelve in total). They used Microsoft Copilot, powered by GPT-4 at the time, as a virtual English tutor in curriculum-aligned activities guided by teachers. The paper reports a 0.31 standard deviation improvement on an assessment covering English, AI knowledge and digital skills, and 0.23 SD on English. It equates the gains to 1.5 to 2 years of business-as-usual schooling. Effects were larger for female students and those with higher initial performance. The World Bank blog of 9 January 2025 describes the gain as about 0.3 SD. The paper gives months, not days.", "replication": { "produced_by": [ "A freely available LLM (Copilot on GPT-4) requiring only student registration", "Curriculum-aligned prompts and activities guided by teachers in school computer labs", "Student-level randomization among volunteers in nine schools with computer labs" ], "must_hold": [ "Working computer labs and internet access", "Teacher guidance in every session", "Attendance (the paper reports gains rising with days attended)" ], "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-0297", "case_url": "records/AIBD-0297.html", "reason": "The developer is headquartered in Finland; the case names United Kingdom, Denmark.", "details": [ { "label": "Developer headquarters", "value": "Finland" }, { "label": "Case jurisdictions", "value": "United Kingdom, Denmark" }, { "label": "Case location", "value": "Cornwall, United Kingdom; Dianalund and Aarhus, Denmark" } ], "filters": { "origin": "Finland", "destination": [ "United Kingdom", "Denmark" ] }, "record": { "id": "AIBD-0297", "title": "Nelli audio-video AI seizure monitor records 247 nocturnal events missed by carers among 15 people with intellectual disability in Cornwall; Danish validation detected all 11 tonic-clonic seizures", "summary": "Cornwall NHS evaluation (15 people who could not tolerate EEG): Nelli flagged 707 possible seizure events, 247 unheard or unrecognised by carers; Danish phase-3 validation sensitivity 93.7%.", "date_observed": "2023-04-23", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Nelli, made by Neuro Event Labs of Tampere, Finland, is a camera-and-microphone unit. Computer vision and what its validators call an artificial-intelligence-based algorithm flag nocturnal motor seizures from its signals. A phase-3 validation at the Danish Epilepsy Center and Aarhus University Hospital had 191 participants, recruited 14 October 2019 to 18 June 2021. It had 4,183 night-time hours, analyzed in real time against video electroencephalography (EEG). Armand Larsen, Beniczky and colleagues, with two Neuro Event Labs co-authors, reported 93.7% sensitivity for major motor seizures (Brain and Behavior, 8 August 2022). All 11 tonic-clonic and four of five hypermotor seizures were detected. False detections were 0.16 per hour, device deficiency 10.5% of the time, and sensitivity for minor motor seizures only 8.3%. Lennard, Shankar and colleagues of the National Health Service (NHS) trust Cornwall Partnership NHS Foundation Trust then evaluated Nelli in the community. The 15 participants had intellectual disability and drug-resistant epilepsy and could not tolerate routine or ambulatory EEG. The report appeared in Epilepsy & Behavior Reports on 23 April 2023. The source gives no study dates. Nelli recorded 707 possible seizure events in 14 people, 247 of them unheard or unrecognized by carers. One person had half of possible events missed by night staff. Carers logged 165 “restless” or “seizure behavior” episodes that Nelli did not deem seizures. Acceptability was 93%. The authors conclude Nelli “allowed for improved communication and care planning”. A single-center feasibility study in Dravet and Lennox-Gastaut syndromes (Wilms et al., Epilepsia, online 9 October 2025; abstract read) ran four weeks of home monitoring. It detected 368 seizures with 97.8% accuracy, while only 26.1% were logged in diaries. No individual's changed outcome is documented in any cited source.", "category": "general benefit", "unexpected": false, "purpose": "Built to detect nocturnal major motor seizures from audio-video so that staff can intervene and clinicians can see events patients cannot report. The unrecognized events it surfaced are that purpose.", "system": "Nelli audio-video seizure monitoring system (computer vision and AI-based event detection)", "model_version": null, "developer": "Neuro Event Labs, Tampere, Finland", "deployer": "Cornwall Partnership NHS Foundation Trust (community evaluation); Danish Epilepsy Center, Dianalund, and Aarhus University Hospital (validation)", "location": "Cornwall, United Kingdom; Dianalund and Aarhus, Denmark", "domain": "Healthcare (epilepsy; nocturnal seizure monitoring)", "segment": "Residential and institutional seizure monitoring for people with intellectual disability", "benefit_type": "Improved Physical Health", "taxonomy_note": "The gain is seizure information for people with intellectual disability who could not tolerate EEG, which also reads as accessibility.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Carers reported peace of mind (13 of 14). Clinicians gained event counts for people who cannot report seizures. No cited source documents a treatment change or injury avoided for a named individual." }, { "group": "organizational", "depth": null, "reason": "The Cornwall authors describe improved care planning and carer training. No measured change in staffing, response times or incidents is reported." } ], "ai_role": { "status": "assessed", "statement": "The system flagged motor events that night staff and carers had not noticed (247 of 707 in Cornwall). In the Danish validation it detected every tonic-clonic seizure in real time. Clinicians still reviewed sampled clips to confirm events. Sensitivity for minor motor seizures was 8.3%. One Cornwall participant's unit was removed to prevent injury and equipment damage. The validation paper is co-authored by two vendor employees. The Cornwall evaluation is independent of the maker." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Lennard S, Newman R, McLean B, Jory C, Cox D, Young C, Corson E, Shankar R (2023). Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera. Epilepsy & Behavior Reports 22:100603 (PMC10160340).", "title": "Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/", "date": "2023-04-23", "written_by": "deployer", "quote": "Nelli recorded 707 possible seizure events across the 14 PwID of which 247 were either not heard or not recognised by carers." }, { "kind": "peer-reviewed study", "citation": "Lennard et al. (2023), Epilepsy Behav Rep 22:100603, abstract.", "title": "Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/", "date": "2023-04-23", "written_by": "deployer", "quote": "Nelli allowed for improved communication and care planning in a hitherto difficult to investigate population." }, { "kind": "peer-reviewed study", "citation": "Armand Larsen S, Terney D, Østerkjerhuus T, Vinding Merinder T, Annala K, Knight A, Beniczky S (2022). Automated detection of nocturnal motor seizures using an audio-video system. Brain and Behavior 12(9):e2737 (PMC9480955). Annala and Knight are Neuro Event Labs employees and shareholders.", "title": "Automated detection of nocturnal motor seizures using an audio-video system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9480955/", "date": "2022-08-08", "written_by": "makers", "quote": "The system detected all 11 TCS and four out of five (80%) hypermotor seizures." }, { "kind": "peer-reviewed study", "citation": "Armand Larsen et al. (2022), Brain Behav 12(9):e2737, methods.", "title": "Automated detection of nocturnal motor seizures using an audio-video system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9480955/", "date": "2022-08-08", "written_by": "makers", "quote": "we assessed the performance of automated detection of nocturnal motor seizures using audio‐video streaming, computer vision and an artificial intelligence‐based algorithm (Nelli)" }, { "kind": "peer-reviewed study", "citation": "Wilms LK, Lossius MI, Annala K, Abdel-Khalik J, Fanter L, Elomaa K, Peltola J (2025). Seizure classification using a multimodal seizure monitoring system (Nelli) in Dravet and Lennox-Gastaut syndromes: A non-randomized, single-center feasibility study. Epilepsia, online 9 October 2025; doi:10.1111/epi.18640 (PMID 41066202; PMC12893256). Neuro Event Labs co-authors.", "title": "Seizure classification using a multimodal seizure monitoring system (Nelli) in Dravet and Lennox-Gastaut syndromes: A non-randomized, single-center feasibility study", "url": "https://europepmc.org/article/MED/41066202", "date": "2025-10-09", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "A camera and microphone unit streaming to a server where computer-vision and AI algorithms flag motor events", "Night-time use for people with suspected nocturnal seizures who could not tolerate EEG, with family or carer consent", "Clinician review of flagged clips (the Cornwall paper says 8–9% of the recording is sampled by an epilepsy expert)" ], "must_hold": [ "Device deficiency kept low (10.5% of monitored time in the Danish validation)", "Staff available at night to act on alarms, the setting the validators say the system is suited to", "The target is major motor seizures; minor motor seizures were detected at 8.3% sensitivity" ], "echoes": [ { "date": "2025-10-09", "setting": "Single-center feasibility study, 20 patients with Dravet or Lennox-Gastaut syndrome, four weeks of home monitoring in bed, August 2023 to March 2024", "model_version": null, "outcome": "recurred", "source": "Wilms LK, Lossius MI, Annala K, Abdel-Khalik J, Fanter L, Elomaa K, Peltola J (2026) Epilepsia 67(1):164-174, doi:10.1111/epi.18640 (abstract: 368 seizures detected with 97.8% accuracy; 'only 26.1% of the seizures were documented' in diaries; vendor co-authors)" } ] }, "paired_incident": null, "headline": "Nelli flags 247 nocturnal seizure events missed by carers", "metric": "Nelli flagged 707 possible seizure events, 247 unheard or unrecognised by carers", "comparator": { "against": "carers' hearing or recognition of night-time events in the same people", "before": "247 were not heard or recognised by carers", "after": "Nelli recorded 707 possible seizure events across the study cohort", "basis": "measured", "quote": "Nelli recorded 707 possible seizure events across the study cohort of which 247 were not heard or recognised by carers. Carers recorded 165 episodes of ‘restless’ or “seizure behaviour” which the Nelli did not deem to be seizures.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/" }, "later": { "status": "persisted", "date": "2025-10-09", "note": "A later single-center feasibility study in Dravet and Lennox-Gastaut syndromes, with vendor co-authors, reported that Nelli detected 368 seizures at 97.8% accuracy over four weeks of home monitoring while diaries documented only 26.1% of seizures.", "quote": "Nelli demonstrated high sensitivity and classification accuracy for detecting and categorizing seizures in bed in patients with DS and LGS, outperforming seizure diaries and providing a reliable tool for seizure monitoring in home settings.", "url": "https://europepmc.org/article/MED/41066202" }, "value": null, "developer_country": "Finland", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "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://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/; https://europepmc.org/article/MED/41066202", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://neuroeventlabs.com/contact/; https://directory.spineservices.nhs.uk/ORD/2-0-0/organisations/RJ8", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: two peer-reviewed studies from the makers and the deployer's peer-reviewed study. 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sensitivity and classification accuracy for detecting and categorizing seizures in bed in patients with DS and LGS, outperforming seizure diaries and providing a reliable tool for seizure monitoring in home settings.", "url": "https://europepmc.org/article/MED/41066202" } } }, "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-0297" } }, "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 gain is seizure information for people with intellectual disability who could not tolerate EEG, which also reads as accessibility." } }, "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": "Nelli, made by Neuro Event Labs of Tampere, Finland, is a camera-and-microphone unit. Computer vision and what its validators call an artificial-intelligence-based algorithm flag nocturnal motor seizures from its signals. A phase-3 validation at the Danish Epilepsy Center and Aarhus University Hospital had 191 participants, recruited 14 October 2019 to 18 June 2021. It had 4,183 night-time hours, analyzed in real time against video electroencephalography (EEG). Armand Larsen, Beniczky and colleagues, with two Neuro Event Labs co-authors, reported 93.7% sensitivity for major motor seizures (Brain and Behavior, 8 August 2022). All 11 tonic-clonic and four of five hypermotor seizures were detected. False detections were 0.16 per hour, device deficiency 10.5% of the time, and sensitivity for minor motor seizures only 8.3%. Lennard, Shankar and colleagues of the National Health Service (NHS) trust Cornwall Partnership NHS Foundation Trust then evaluated Nelli in the community. The 15 participants had intellectual disability and drug-resistant epilepsy and could not tolerate routine or ambulatory EEG. The report appeared in Epilepsy & Behavior Reports on 23 April 2023. The source gives no study dates. Nelli recorded 707 possible seizure events in 14 people, 247 of them unheard or unrecognized by carers. One person had half of possible events missed by night staff. Carers logged 165 “restless” or “seizure behavior” episodes that Nelli did not deem seizures. Acceptability was 93%. The authors conclude Nelli “allowed for improved communication and care planning”. A single-center feasibility study in Dravet and Lennox-Gastaut syndromes (Wilms et al., Epilepsia, online 9 October 2025; abstract read) ran four weeks of home monitoring. It detected 368 seizures with 97.8% accuracy, while only 26.1% were logged in diaries. No individual's changed outcome is documented in any cited 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": "Carers reported peace of mind (13 of 14). Clinicians gained event counts for people who cannot report seizures. No cited source documents a treatment change or injury avoided for a named individual." }, { "group": "organizational", "reason": "The Cornwall authors describe improved care planning and carer training. No measured change in staffing, response times or incidents 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": "Built to detect nocturnal major motor seizures from audio-video so that staff can intervene and clinicians can see events patients cannot report. The unrecognized events it surfaced are that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Cornwall, United Kingdom; Dianalund and Aarhus, Denmark", "developer_country": "Finland", "deployer_country": "United Kingdom" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to detect nocturnal major motor seizures from audio-video so that staff can intervene and clinicians can see events patients cannot report. The unrecognized events it surfaced are that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2023-04-23", "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://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/; https://europepmc.org/article/MED/41066202", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://neuroeventlabs.com/contact/; https://directory.spineservices.nhs.uk/ORD/2-0-0/organisations/RJ8", "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 camera and microphone unit streaming to a server where computer-vision and AI algorithms flag motor events", "Night-time use for people with suspected nocturnal seizures who could not tolerate EEG, with family or carer consent", "Clinician review of flagged clips (the Cornwall paper says 8–9% of the recording is sampled by an epilepsy expert)" ], "must_hold": [ "Device deficiency kept low (10.5% of monitored time in the Danish validation)", "Staff available at night to act on alarms, the setting the validators say the system is suited to", "The target is major motor seizures; minor motor seizures were detected at 8.3% sensitivity" ], "echoes": [ { "date": "2025-10-09", "setting": "Single-center feasibility study, 20 patients with Dravet or Lennox-Gastaut syndrome, four weeks of home monitoring in bed, August 2023 to March 2024", "outcome": "recurred", "source": "Wilms LK, Lossius MI, Annala K, Abdel-Khalik J, Fanter L, Elomaa K, Peltola J (2026) Epilepsia 67(1):164-174, doi:10.1111/epi.18640 (abstract: 368 seizures detected with 97.8% accuracy; 'only 26.1% of the seizures were documented' in diaries; vendor co-authors)" } ] } } }, "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 and microphone unit streaming to a server where computer-vision and AI algorithms flag motor events", "Night-time use for people with suspected nocturnal seizures who could not tolerate EEG, with family or carer consent", "Clinician review of flagged clips (the Cornwall paper says 8–9% of the recording is sampled by an epilepsy expert)" ], "must_hold": [ "Device deficiency kept low (10.5% of monitored time in the Danish validation)", "Staff available at night to act on alarms, the setting the validators say the system is suited to", "The target is major motor seizures; 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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": "Healthcare (epilepsy; nocturnal seizure monitoring)", "segment": "Residential and institutional seizure monitoring for people with intellectual disability" } }, "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": "Lennard S, Newman R, McLean B, Jory C, Cox D, Young C, Corson E, Shankar R (2023). Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera. Epilepsy & Behavior Reports 22:100603 (PMC10160340).", "title": "Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/", "date": "2023-04-23", "written_by": "deployer", "quote": "Nelli recorded 707 possible seizure events across the 14 PwID of which 247 were either not heard or not recognised by carers." }, { "kind": "peer-reviewed study", "citation": "Lennard et al. (2023), Epilepsy Behav Rep 22:100603, abstract.", "title": "Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/", "date": "2023-04-23", "written_by": "deployer", "quote": "Nelli allowed for improved communication and care planning in a hitherto difficult to investigate population." }, { "kind": "peer-reviewed study", "citation": "Armand Larsen S, Terney D, Østerkjerhuus T, Vinding Merinder T, Annala K, Knight A, Beniczky S (2022). Automated detection of nocturnal motor seizures using an audio-video system. Brain and Behavior 12(9):e2737 (PMC9480955). Annala and Knight are Neuro Event Labs employees and shareholders.", "title": "Automated detection of nocturnal motor seizures using an audio-video system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9480955/", "date": "2022-08-08", "written_by": "makers", "quote": "The system detected all 11 TCS and four out of five (80%) hypermotor seizures." }, { "kind": "peer-reviewed study", "citation": "Armand Larsen et al. (2022), Brain Behav 12(9):e2737, methods.", "title": "Automated detection of nocturnal motor seizures using an audio-video system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9480955/", "date": "2022-08-08", "written_by": "makers", "quote": "we assessed the performance of automated detection of nocturnal motor seizures using audio‐video streaming, computer vision and an artificial intelligence‐based algorithm (Nelli)" }, { "kind": "peer-reviewed study", "citation": "Wilms LK, Lossius MI, Annala K, Abdel-Khalik J, Fanter L, Elomaa K, Peltola J (2025). Seizure classification using a multimodal seizure monitoring system (Nelli) in Dravet and Lennox-Gastaut syndromes: A non-randomized, single-center feasibility study. Epilepsia, online 9 October 2025; doi:10.1111/epi.18640 (PMID 41066202; PMC12893256). Neuro Event Labs co-authors.", "title": "Seizure classification using a multimodal seizure monitoring system (Nelli) in Dravet and Lennox-Gastaut syndromes: A non-randomized, single-center feasibility study", "url": "https://europepmc.org/article/MED/41066202", "date": "2025-10-09", "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-0297-source-0001", "AIBD-0297-source-0002", "AIBD-0297-source-0003", "AIBD-0297-source-0004", "AIBD-0297-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": "peer-reviewed study", "citation": "Lennard S, Newman R, McLean B, Jory C, Cox D, Young C, Corson E, Shankar R (2023). Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera. Epilepsy & Behavior Reports 22:100603 (PMC10160340).", "title": "Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/", "date": "2023-04-23", "written_by": "deployer", "quote": "Nelli recorded 707 possible seizure events across the 14 PwID of which 247 were either not heard or not recognised by carers." }, { "kind": "peer-reviewed study", "citation": "Lennard et al. (2023), Epilepsy Behav Rep 22:100603, abstract.", "title": "Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10160340/", "date": "2023-04-23", "written_by": "deployer", "quote": "Nelli allowed for improved communication and care planning in a hitherto difficult to investigate population." }, { "kind": "peer-reviewed study", "citation": "Armand Larsen S, Terney D, Østerkjerhuus T, Vinding Merinder T, Annala K, Knight A, Beniczky S (2022). Automated detection of nocturnal motor seizures using an audio-video system. Brain and Behavior 12(9):e2737 (PMC9480955). Annala and Knight are Neuro Event Labs employees and shareholders.", "title": "Automated detection of nocturnal motor seizures using an audio-video system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9480955/", "date": "2022-08-08", "written_by": "makers", "quote": "The system detected all 11 TCS and four out of five (80%) hypermotor seizures." }, { "kind": "peer-reviewed study", "citation": "Armand Larsen et al. (2022), Brain Behav 12(9):e2737, methods.", "title": "Automated detection of nocturnal motor seizures using an audio-video system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9480955/", "date": "2022-08-08", "written_by": "makers", "quote": "we assessed the performance of automated detection of nocturnal motor seizures using audio‐video streaming, computer vision and an artificial intelligence‐based algorithm (Nelli)" }, { "kind": "peer-reviewed study", "citation": "Wilms LK, Lossius MI, Annala K, Abdel-Khalik J, Fanter L, Elomaa K, Peltola J (2025). Seizure classification using a multimodal seizure monitoring system (Nelli) in Dravet and Lennox-Gastaut syndromes: A non-randomized, single-center feasibility study. Epilepsia, online 9 October 2025; doi:10.1111/epi.18640 (PMID 41066202; PMC12893256). Neuro Event Labs co-authors.", "title": "Seizure classification using a multimodal seizure monitoring system (Nelli) in Dravet and Lennox-Gastaut syndromes: A non-randomized, single-center feasibility study", "url": "https://europepmc.org/article/MED/41066202", "date": "2025-10-09", "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": "Cornwall NHS evaluation (15 people who could not tolerate EEG): Nelli flagged 707 possible seizure events, 247 unheard or unrecognised by carers; Danish phase-3 validation sensitivity 93.7%.", "account": "Nelli, made by Neuro Event Labs of Tampere, Finland, is a camera-and-microphone unit. Computer vision and what its validators call an artificial-intelligence-based algorithm flag nocturnal motor seizures from its signals. A phase-3 validation at the Danish Epilepsy Center and Aarhus University Hospital had 191 participants, recruited 14 October 2019 to 18 June 2021. It had 4,183 night-time hours, analyzed in real time against video electroencephalography (EEG). Armand Larsen, Beniczky and colleagues, with two Neuro Event Labs co-authors, reported 93.7% sensitivity for major motor seizures (Brain and Behavior, 8 August 2022). All 11 tonic-clonic and four of five hypermotor seizures were detected. False detections were 0.16 per hour, device deficiency 10.5% of the time, and sensitivity for minor motor seizures only 8.3%. Lennard, Shankar and colleagues of the National Health Service (NHS) trust Cornwall Partnership NHS Foundation Trust then evaluated Nelli in the community. The 15 participants had intellectual disability and drug-resistant epilepsy and could not tolerate routine or ambulatory EEG. The report appeared in Epilepsy & Behavior Reports on 23 April 2023. The source gives no study dates. Nelli recorded 707 possible seizure events in 14 people, 247 of them unheard or unrecognized by carers. One person had half of possible events missed by night staff. Carers logged 165 “restless” or “seizure behavior” episodes that Nelli did not deem seizures. Acceptability was 93%. The authors conclude Nelli “allowed for improved communication and care planning”. A single-center feasibility study in Dravet and Lennox-Gastaut syndromes (Wilms et al., Epilepsia, online 9 October 2025; abstract read) ran four weeks of home monitoring. It detected 368 seizures with 97.8% accuracy, while only 26.1% were logged in diaries. No individual's changed outcome is documented in any cited 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": "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": "Nelli audio-video seizure monitoring system (computer vision and AI-based 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": "Neuro Event Labs, Tampere, Finland" } }, "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": "Nelli audio-video AI seizure monitor records 247 nocturnal events missed by carers among 15 people with intellectual disability in Cornwall; Danish validation detected all 11 tonic-clonic seizures", "headline": "Nelli flags 247 nocturnal seizure events missed by carers" } }, "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": [ "916bef6a0a2af27bda417c248c50a5f12f401749fda46099943762426b41a002" ], "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": "Nelli, made by Neuro Event Labs of Tampere, Finland, is a camera-and-microphone unit. Computer vision and what its validators call an artificial-intelligence-based algorithm flag nocturnal motor seizures from its signals. A phase-3 validation at the Danish Epilepsy Center and Aarhus University Hospital had 191 participants, recruited 14 October 2019 to 18 June 2021. It had 4,183 night-time hours, analyzed in real time against video electroencephalography (EEG). Armand Larsen, Beniczky and colleagues, with two Neuro Event Labs co-authors, reported 93.7% sensitivity for major motor seizures (Brain and Behavior, 8 August 2022). All 11 tonic-clonic and four of five hypermotor seizures were detected. False detections were 0.16 per hour, device deficiency 10.5% of the time, and sensitivity for minor motor seizures only 8.3%. Lennard, Shankar and colleagues of the National Health Service (NHS) trust Cornwall Partnership NHS Foundation Trust then evaluated Nelli in the community. The 15 participants had intellectual disability and drug-resistant epilepsy and could not tolerate routine or ambulatory EEG. The report appeared in Epilepsy & Behavior Reports on 23 April 2023. The source gives no study dates. Nelli recorded 707 possible seizure events in 14 people, 247 of them unheard or unrecognized by carers. One person had half of possible events missed by night staff. Carers logged 165 “restless” or “seizure behavior” episodes that Nelli did not deem seizures. Acceptability was 93%. The authors conclude Nelli “allowed for improved communication and care planning”. A single-center feasibility study in Dravet and Lennox-Gastaut syndromes (Wilms et al., Epilepsia, online 9 October 2025; abstract read) ran four weeks of home monitoring. It detected 368 seizures with 97.8% accuracy, while only 26.1% were logged in diaries. No individual's changed outcome is documented in any cited source.", "replication": { "produced_by": [ "A camera and microphone unit streaming to a server where computer-vision and AI algorithms flag motor events", "Night-time use for people with suspected nocturnal seizures who could not tolerate EEG, with family or carer consent", "Clinician review of flagged clips (the Cornwall paper says 8–9% of the recording is sampled by an epilepsy expert)" ], "must_hold": [ "Device deficiency kept low (10.5% of monitored time in the Danish validation)", "Staff available at night to act on alarms, the setting the validators say the system is suited to", "The target is major motor seizures; minor motor seizures were detected at 8.3% sensitivity" ], "echoes": [ { "date": "2025-10-09", "setting": "Single-center feasibility study, 20 patients with Dravet or Lennox-Gastaut syndrome, four weeks of home monitoring in bed, August 2023 to March 2024", "outcome": "recurred", "source": "Wilms LK, Lossius MI, Annala K, Abdel-Khalik J, Fanter L, Elomaa K, Peltola J (2026) Epilepsia 67(1):164-174, doi:10.1111/epi.18640 (abstract: 368 seizures detected with 97.8% accuracy; 'only 26.1% of the seizures were documented' in diaries; vendor co-authors)" } ] } } }, "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-0172", "case_url": "records/AIBD-0172.html", "reason": "The developer is headquartered in United States; the case names Germany.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Germany" }, { "label": "Case location", "value": "Germany" } ], "filters": { "origin": "United States", "destination": [ "Germany" ] }, "record": { "id": "AIBD-0172", "title": "Germany deploys IdentiFlight cameras to slow turbines for lesser spotted eagles", "summary": "The OECD AI Incidents Monitor records Germany's deployment of IdentiFlight AI cameras to detect lesser spotted eagles near wind turbines and slow the blades (September 2022); no German outcome data.", "date_observed": "2022-09-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The OECD AI Incidents entry summarizes press coverage of 20 and 21 September 2022 (The Guardian, Mother Jones, The Register and others, not among the sources). It says Germany is deploying AI-powered camera systems developed by IdentiFlight to detect endangered lesser spotted eagles near wind turbines. They slow the blades to prevent fatal collisions, as wind-farm expansion raises the risk to the species. The monitor's rationale cites the 82% reduction reported from the Wyoming study rather than any German measurement. 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OekoFor portfolio page on the German validation of IdentiFlight.", "title": "IdentiFlight", "url": "http://www.oekofor.de/en/portfolio/idf_en/", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "The OECD AI Incidents Monitor records Germany's deployment of IdentiFlight AI cameras to detect lesser spotted eagles near wind turbines and slow the blades (September 2022); no German outcome data.", "account": "The OECD AI Incidents entry summarizes press coverage of 20 and 21 September 2022 (The Guardian, Mother Jones, The Register and others, not among the sources). 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Only the registry page is among the sources; the sites, operators and any fatality data in Germany are not documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "IdentiFlight automated eagle detection and turbine curtailment" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "IdentiFlight" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Germany deploys IdentiFlight cameras to slow turbines for lesser spotted eagles", "headline": "Germany deploys IdentiFlight to protect lesser spotted eagles" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "d681e6db13db4697d9388094d32b787ef2bd96acf429df05405aae7a98d10f6d" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents entry summarizes press coverage of 20 and 21 September 2022 (The Guardian, Mother Jones, The Register and others, not among the sources). It says Germany is deploying AI-powered camera systems developed by IdentiFlight to detect endangered lesser spotted eagles near wind turbines. They slow the blades to prevent fatal collisions, as wind-farm expansion raises the risk to the species. The monitor's rationale cites the 82% reduction reported from the Wyoming study rather than any German measurement. 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The fieldwork is dated only to 2018 and 2019. It evaluated the EasyScan GO, a digital microscopy device whose convolutional-neural-network software was developed by Intellectual Ventures' Global Good Fund. Motic built its production prototype. The 11 sites were in 11 countries: Burkina Faso, Kenya, Republic of the Congo, Senegal, South Africa, Uganda, Bangladesh, Cambodia, Nepal, Thailand and Brazil. In total 2,250 Giemsa-stained blood films were read independently by expert microscopists and by the device. Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0); with good-quality slides specificity rose to 85.1%. Species were identified accurately in 93% of P. falciparum and 92% of P. vivax samples. Parasite density estimates were within 25% of the reference in 23% of slides. The authors conclude the device meets WHO-TDR level 2 criteria for detection and species identification and level 4 for quantification and false-positive rate. They say all parameters were significantly affected by slide quality, and the study did not evaluate performance under routine field operation. The study was funded by the Global Good Fund, which donated the devices; six co-authors were its employees. No patient-management outcome was measured.", "category": "general benefit", "unexpected": false, "purpose": "Automated detection, species identification and quantification of malaria parasites in blood films to replace or support expert microscopy. 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The evidence is a single peer-reviewed study funded by the maker (Global Good Fund) with maker-employed co-authors who provided inputs to study design and training. The authors state the study did not evaluate performance under routine field operation and that all parameters were significantly affected by slide quality. No independent or outcome-level evidence was found." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Das, D., Vongpromek, R., Assawariyathipat, T., et al. (2022). Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal 21, 122. doi:10.1186/s12936-022-04146-1.", "title": "Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning", "url": "https://doi.org/10.1186/s12936-022-04146-1", "date": "2022-04-12", "written_by": "makers", "quote": "The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9-92.7), and specificity 75.6% (95% CI 73.1-78.0)." }, { "kind": "peer-reviewed study", "citation": "Das, D., et al. (2022). Same article, full text (methods and limitations). Europe PMC PMC9004086.", "title": "Field evaluation of the diagnostic performance of EasyScan GO (full text)", "url": "https://europepmc.org/article/PMC/PMC9004086", "date": "2022-04-12", "written_by": "makers", "quote": "In addition, the study did not evaluate the performance of the EasyScan Go under routine field operation." }, { "kind": "peer-reviewed study", "citation": "Das, D., et al. (2022). Same article, competing interests statement. Europe PMC PMC9004086.", "title": "Field evaluation of the diagnostic performance of EasyScan GO (competing interests)", "url": "https://europepmc.org/article/PMC/PMC9004086", "date": "2022-04-12", "written_by": "makers", "quote": "MPH, SKM, CM, SB, CBD and CB were employees of the Global Good Fund, Intellectual Ventures at the time of study implementation" }, { "kind": "peer-reviewed study", "citation": "Das, D., Vongpromek, R., Assawariyathipat, T., et al. (2022). Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal 21, 122. doi:10.1186/s12936-022-04146-1. Full text as served by the Europe PMC REST API (PMC9004086).", "title": "Field evaluation of the diagnostic performance of EasyScan GO (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML", "date": "2022-04-12", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "EasyScan GO devices donated and set up with manufacturer training at each site", "Giemsa-stained thick blood films read in parallel by two expert microscopists as reference", "STARD-reported multi-center observational design across 11 countries (ClinicalTrials.gov NCT03512678)" ], "must_hold": [ "Good slide quality, which the authors found significantly affected all performance parameters", "Parasitemia above about 200 parasites/uL, below which sensitivity fell to 57%", "Thick-film-only software (the tested version did not separate non-falciparum species)" ], "echoes": [ { "date": null, "setting": "Earlier field test of the first thick-film version in Thailand, 2014–2015, reported in the same article's background", "model_version": "first thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" }, { "date": null, "setting": "Thailand and Indonesia, 2016–2017: the article reports sensitivity 89% and specificity 97% for the second thick-film algorithm against expert microscopy", "model_version": "second thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" } ] }, "paired_incident": null, "headline": "EasyScan GO reaches 91.1% sensitivity for malaria at 11 sites", "metric": "Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0)", "comparator": { "against": "expert microscopy", "before": null, "after": "diagnostic sensitivity of the EasyScan Go device was 91.1% (95% CI 88.9–92.7) and specificity was 75.6% (95% CI 73.1–78.0)", "basis": "measured", "quote": "Compared to reference microscopy, the diagnostic sensitivity of the EasyScan Go device was 91.1% (95% CI 88.9–92.7) and specificity was 75.6% (95% CI 73.1–78.0) (Table 1).", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML" }, "later": { "status": "unknown", "date": null, "note": "No later evaluation, approval or deployment is reported in the article.", "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": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML", "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 added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Intellectual_Ventures", "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": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: three peer-reviewed studies from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0352", "catalog_version": "1.0.0", "source_fingerprint": "165d995b2ec7578feda05d5f3990957643789fa9131b19484d3c70473c63db0f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0352-source-0001", "AIBD-0352-source-0002", "AIBD-0352-source-0003", "AIBD-0352-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": "Intellectual Ventures' Global Good Fund (software and original hardware); Motic (production prototype device, software licensed to Motic)", "deployer": "Research sites in 11 countries under a multi-center study with University of Oxford / Mahidol-Oxford Tropical Medicine Research Unit-affiliated investigators (research use, not routine 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": "The machine-learning device produced the readings compared against expert microscopy, so the measured accuracy is the AI's own performance. 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No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "version1.15.17b" } }, "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": "Version 1.15.17b.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "EasyScan GO digital malaria microscopy device with convolutional-neural-network image analysis of thick blood films" } }, "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": "Intellectual Ventures' Global Good Fund (software and original hardware); Motic (production prototype device, software licensed to Motic)" } }, "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": "EasyScan GO machine-learning malaria microscopy reaches 91.1% sensitivity and 75.6% specificity against expert microscopy at 11 sites; accuracy study only, manufacturer-funded", "headline": "EasyScan GO reaches 91.1% sensitivity for malaria at 11 sites" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "165d995b2ec7578feda05d5f3990957643789fa9131b19484d3c70473c63db0f" ], "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 multi-center observational study was published in Malaria Journal on 12 April 2022 (Das et al.). The fieldwork is dated only to 2018 and 2019. It evaluated the EasyScan GO, a digital microscopy device whose convolutional-neural-network software was developed by Intellectual Ventures' Global Good Fund. Motic built its production prototype. The 11 sites were in 11 countries: Burkina Faso, Kenya, Republic of the Congo, Senegal, South Africa, Uganda, Bangladesh, Cambodia, Nepal, Thailand and Brazil. In total 2,250 Giemsa-stained blood films were read independently by expert microscopists and by the device. Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0); with good-quality slides specificity rose to 85.1%. Species were identified accurately in 93% of P. falciparum and 92% of P. vivax samples. Parasite density estimates were within 25% of the reference in 23% of slides. The authors conclude the device meets WHO-TDR level 2 criteria for detection and species identification and level 4 for quantification and false-positive rate. They say all parameters were significantly affected by slide quality, and the study did not evaluate performance under routine field operation. The study was funded by the Global Good Fund, which donated the devices; six co-authors were its employees. No patient-management outcome was measured.", "replication": { "produced_by": [ "EasyScan GO devices donated and set up with manufacturer training at each site", "Giemsa-stained thick blood films read in parallel by two expert microscopists as reference", "STARD-reported multi-center observational design across 11 countries (ClinicalTrials.gov NCT03512678)" ], "must_hold": [ "Good slide quality, which the authors found significantly affected all performance parameters", "Parasitemia above about 200 parasites/uL, below which sensitivity fell to 57%", "Thick-film-only software (the tested version did not separate non-falciparum species)" ], "echoes": [ { "setting": "Earlier field test of the first thick-film version in Thailand, 2014–2015, reported in the same article's background", "model_version": "first thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" }, { "setting": "Thailand and Indonesia, 2016–2017: the article reports sensitivity 89% and specificity 97% for the second thick-film algorithm against expert microscopy", "model_version": "second thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" } ] } } }, "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-0096", "case_url": "records/AIBD-0096.html", "reason": "The developer is headquartered in Netherlands; the case names Nigeria.", "details": [ { "label": "Developer headquarters", "value": "Netherlands" }, { "label": "Case jurisdictions", "value": "Nigeria" }, { "label": "Case location", "value": "Eight states, Nigeria" } ], "filters": { "origin": "Netherlands", "destination": [ "Nigeria" ] }, "record": { "id": "AIBD-0096", "title": "CAD4TB on ultra-portable X-rays screened 94,694 people in eight Nigerian states and diagnosed 2,337 TB cases (2022)", "summary": "KNCV/USAID deployment Jan–Sep 2022: CAD4TB v7 screened 94,694 people in eight Nigerian states; 2,337 TB cases diagnosed, 97% treated", "date_observed": "2022-01-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "KNCV Tuberculosis Foundation Nigeria and the Institute of Human Virology Nigeria (IHVN), funded by USAID, deployed ten ultra-portable digital X-ray units. This was under the Stop TB Partnership's Introducing New Tools Project. They ran CAD4TB version 7 (threshold 50) for community active case finding in eight states (Katsina, Kano, Nasarawa, Benue, Delta, Cross River, Osun, Oyo). The period was January to September 2022 (day not given; first day of month used). Under a parallel algorithm, every client had both symptom and chest X-ray screening. Anyone positive on either (a CAD4TB score of 50 or above) was classified as presumptive TB and sent for Xpert testing and/or clinical assessment. Of those diagnosed, 1,614 (69.1%) scored 50 or above and 723 (30.9%) scored below 50. The program screened 94,694 people aged four and over and identified 10,236 presumptive cases. It diagnosed 2,337 TB cases (920 bacteriologically confirmed, 1,386 clinically diagnosed, 31 rifampicin-resistant). 97.3% started treatment, and the average number needed to screen (NNS) was 38. The paper's tables tally 2,481 diagnoses (986 bacteriologically confirmed, 1,466 clinically diagnosed, 29 rifampicin-resistant) with 2,414 (97.3%) started on treatment. They derive the NNS of 38 from that tally; its text gives 2,337 diagnoses. A companion six-state analysis reported 85,910 screened, 1,915 diagnosed and 1,869 treated.", "category": "general benefit", "unexpected": false, "purpose": "Triage chest X-rays to select presumptive TB cases for molecular testing during community active case finding.", "system": "CAD4TB computer-aided detection on ultra-portable digital chest X-ray", "model_version": "CAD4TB version 7, threshold score 50 or above", "developer": "Delft Imaging", "deployer": "KNCV Tuberculosis Foundation Nigeria and Institute of Human Virology Nigeria with the National TB and Leprosy Control Program, funded by USAID", "location": "Eight states, Nigeria", "domain": "Global health: tuberculosis case finding", "segment": "Public health program", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Aggregate screening yield; the sources do not document any individual's outcome attributable to the software." }, { "group": "organizational", "depth": null, "reason": "Efficiency figures (NNS 38, NNT 4) are program aggregates without a discrete attributable event." }, { "group": "societal", "depth": null, "reason": "2,337 diagnoses are an aggregate program result." } ], "ai_role": { "status": "assessed", "statement": "A parallel algorithm classified anyone positive on symptom screening or with a CAD4TB score of 50 or above as presumptive TB. They went for Xpert testing and/or clinical assessment, so the AI score was one of two gates to testing, not the sole gate. Of the 2,337 diagnosed, 1,614 (69.1%) scored 50 or above and 723 (30.9%) scored below 50. The TB yield also depends on X-ray reach, Xpert availability and treatment linkage. Both papers are authored by implementing organizations (KNCV Nigeria, IHVN, USAID, NTBLCP), not the vendor." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Alege, A., Useni, S., Ihesie, A., Eneogu, R., Agbaje, A., et al. (2025). Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria. Frontiers in Digital Health.", "title": "Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12257310/", "date": "2025-06-30", "written_by": "deployer", "quote": "A total of 94,694 subjects aged 4 years and above were screened for TB" }, { "kind": "peer-reviewed study", "citation": "Useni, S., Odume, B., Tukur, M., et al. (2025). Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria. Public Health Action 15(1), 17–20.", "title": "Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11841120/", "date": "2025-03-01", "written_by": "deployer", "quote": "Screened 85,910 persons and identified 8,416 presumptive TB cases. Among these, 1,915 TB cases were diagnosed, and 1,869 patients were put on treatment." }, { "kind": "report", "citation": "KNCV Nigeria (2026). 2025 Annual Report. Knowledge Network for disease Control and Vigilance, Abuja (PDF posted March 2026; day not given, first day of month used).", "title": "Knowledge Network for disease Control and Vigilance 2025 Annual Report", "url": "https://kncvnigeria.org/wp-content/uploads/2026/03/2026-KNCV-Nigeria-Annual-Report.pdf", "date": "2026-03-01", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "Ultra-portable X-ray units with CAD4TB v7 at threshold 50", "Community mobilization and same-day Xpert testing", "USAID funding through the iNTP project" ], "must_hold": [ "Threshold calibrated to local prevalence", "Xpert cartridge supply and sample transport", "Treatment linkage within days" ], "echoes": [ { "date": "2022-01-01", "setting": "Six-state analysis of the same deployment (Benue, Cross River, Delta, Kano, Katsina, Nasarawa)", "model_version": "CAD4TB v7", "outcome": "recurred", "source": "Useni et al. (2025) Public Health Action, https://pmc.ncbi.nlm.nih.gov/articles/PMC11841120/" }, { "date": null, "setting": "Routine active case finding with digital X-ray and CAD4TB in Nigerian penitentiary institutions", "model_version": null, "outcome": "unknown", "source": "Chukwuogo et al. (2026) BMC Infectious Diseases, DOI 10.1186/s12879-026-12627-w (Europe PMC listing only)" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "81", "title": "Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers", "url": "https://incidentdatabase.ai/cite/81", "severity": null, "group": "personal", "basis": "Chest X-ray classification systems, the same kind as CAD4TB, underdiagnosed economically vulnerable, female and Hispanic patients because of biased training data." }, "headline": "CAD4TB triage of 94,694 people in Nigeria yields 2,337 TB cases", "metric": "2,337 TB cases diagnosed, 97% treated", "comparator": { "against": "KNCV Nigeria's other 2022 case-finding strategies in the same states without portable X-ray (intensified case finding in public and private facilities, medicine vendors and community pharmacists, contact investigation, community active case finding)", "before": "NNS 205.6 (public facilities), 179.0 (private facilities), 127.8 (medicine vendors and community pharmacists), 49.0 (contact investigation), 388.5 (community active case-finding)", "after": "NNS 44.9 with PDX-CAD", "basis": "measured", "quote": "During the same period, 3,867,059 persons were screened for TB in public and private health facilities, 19,140 TB cases were detected, and 18,625 (97.3%) were successfully placed on treatment. The NNS was 205.6 and 127.8, respectively. The NNS was quite high compared to 44.9 in the PDX-CAD system.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11841120/" }, "later": { "status": "scaled", "date": "2025-09-30", "note": "KNCV Nigeria's 2025 annual report says the IMPACT project completed deployment of 390 portable digital X-ray units with AI across all 36 states and the FCT, adding 266 units in 2025, and screened over 780,000 people with over 23,000 TB cases diagnosed in January to September 2025.", "quote": "In 2025, a total of 266 additional PDX platforms were deployed for use across all geo-political zones of the country. In the first three quarters of 2025 (January - September), over 780 thousand persons were screened for TB using the portable digital X-rays, and over 23 thousand TB cases diagnosed using this tool.", "url": "https://kncvnigeria.org/wp-content/uploads/2026/03/2026-KNCV-Nigeria-Annual-Report.pdf" }, "value": null, "developer_country": "Netherlands", "deployer_country": "Nigeria", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 81 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/81", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Rewrote the AI-role statement and the account so that the CAD4TB score is one of two gates in the paper's parallel algorithm (symptom screen or CAD score of 50 or above) rather than the sole gate to Xpert testing, added the split of diagnosed patients by CAD score (1,614 at 50 or above, 723 below), and noted that the 97.3% treatment rate and the NNS of 38 come from the tables' tally of 2,481 diagnoses while the text gives 2,337.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12257310/", "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://pmc.ncbi.nlm.nih.gov/articles/PMC11841120/; https://kncvnigeria.org/wp-content/uploads/2026/03/2026-KNCV-Nigeria-Annual-Report.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://delftimaging.com/contact/; https://kncvnigeria.org/contact-us/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: two peer-reviewed studies from the deployer and the deployer's report. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0096", "catalog_version": "1.0.0", "source_fingerprint": "ceffb7ad68c5d4fcc0c7eba5b208ba4b42e015ce6a5523caedff0421dba02726", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0096-source-0001", "AIBD-0096-source-0002", "AIBD-0096-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": "Delft Imaging", "deployer": "KNCV Tuberculosis Foundation Nigeria and Institute of Human Virology Nigeria with the National TB and Leprosy Control Program, funded by USAID" } }, "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": "A parallel algorithm classified anyone positive on symptom screening or with a CAD4TB score of 50 or above as presumptive TB. 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"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": "2,337 TB cases diagnosed, 97% treated", "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": 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In the first three quarters of 2025 (January - September), over 780 thousand persons were screened for TB using the portable digital X-rays, and over 23 thousand TB cases diagnosed using this tool.", "url": "https://kncvnigeria.org/wp-content/uploads/2026/03/2026-KNCV-Nigeria-Annual-Report.pdf" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0096" } }, "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": "KNCV Tuberculosis Foundation Nigeria and the Institute of Human Virology Nigeria (IHVN), funded by USAID, deployed ten ultra-portable digital X-ray units. This was under the Stop TB Partnership's Introducing New Tools Project. They ran CAD4TB version 7 (threshold 50) for community active case finding in eight states (Katsina, Kano, Nasarawa, Benue, Delta, Cross River, Osun, Oyo). The period was January to September 2022 (day not given; first day of month used). Under a parallel algorithm, every client had both symptom and chest X-ray screening. Anyone positive on either (a CAD4TB score of 50 or above) was classified as presumptive TB and sent for Xpert testing and/or clinical assessment. Of those diagnosed, 1,614 (69.1%) scored 50 or above and 723 (30.9%) scored below 50. The program screened 94,694 people aged four and over and identified 10,236 presumptive cases. It diagnosed 2,337 TB cases (920 bacteriologically confirmed, 1,386 clinically diagnosed, 31 rifampicin-resistant). 97.3% started treatment, and the average number needed to screen (NNS) was 38. The paper's tables tally 2,481 diagnoses (986 bacteriologically confirmed, 1,466 clinically diagnosed, 29 rifampicin-resistant) with 2,414 (97.3%) started on treatment. They derive the NNS of 38 from that tally; its text gives 2,337 diagnoses. A companion six-state analysis reported 85,910 screened, 1,915 diagnosed and 1,869 treated.", "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": "Aggregate screening yield; the sources do not document any individual's outcome attributable to the software." }, { "group": "organizational", "reason": "Efficiency figures (NNS 38, NNT 4) are program aggregates without a discrete attributable event." }, { "group": "societal", "reason": "2,337 diagnoses are an aggregate program result." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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(2025). Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria. Frontiers in Digital Health.", "title": "Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12257310/", "date": "2025-06-30", "written_by": "deployer", "quote": "A total of 94,694 subjects aged 4 years and above were screened for TB" }, { "kind": "peer-reviewed study", "citation": "Useni, S., Odume, B., Tukur, M., et al. (2025). Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria. Public Health Action 15(1), 17–20.", "title": "Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11841120/", "date": "2025-03-01", "written_by": "deployer", "quote": "Screened 85,910 persons and identified 8,416 presumptive TB cases. Among these, 1,915 TB cases were diagnosed, and 1,869 patients were put on treatment." }, { "kind": "report", "citation": "KNCV Nigeria (2026). 2025 Annual Report. Knowledge Network for disease Control and Vigilance, Abuja (PDF posted March 2026; day not given, first day of month used).", "title": "Knowledge Network for disease Control and Vigilance 2025 Annual Report", "url": "https://kncvnigeria.org/wp-content/uploads/2026/03/2026-KNCV-Nigeria-Annual-Report.pdf", "date": "2026-03-01", "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-0096-source-0001", "AIBD-0096-source-0002", "AIBD-0096-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Alege, A., Useni, S., Ihesie, A., Eneogu, R., Agbaje, A., et al. (2025). Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria. Frontiers in Digital Health.", "title": "Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12257310/", "date": "2025-06-30", "written_by": "deployer", "quote": "A total of 94,694 subjects aged 4 years and above were screened for TB" }, { "kind": "peer-reviewed study", "citation": "Useni, S., Odume, B., Tukur, M., et al. (2025). Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria. Public Health Action 15(1), 17–20.", "title": "Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11841120/", "date": "2025-03-01", "written_by": "deployer", "quote": "Screened 85,910 persons and identified 8,416 presumptive TB cases. Among these, 1,915 TB cases were diagnosed, and 1,869 patients were put on treatment." }, { "kind": "report", "citation": "KNCV Nigeria (2026). 2025 Annual Report. Knowledge Network for disease Control and Vigilance, Abuja (PDF posted March 2026; day not given, first day of month used).", "title": "Knowledge Network for disease Control and Vigilance 2025 Annual Report", "url": "https://kncvnigeria.org/wp-content/uploads/2026/03/2026-KNCV-Nigeria-Annual-Report.pdf", "date": "2026-03-01", "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": "KNCV/USAID deployment Jan–Sep 2022: CAD4TB v7 screened 94,694 people in eight Nigerian states; 2,337 TB cases diagnosed, 97% treated", "account": "KNCV Tuberculosis Foundation Nigeria and the Institute of Human Virology Nigeria (IHVN), funded by USAID, deployed ten ultra-portable digital X-ray units. This was under the Stop TB Partnership's Introducing New Tools Project. They ran CAD4TB version 7 (threshold 50) for community active case finding in eight states (Katsina, Kano, Nasarawa, Benue, Delta, Cross River, Osun, Oyo). The period was January to September 2022 (day not given; first day of month used). Under a parallel algorithm, every client had both symptom and chest X-ray screening. Anyone positive on either (a CAD4TB score of 50 or above) was classified as presumptive TB and sent for Xpert testing and/or clinical assessment. Of those diagnosed, 1,614 (69.1%) scored 50 or above and 723 (30.9%) scored below 50. The program screened 94,694 people aged four and over and identified 10,236 presumptive cases. It diagnosed 2,337 TB cases (920 bacteriologically confirmed, 1,386 clinically diagnosed, 31 rifampicin-resistant). 97.3% started treatment, and the average number needed to screen (NNS) was 38. The paper's tables tally 2,481 diagnoses (986 bacteriologically confirmed, 1,466 clinically diagnosed, 29 rifampicin-resistant) with 2,414 (97.3%) started on treatment. They derive the NNS of 38 from that tally; its text gives 2,337 diagnoses. A companion six-state analysis reported 85,910 screened, 1,915 diagnosed and 1,869 treated." } }, "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": "CAD4TB scored chest X-rays for signs of tuberculosis." }, "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 CAD4TB version 7 with a threshold score of 50." }, "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": "CAD4TB version 7, threshold score 50 or above" } }, "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": "CAD4TB version 7.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "CAD4TB computer-aided detection on ultra-portable digital chest 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": "Delft Imaging" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "CAD4TB on ultra-portable X-rays screened 94,694 people in eight Nigerian states and diagnosed 2,337 TB cases (2022)", "headline": "CAD4TB triage of 94,694 people in Nigeria yields 2,337 TB 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": [ "ceffb7ad68c5d4fcc0c7eba5b208ba4b42e015ce6a5523caedff0421dba02726" ], "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": "KNCV Tuberculosis Foundation Nigeria and the Institute of Human Virology Nigeria (IHVN), funded by USAID, deployed ten ultra-portable digital X-ray units. This was under the Stop TB Partnership's Introducing New Tools Project. They ran CAD4TB version 7 (threshold 50) for community active case finding in eight states (Katsina, Kano, Nasarawa, Benue, Delta, Cross River, Osun, Oyo). The period was January to September 2022 (day not given; first day of month used). Under a parallel algorithm, every client had both symptom and chest X-ray screening. Anyone positive on either (a CAD4TB score of 50 or above) was classified as presumptive TB and sent for Xpert testing and/or clinical assessment. Of those diagnosed, 1,614 (69.1%) scored 50 or above and 723 (30.9%) scored below 50. The program screened 94,694 people aged four and over and identified 10,236 presumptive cases. It diagnosed 2,337 TB cases (920 bacteriologically confirmed, 1,386 clinically diagnosed, 31 rifampicin-resistant). 97.3% started treatment, and the average number needed to screen (NNS) was 38. The paper's tables tally 2,481 diagnoses (986 bacteriologically confirmed, 1,466 clinically diagnosed, 29 rifampicin-resistant) with 2,414 (97.3%) started on treatment. They derive the NNS of 38 from that tally; its text gives 2,337 diagnoses. A companion six-state analysis reported 85,910 screened, 1,915 diagnosed and 1,869 treated.", "replication": { "produced_by": [ "Ultra-portable X-ray units with CAD4TB v7 at threshold 50", "Community mobilization and same-day Xpert testing", "USAID funding through the iNTP project" ], "must_hold": [ "Threshold calibrated to local prevalence", "Xpert cartridge supply and sample transport", "Treatment linkage within days" ], "echoes": [ { "date": "2022-01-01", "setting": "Six-state analysis of the same deployment (Benue, Cross River, Delta, Kano, Katsina, Nasarawa)", "model_version": "CAD4TB v7", "outcome": "recurred", "source": "Useni et al. (2025) Public Health Action, https://pmc.ncbi.nlm.nih.gov/articles/PMC11841120/" }, { "setting": "Routine active case finding with digital X-ray and CAD4TB in Nigerian penitentiary institutions", "outcome": "unknown", "source": "Chukwuogo et al. (2026) BMC Infectious Diseases, DOI 10.1186/s12879-026-12627-w (Europe PMC listing only)" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 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-0354", "case_url": "records/AIBD-0354.html", "reason": "The developer is headquartered in Netherlands; the case names Philippines.", "details": [ { "label": "Developer headquarters", "value": "Netherlands" }, { "label": "Case jurisdictions", "value": "Philippines" }, { "label": "Case location", "value": "Philippines (Regions 5, 8 and 13)" } ], "filters": { "origin": "Netherlands", "destination": [ "Philippines" ] }, "record": { "id": "AIBD-0354", "title": "UN OCHA's CERF anticipatory action framework for Philippine typhoons adopted 510's machine-learning impact model as trigger in 2021; no activation on that trigger documented to 2025", "summary": "OCHA frameworks (2021, 2023, 2025) use 510's XGBoost house-damage model as activation trigger; near-activation Dec 2021 (Rai); the Nov 2025 activation for Uwan ran on the 185 km/h wind trigger.", "date_observed": "2021-09-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "OCHA's Anticipatory Action (AA) Framework for Philippine typhoons was posted on ReliefWeb on 15 September 2021. It set up a US$7.5 million CERF pilot for about 270,500 people in Regions 5 and 8 with FAO, IOM, UNFPA, UNICEF and WFP. It has a two-stage trigger. The readiness trigger is PAGASA and 510 forecasts of a category 4 or stronger cyclone. The activation trigger, within 72 hours of landfall, is the IFRC/Netherlands Red Cross 510 probabilistic typhoon impact prediction model's predicted number of totally damaged houses. The framework says that model partially uses a machine learning model relating wind speed to housing damage. 510's 2024 NHESS paper (vendor-authored) states the XGBoost model served in 2021 as the trigger model for the UN OCHA AA pilot. OCHA's After Action Review of 31 March 2022 records that Typhoon Rai (Odette) in December 2021 rapidly intensified and did not meet the activation criteria. There was a near activation with staff pre-deployed. The 2023 revision retained the 510 model. The framework endorsed on 19 October 2025 covers US$6 million CERF and about 955,507 people in Regions 5, 8 and 13. OCHA and its Centre for Humanitarian Data compute predicted totally damaged buildings with the 510 model every 6–12 hours. It adds an observational trigger for super typhoons of 185 km/h or more reported by PAGASA. OCHA's June 2025 report says the readiness trigger was met for Super Typhoon Pepito (Man-yi) in its November 2024 to April 2025 reporting period. Activation thresholds were not reached and CERF AA resources were reallocated to the response. On 6 November 2025 CERF AA was activated for Super Typhoon Fung-Wong (Uwan) after the observational wind-speed threshold was triggered. OCHA reports nearly US$6 million released and WFP alone reaching 42,100 families. No cited document records an activation produced by the 510 model's output.", "category": "general benefit", "unexpected": false, "purpose": "Forecast the number of totally damaged houses from typhoon track, wind and rainfall forecasts so that CERF-funded early actions can be triggered before landfall. The benefit claimed (AI-triggered anticipatory payouts) is that purpose, but no activation on the model's output is documented.", "system": "510 probabilistic typhoon impact model (XGBoost) predicting totally damaged houses, used as the CERF anticipatory action activation trigger", "model_version": "probabilistic version using ECMWF ensemble tracks (revamped from deterministic, 2021)", "developer": "510, an initiative of the Netherlands Red Cross, with IFRC", "deployer": "UN OCHA Philippines with the CERF AA Core Group (FAO, IOM, UNFPA, UNICEF, WFP); impact estimates generated by 510 (2021 framework) and by OCHA with its Centre for Humanitarian Data (2025 framework)", "location": "Philippines (Regions 5, 8 and 13)", "domain": "Humanitarian anticipatory action / typhoon impact forecasting", "segment": "UN-coordinated anticipatory financing (CERF)", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The model supplies impact estimates that decide whether anticipatory funds are released, and no activation attributable to it is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No activation attributable to the model's output is documented, so no individual-level outcome can be assigned to the AI." }, { "group": "organizational", "depth": null, "reason": "OCHA adopted the model as its trigger and computes estimates each cycle. The only documented activation (6 November 2025) was triggered by the observational wind-speed threshold." }, { "group": "societal", "depth": null, "reason": "Pre-landfall assistance in November 2025 (WFP reports 42,100 families) is documented, but not attributed to the AI model by any cited source." } ], "ai_role": { "status": "assessed", "statement": "OCHA's 2021, 2023 and 2025 frameworks name the IFRC/510 impact model as the activation-trigger model. The 2021 framework describes it as partially a machine learning model. This is documented by the deployer, not only the maker. The model's forecasts were monitored during the December 2021 near activation (Typhoon Rai) without meeting criteria. The 2024 readiness phase for Pepito ended with CERF AA resources reallocated to response. OCHA's flash update attributes the 6 November 2025 activation for Uwan to the observational threshold (forecast peak 195 km/h against the 185 km/h trigger). It does not state that the 510 model's estimates reached the activation matrix. The AI's contribution to any payout is therefore not established. The maker's NHESS paper is the only source describing the algorithm (XGBoost)." }, "evidence": [ { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022. ReliefWeb, posted 15 September 2021 (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-2021-2022", "date": "2021-09-15", "written_by": "deployer", "quote": "The Model includes a trigger mechanism based on the ECMWF forecast and IFRC/ Netherlands Red Cross 510 probabilistic typhoon impact prediction model developed for the Philippines" }, { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022, model checklist and model selection sections (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022 (model selection)", "url": "https://reliefweb.int/attachments/78d0268e-b476-3a55-ab82-94e7478fc408/Anticipatory%20Action%20Framework%20-%20Philippines%20-%20Typhoons%20-%202021-2022.pdf", "date": "2021-09-15", "written_by": "deployer", "quote": "Partially: Uses a machine learning model to determine relationship between wind speed and housing damage." }, { "kind": "report", "citation": "OCHA Philippines (2022). Report on the After Action Review for CERF Anticipatory Action, 31 March 2022. ReliefWeb (PDF dated 22 April 2022).", "title": "After Action Review on the CERF Anticipatory Action for Typhoons", "url": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons", "date": "2022-03-31", "written_by": "deployer", "quote": "While the typhoon at the end did not meet the AA pilot activation criteria, the CERF AA Core Group was closely monitoring the situation" }, { "kind": "report", "citation": "OCHA (2023). Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision. unocha.org, 3 October 2023 (PDF).", "title": "Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision", "url": "https://www.unocha.org/publications/report/philippines/philippines-cerf-aa-anticipatory-action-framework-2023-revision", "date": "2023-10-03", "written_by": "deployer", "quote": "Tropical Cyclone with potential to reach category level 3 or higher" }, { "kind": "report", "citation": "OCHA (2025). Anticipatory Action Framework Philippines - Tropical Cyclones (as of 03 October 2025, endorsed 19 October 2025). ReliefWeb, posted 4 November 2025 (PDF).", "title": "Anticipatory Action Framework Philippines - Tropical Cyclones (As of 03 October 2025)", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-tropical-cyclones-03-october-2025", "date": "2025-10-19", "written_by": "deployer", "quote": "This will be used to compare against 510 model outputs at 72 hours before landfall, in order to account for varying regional vulnerabilities." }, { "kind": "report", "citation": "OCHA (2025). Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1 (as of 07 November, 9 p.m. local time). ReliefWeb.", "title": "Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "date": "2025-11-07", "written_by": "deployer", "quote": "CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered" }, { "kind": "press release", "citation": "Paulose, H. (2026). How anticipatory action shaped the response to the Philippines' Twin Typhoons. OCHA Financing and Outreach Division, un.org, January 2026.", "title": "How anticipatory action shaped the response to the Philippines' Twin Typhoons", "url": "https://www.un.org/en/delegate/how-anticipatory-action-shaped-response-philippines%E2%80%99-twin-typhoons", "date": "2026-01-19", "written_by": "deployer", "quote": "WFP alone reached 42,100 families through its anticipatory financing mechanism" }, { "kind": "report", "citation": "OCHA (2025). Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report (November 2024 - April 2025). unocha.org, 27 June 2025.", "title": "Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report", "url": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025", "date": "2025-06-27", "written_by": "deployer", "quote": "CERF AA resources were reallocated to support the emergency response following Super Typhoon Pepito (Man-yi)" }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309-329, 1 February 2024.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "in 2021 as the trigger model for the UN OCHA AA pilot" } ], "replication": { "produced_by": [ "A pre-arranged CERF allocation with agency action plans ready before the typhoon season", "ECMWF ensemble track, wind and rainfall forecasts fed to the 510 impact model every 6–12 hours", "Historical house-damage data linking hazard, vulnerability and exposure to train the model", "A threshold matrix of predicted totally damaged houses per region with a 1-in-3.5-year return period (2025)" ], "must_hold": [ "Forecast lead time of at least 72 hours; rapid intensification (Rai 2021) shortened lead time below the trigger window", "Model impact estimates reaching the regional threshold matrix, which no read activation record documents", "Agency delivery capacity (financial service providers, data-sharing agreements) in the 72 hours before landfall", "PAGASA and ECMWF data availability" ], "echoes": [ { "date": null, "setting": "Typhoon Rai (Odette), December 2021: forecasts monitored, activation criteria not met, staff pre-deployed (near activation)", "model_version": "2021 probabilistic version", "outcome": "did not recur", "source": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons" }, { "date": null, "setting": "Super Typhoon Pepito (Man-yi), within OCHA's November 2024 to April 2025 reporting period: readiness trigger met, activation thresholds not reached, CERF AA resources reallocated to emergency response", "model_version": null, "outcome": "did not recur", "source": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025" }, { "date": "2025-11-06", "setting": "Super Typhoon Fung-Wong (Uwan): CERF AA activated on the observational 185 km/h threshold; nearly US$6 million released; WFP reports 42,100 families reached; AI model's role not stated", "model_version": null, "outcome": "unknown", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } ] }, "paired_incident": null, "headline": "OCHA uses 510's model as CERF trigger; no activation documented", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2025-11-06", "note": "The framework stayed in force and CERF anticipatory action was activated for Super Typhoon Fung-Wong (Uwan) on 6 November 2025 with nearly US$6 million, but on the observational 185 km/h wind threshold rather than on the 510 model's output.", "quote": "The CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered. Nearly US$6 million has been allocated to five United Nations agencies to implement AA and early response efforts.", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" }, "value": null, "developer_country": "Netherlands", "deployer_country": "Philippines", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://www.unocha.org/philippines", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, seven reports from the deployer and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0354", "catalog_version": "1.0.0", "source_fingerprint": "fb2e5c80103070fb65e02114473a292ff7832a3193b97eaae26cfce2e51f4adb", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0354-source-0001", "AIBD-0354-source-0002", "AIBD-0354-source-0003", "AIBD-0354-source-0004", "AIBD-0354-source-0005", "AIBD-0354-source-0006", "AIBD-0354-source-0007", "AIBD-0354-source-0008", "AIBD-0354-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "510, an initiative of the Netherlands Red Cross, with IFRC", "deployer": "UN OCHA Philippines with the CERF AA Core Group (FAO, IOM, UNFPA, UNICEF, WFP); impact estimates generated by 510 (2021 framework) and by OCHA with its Centre for Humanitarian Data (2025 framework)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "OCHA's 2021, 2023 and 2025 frameworks name the IFRC/510 impact model as the activation-trigger model. The 2021 framework describes it as partially a machine learning model. This is documented by the deployer, not only the maker. The model's forecasts were monitored during the December 2021 near activation (Typhoon Rai) without meeting criteria. The 2024 readiness phase for Pepito ended with CERF AA resources reallocated to response. OCHA's flash update attributes the 6 November 2025 activation for Uwan to the observational threshold (forecast peak 195 km/h against the 185 km/h trigger). It does not state that the 510 model's estimates reached the activation matrix. The AI's contribution to any payout is therefore not established. The maker's NHESS paper is the only source describing the algorithm (XGBoost)." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "OCHA’s frameworks included the 510 model’s forecasts of damage to housing among the conditions for early action before a typhoon.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "OCHA's 2021, 2023 and 2025 frameworks name the IFRC/510 impact model as the activation-trigger model. The 2021 framework describes it as partially a machine learning model. This is documented by the deployer, not only the maker. The model's forecasts were monitored during the December 2021 near activation (Typhoon Rai) without meeting criteria. The 2024 readiness phase for Pepito ended with CERF AA resources reallocated to response. OCHA's flash update attributes the 6 November 2025 activation for Uwan to the observational threshold (forecast peak 195 km/h against the 185 km/h trigger). It does not state that the 510 model's estimates reached the activation matrix. The AI's contribution to any payout is therefore not established. The maker's NHESS paper is the only source describing the algorithm (XGBoost)." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: the makers' peer-reviewed study, seven reports from the deployer and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No activation attributable to the model's output is documented, so no individual-level outcome can be assigned to the AI." }, { "group": "organizational", "reason": "OCHA adopted the model as its trigger and computes estimates each cycle. The only documented activation (6 November 2025) was triggered by the observational wind-speed threshold." }, { "group": "societal", "reason": "Pre-landfall assistance in November 2025 (WFP reports 42,100 families) is documented, but not attributed to the AI model by any cited source." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No activation attributable to the model's output is documented, so no individual-level outcome can be assigned to the AI." }, { "group": "organizational", "reason": "OCHA adopted the model as its trigger and computes estimates each cycle. The only documented activation (6 November 2025) was triggered by the observational wind-speed threshold." }, { "group": "societal", "reason": "Pre-landfall assistance in November 2025 (WFP reports 42,100 families) is documented, but not attributed to the AI model by any cited source." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "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 available accounts do not establish that the model triggered a payment. OCHA attributed the November 2025 activation for Typhoon Uwan to a wind-speed threshold, without saying that the model’s damage estimates met the activation criteria.", "editor_note": "" } }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2025-11-06", "note": "The framework stayed in force and CERF anticipatory action was activated for Super Typhoon Fung-Wong (Uwan) on 6 November 2025 with nearly US$6 million, but on the observational 185 km/h wind threshold rather than on the 510 model's output.", "quote": "The CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered. Nearly US$6 million has been allocated to five United Nations agencies to implement AA and early response efforts.", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0354" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The model supplies impact estimates that decide whether anticipatory funds are released, and no activation attributable to it is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "OCHA's Anticipatory Action (AA) Framework for Philippine typhoons was posted on ReliefWeb on 15 September 2021. It set up a US$7.5 million CERF pilot for about 270,500 people in Regions 5 and 8 with FAO, IOM, UNFPA, UNICEF and WFP. It has a two-stage trigger. The readiness trigger is PAGASA and 510 forecasts of a category 4 or stronger cyclone. The activation trigger, within 72 hours of landfall, is the IFRC/Netherlands Red Cross 510 probabilistic typhoon impact prediction model's predicted number of totally damaged houses. The framework says that model partially uses a machine learning model relating wind speed to housing damage. 510's 2024 NHESS paper (vendor-authored) states the XGBoost model served in 2021 as the trigger model for the UN OCHA AA pilot. OCHA's After Action Review of 31 March 2022 records that Typhoon Rai (Odette) in December 2021 rapidly intensified and did not meet the activation criteria. There was a near activation with staff pre-deployed. The 2023 revision retained the 510 model. The framework endorsed on 19 October 2025 covers US$6 million CERF and about 955,507 people in Regions 5, 8 and 13. OCHA and its Centre for Humanitarian Data compute predicted totally damaged buildings with the 510 model every 6–12 hours. It adds an observational trigger for super typhoons of 185 km/h or more reported by PAGASA. OCHA's June 2025 report says the readiness trigger was met for Super Typhoon Pepito (Man-yi) in its November 2024 to April 2025 reporting period. Activation thresholds were not reached and CERF AA resources were reallocated to the response. On 6 November 2025 CERF AA was activated for Super Typhoon Fung-Wong (Uwan) after the observational wind-speed threshold was triggered. OCHA reports nearly US$6 million released and WFP alone reaching 42,100 families. No cited document records an activation produced by the 510 model's output.", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No activation attributable to the model's output is documented, so no individual-level outcome can be assigned to the AI." }, { "group": "organizational", "reason": "OCHA adopted the model as its trigger and computes estimates each cycle. The only documented activation (6 November 2025) was triggered by the observational wind-speed threshold." }, { "group": "societal", "reason": "Pre-landfall assistance in November 2025 (WFP reports 42,100 families) is documented, but not attributed to the AI model by any cited source." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Forecast the number of totally damaged houses from typhoon track, wind and rainfall forecasts so that CERF-funded early actions can be triggered before landfall. The benefit claimed (AI-triggered anticipatory payouts) is that purpose, but no activation on the model's output is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Philippines (Regions 5, 8 and 13)", "developer_country": "Netherlands", "deployer_country": "Philippines" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Forecast the number of totally damaged houses from typhoon track, wind and rainfall forecasts so that CERF-funded early actions can be triggered before landfall. The benefit claimed (AI-triggered anticipatory payouts) is that purpose, but no activation on the model's output is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2021-09-15", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://www.unocha.org/philippines", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A pre-arranged CERF allocation with agency action plans ready before the typhoon season", "ECMWF ensemble track, wind and rainfall forecasts fed to the 510 impact model every 6–12 hours", "Historical house-damage data linking hazard, vulnerability and exposure to train the model", "A threshold matrix of predicted totally damaged houses per region with a 1-in-3.5-year return period (2025)" ], "must_hold": [ "Forecast lead time of at least 72 hours; rapid intensification (Rai 2021) shortened lead time below the trigger window", "Model impact estimates reaching the regional threshold matrix, which no read activation record documents", "Agency delivery capacity (financial service providers, data-sharing agreements) in the 72 hours before landfall", "PAGASA and ECMWF data availability" ], "echoes": [ { "setting": "Typhoon Rai (Odette), December 2021: forecasts monitored, activation criteria not met, staff pre-deployed (near activation)", "model_version": "2021 probabilistic version", "outcome": "did not recur", "source": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons" }, { "setting": "Super Typhoon Pepito (Man-yi), within OCHA's November 2024 to April 2025 reporting period: readiness trigger met, activation thresholds not reached, CERF AA resources reallocated to emergency response", "outcome": "did not recur", "source": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025" }, { "date": "2025-11-06", "setting": "Super Typhoon Fung-Wong (Uwan): CERF AA activated on the observational 185 km/h threshold; nearly US$6 million released; WFP reports 42,100 families reached; AI model's role not stated", "outcome": "unknown", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A pre-arranged CERF allocation with agency action plans ready before the typhoon season", "ECMWF ensemble track, wind and rainfall forecasts fed to the 510 impact model every 6–12 hours", "Historical house-damage data linking hazard, vulnerability and exposure to train the model", "A threshold matrix of predicted totally damaged houses per region with a 1-in-3.5-year return period (2025)" ], "must_hold": [ "Forecast lead time of at least 72 hours; rapid intensification (Rai 2021) shortened lead time below the trigger window", "Model impact estimates reaching the regional threshold matrix, which no read activation record documents", "Agency delivery capacity (financial service providers, data-sharing agreements) in the 72 hours before landfall", "PAGASA and ECMWF data availability" ], "echoes": [ { "setting": "Typhoon Rai (Odette), December 2021: forecasts monitored, activation criteria not met, staff pre-deployed (near activation)", "model_version": "2021 probabilistic version", "outcome": "did not recur", "source": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons" }, { "setting": "Super Typhoon Pepito (Man-yi), within OCHA's November 2024 to April 2025 reporting period: readiness trigger met, activation thresholds not reached, CERF AA resources reallocated to emergency response", "outcome": "did not recur", "source": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025" }, { "date": "2025-11-06", "setting": "Super Typhoon Fung-Wong (Uwan): CERF AA activated on the observational 185 km/h threshold; nearly US$6 million released; WFP reports 42,100 families reached; AI model's role not stated", "outcome": "unknown", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://www.unocha.org/philippines", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Humanitarian anticipatory action / typhoon impact forecasting", "segment": "UN-coordinated anticipatory financing (CERF)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022. ReliefWeb, posted 15 September 2021 (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-2021-2022", "date": "2021-09-15", "written_by": "deployer", "quote": "The Model includes a trigger mechanism based on the ECMWF forecast and IFRC/ Netherlands Red Cross 510 probabilistic typhoon impact prediction model developed for the Philippines" }, { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022, model checklist and model selection sections (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022 (model selection)", "url": "https://reliefweb.int/attachments/78d0268e-b476-3a55-ab82-94e7478fc408/Anticipatory%20Action%20Framework%20-%20Philippines%20-%20Typhoons%20-%202021-2022.pdf", "date": "2021-09-15", "written_by": "deployer", "quote": "Partially: Uses a machine learning model to determine relationship between wind speed and housing damage." }, { "kind": "report", "citation": "OCHA Philippines (2022). Report on the After Action Review for CERF Anticipatory Action, 31 March 2022. ReliefWeb (PDF dated 22 April 2022).", "title": "After Action Review on the CERF Anticipatory Action for Typhoons", "url": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons", "date": "2022-03-31", "written_by": "deployer", "quote": "While the typhoon at the end did not meet the AA pilot activation criteria, the CERF AA Core Group was closely monitoring the situation" }, { "kind": "report", "citation": "OCHA (2023). Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision. unocha.org, 3 October 2023 (PDF).", "title": "Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision", "url": "https://www.unocha.org/publications/report/philippines/philippines-cerf-aa-anticipatory-action-framework-2023-revision", "date": "2023-10-03", "written_by": "deployer", "quote": "Tropical Cyclone with potential to reach category level 3 or higher" }, { "kind": "report", "citation": "OCHA (2025). Anticipatory Action Framework Philippines - Tropical Cyclones (as of 03 October 2025, endorsed 19 October 2025). ReliefWeb, posted 4 November 2025 (PDF).", "title": "Anticipatory Action Framework Philippines - Tropical Cyclones (As of 03 October 2025)", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-tropical-cyclones-03-october-2025", "date": "2025-10-19", "written_by": "deployer", "quote": "This will be used to compare against 510 model outputs at 72 hours before landfall, in order to account for varying regional vulnerabilities." }, { "kind": "report", "citation": "OCHA (2025). Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1 (as of 07 November, 9 p.m. local time). ReliefWeb.", "title": "Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "date": "2025-11-07", "written_by": "deployer", "quote": "CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered" }, { "kind": "press release", "citation": "Paulose, H. (2026). How anticipatory action shaped the response to the Philippines' Twin Typhoons. OCHA Financing and Outreach Division, un.org, January 2026.", "title": "How anticipatory action shaped the response to the Philippines' Twin Typhoons", "url": "https://www.un.org/en/delegate/how-anticipatory-action-shaped-response-philippines%E2%80%99-twin-typhoons", "date": "2026-01-19", "written_by": "deployer", "quote": "WFP alone reached 42,100 families through its anticipatory financing mechanism" }, { "kind": "report", "citation": "OCHA (2025). Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report (November 2024 - April 2025). unocha.org, 27 June 2025.", "title": "Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report", "url": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025", "date": "2025-06-27", "written_by": "deployer", "quote": "CERF AA resources were reallocated to support the emergency response following Super Typhoon Pepito (Man-yi)" }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309-329, 1 February 2024.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "in 2021 as the trigger model for the UN OCHA AA pilot" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0354-source-0001", "AIBD-0354-source-0002", "AIBD-0354-source-0003", "AIBD-0354-source-0004", "AIBD-0354-source-0005", "AIBD-0354-source-0006", "AIBD-0354-source-0007", "AIBD-0354-source-0008", "AIBD-0354-source-0009" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022. ReliefWeb, posted 15 September 2021 (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-2021-2022", "date": "2021-09-15", "written_by": "deployer", "quote": "The Model includes a trigger mechanism based on the ECMWF forecast and IFRC/ Netherlands Red Cross 510 probabilistic typhoon impact prediction model developed for the Philippines" }, { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022, model checklist and model selection sections (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022 (model selection)", "url": "https://reliefweb.int/attachments/78d0268e-b476-3a55-ab82-94e7478fc408/Anticipatory%20Action%20Framework%20-%20Philippines%20-%20Typhoons%20-%202021-2022.pdf", "date": "2021-09-15", "written_by": "deployer", "quote": "Partially: Uses a machine learning model to determine relationship between wind speed and housing damage." }, { "kind": "report", "citation": "OCHA Philippines (2022). Report on the After Action Review for CERF Anticipatory Action, 31 March 2022. ReliefWeb (PDF dated 22 April 2022).", "title": "After Action Review on the CERF Anticipatory Action for Typhoons", "url": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons", "date": "2022-03-31", "written_by": "deployer", "quote": "While the typhoon at the end did not meet the AA pilot activation criteria, the CERF AA Core Group was closely monitoring the situation" }, { "kind": "report", "citation": "OCHA (2023). Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision. unocha.org, 3 October 2023 (PDF).", "title": "Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision", "url": "https://www.unocha.org/publications/report/philippines/philippines-cerf-aa-anticipatory-action-framework-2023-revision", "date": "2023-10-03", "written_by": "deployer", "quote": "Tropical Cyclone with potential to reach category level 3 or higher" }, { "kind": "report", "citation": "OCHA (2025). Anticipatory Action Framework Philippines - Tropical Cyclones (as of 03 October 2025, endorsed 19 October 2025). ReliefWeb, posted 4 November 2025 (PDF).", "title": "Anticipatory Action Framework Philippines - Tropical Cyclones (As of 03 October 2025)", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-tropical-cyclones-03-october-2025", "date": "2025-10-19", "written_by": "deployer", "quote": "This will be used to compare against 510 model outputs at 72 hours before landfall, in order to account for varying regional vulnerabilities." }, { "kind": "report", "citation": "OCHA (2025). Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1 (as of 07 November, 9 p.m. local time). ReliefWeb.", "title": "Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "date": "2025-11-07", "written_by": "deployer", "quote": "CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered" }, { "kind": "press release", "citation": "Paulose, H. (2026). How anticipatory action shaped the response to the Philippines' Twin Typhoons. OCHA Financing and Outreach Division, un.org, January 2026.", "title": "How anticipatory action shaped the response to the Philippines' Twin Typhoons", "url": "https://www.un.org/en/delegate/how-anticipatory-action-shaped-response-philippines%E2%80%99-twin-typhoons", "date": "2026-01-19", "written_by": "deployer", "quote": "WFP alone reached 42,100 families through its anticipatory financing mechanism" }, { "kind": "report", "citation": "OCHA (2025). Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report (November 2024 - April 2025). unocha.org, 27 June 2025.", "title": "Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report", "url": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025", "date": "2025-06-27", "written_by": "deployer", "quote": "CERF AA resources were reallocated to support the emergency response following Super Typhoon Pepito (Man-yi)" }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309-329, 1 February 2024.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "in 2021 as the trigger model for the UN OCHA AA pilot" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "OCHA frameworks (2021, 2023, 2025) use 510's XGBoost house-damage model as activation trigger; near-activation Dec 2021 (Rai); the Nov 2025 activation for Uwan ran on the 185 km/h wind trigger.", "account": "OCHA's Anticipatory Action (AA) Framework for Philippine typhoons was posted on ReliefWeb on 15 September 2021. It set up a US$7.5 million CERF pilot for about 270,500 people in Regions 5 and 8 with FAO, IOM, UNFPA, UNICEF and WFP. It has a two-stage trigger. The readiness trigger is PAGASA and 510 forecasts of a category 4 or stronger cyclone. The activation trigger, within 72 hours of landfall, is the IFRC/Netherlands Red Cross 510 probabilistic typhoon impact prediction model's predicted number of totally damaged houses. The framework says that model partially uses a machine learning model relating wind speed to housing damage. 510's 2024 NHESS paper (vendor-authored) states the XGBoost model served in 2021 as the trigger model for the UN OCHA AA pilot. OCHA's After Action Review of 31 March 2022 records that Typhoon Rai (Odette) in December 2021 rapidly intensified and did not meet the activation criteria. There was a near activation with staff pre-deployed. The 2023 revision retained the 510 model. The framework endorsed on 19 October 2025 covers US$6 million CERF and about 955,507 people in Regions 5, 8 and 13. OCHA and its Centre for Humanitarian Data compute predicted totally damaged buildings with the 510 model every 6–12 hours. It adds an observational trigger for super typhoons of 185 km/h or more reported by PAGASA. OCHA's June 2025 report says the readiness trigger was met for Super Typhoon Pepito (Man-yi) in its November 2024 to April 2025 reporting period. Activation thresholds were not reached and CERF AA resources were reallocated to the response. On 6 November 2025 CERF AA was activated for Super Typhoon Fung-Wong (Uwan) after the observational wind-speed threshold was triggered. OCHA reports nearly US$6 million released and WFP alone reaching 42,100 families. No cited document records an activation produced by the 510 model's output." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The model estimated how many houses a typhoon might destroy." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The 2021 revision replaced deterministic forecasts with probabilities derived from ECMWF ensemble tracks." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "value_present", "value": { "kind": "text", "text": "The model converted typhoon forecasts and data on exposed housing into estimates of houses likely to be destroyed." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "system", "ai_role", "replication", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "XGBoost within the 510 typhoon-impact model." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "probabilistic version using ECMWF ensemble tracks (revamped from deterministic, 2021)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "510 probabilistic typhoon impact model (XGBoost) predicting totally damaged houses, used as the CERF anticipatory action activation trigger" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "510, an initiative of the Netherlands Red Cross, with IFRC" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UN OCHA's CERF anticipatory action framework for Philippine typhoons adopted 510's machine-learning impact model as trigger in 2021; no activation on that trigger documented to 2025", "headline": "OCHA uses 510's model as CERF trigger; no activation documented" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "fb2e5c80103070fb65e02114473a292ff7832a3193b97eaae26cfce2e51f4adb" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "OCHA's Anticipatory Action (AA) Framework for Philippine typhoons was posted on ReliefWeb on 15 September 2021. It set up a US$7.5 million CERF pilot for about 270,500 people in Regions 5 and 8 with FAO, IOM, UNFPA, UNICEF and WFP. It has a two-stage trigger. The readiness trigger is PAGASA and 510 forecasts of a category 4 or stronger cyclone. The activation trigger, within 72 hours of landfall, is the IFRC/Netherlands Red Cross 510 probabilistic typhoon impact prediction model's predicted number of totally damaged houses. The framework says that model partially uses a machine learning model relating wind speed to housing damage. 510's 2024 NHESS paper (vendor-authored) states the XGBoost model served in 2021 as the trigger model for the UN OCHA AA pilot. OCHA's After Action Review of 31 March 2022 records that Typhoon Rai (Odette) in December 2021 rapidly intensified and did not meet the activation criteria. There was a near activation with staff pre-deployed. The 2023 revision retained the 510 model. The framework endorsed on 19 October 2025 covers US$6 million CERF and about 955,507 people in Regions 5, 8 and 13. OCHA and its Centre for Humanitarian Data compute predicted totally damaged buildings with the 510 model every 6–12 hours. It adds an observational trigger for super typhoons of 185 km/h or more reported by PAGASA. OCHA's June 2025 report says the readiness trigger was met for Super Typhoon Pepito (Man-yi) in its November 2024 to April 2025 reporting period. Activation thresholds were not reached and CERF AA resources were reallocated to the response. On 6 November 2025 CERF AA was activated for Super Typhoon Fung-Wong (Uwan) after the observational wind-speed threshold was triggered. OCHA reports nearly US$6 million released and WFP alone reaching 42,100 families. No cited document records an activation produced by the 510 model's output.", "replication": { "produced_by": [ "A pre-arranged CERF allocation with agency action plans ready before the typhoon season", "ECMWF ensemble track, wind and rainfall forecasts fed to the 510 impact model every 6–12 hours", "Historical house-damage data linking hazard, vulnerability and exposure to train the model", "A threshold matrix of predicted totally damaged houses per region with a 1-in-3.5-year return period (2025)" ], "must_hold": [ "Forecast lead time of at least 72 hours; rapid intensification (Rai 2021) shortened lead time below the trigger window", "Model impact estimates reaching the regional threshold matrix, which no read activation record documents", "Agency delivery capacity (financial service providers, data-sharing agreements) in the 72 hours before landfall", "PAGASA and ECMWF data availability" ], "echoes": [ { "setting": "Typhoon Rai (Odette), December 2021: forecasts monitored, activation criteria not met, staff pre-deployed (near activation)", "model_version": "2021 probabilistic version", "outcome": "did not recur", "source": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons" }, { "setting": "Super Typhoon Pepito (Man-yi), within OCHA's November 2024 to April 2025 reporting period: readiness trigger met, activation thresholds not reached, CERF AA resources reallocated to emergency response", "outcome": "did not recur", "source": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025" }, { "date": "2025-11-06", "setting": "Super Typhoon Fung-Wong (Uwan): CERF AA activated on the observational 185 km/h threshold; nearly US$6 million released; WFP reports 42,100 families reached; AI model's role not stated", "outcome": "unknown", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 33, "reviewed": 0, "record_mapped": 33, "not_assessed": 58, "complete": false, "by_state": { "value_present": 33, "not_reported": 0, "unresolved": 0, "not_assessed": 58, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0347", "case_url": "records/AIBD-0347.html", "reason": "The developer is headquartered in United States; the case names Indonesia.", "details": [ { "label": "Developer headquarters", "value": "United States" }, { "label": "Case jurisdictions", "value": "Indonesia" }, { "label": "Case location", "value": "Indonesian exclusive economic zone, Indonesia" } ], "filters": { "origin": "United States", "destination": [ "Indonesia" ] }, "record": { "id": "AIBD-0347", "title": "Indonesia captures the blacklisted MV NIKA on 12 July 2019 after INTERPOL tracking supported by OceanMind and Global Fishing Watch; the tracking was by analysts, not an AI detection", "summary": "MV NIKA seized by Indonesia 12 July 2019 after INTERPOL tracking with OceanMind and GFW; captain convicted, owner fined, vessel forfeited; no cited source documents an AI detection.", "date_observed": "2019-07-12", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "INTERPOL reported on 22 July 2019 that the MV NIKA had been detained by Indonesian authorities. The vessel was suspected of illegal fishing in the Southern Ocean. Its owner also owned the blacklisted STS 50 intercepted in Indonesia in 2018. INTERPOL tracked the vessel's movement with support from the UK not-for-profit OceanMind, sending daily reports to Indonesia, New Zealand, Panama, the UK and the US. Hourly updates aided by Global Fishing Watch (GFW) followed as it approached Indonesian waters. GFW's account (23 July 2019) says GFW analyst Imam Prakoso helped track the vessel from February 2019 after it illegally switched off its tracking system. It says a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour exchanged information. GFW analysts helped predict its course and identify rendezvous points. Indonesian patrol boats ORCA 2 and ORCA 3 intercepted it on 12 July 2019 at 08:06 local time. The court outcome comes from a UK Government Blue Belt blog of 5 June 2021. That blog was no longer available as of 20 October 2026. The Indonesian Supreme Court gave its verdict on 27 February 2020, the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited. None of the sources describes a machine-learning or AI detection. OceanMind's homepage says only, in general terms, that it uses satellites, AI and human expertise.", "category": "protective response", "unexpected": false, "purpose": "Vessel tracking and analysis to support fisheries enforcement against illegal, unreported and unregulated fishing. The capture was the intended use, but the sources attribute it to analysts and inter-agency coordination rather than to an AI output.", "system": "Global Fishing Watch vessel-tracking platform and analysts; OceanMind analysis for INTERPOL (no AI component documented for this case)", "model_version": null, "developer": "Global Fishing Watch; OceanMind", "deployer": "INTERPOL (Global Fisheries Enforcement program); Indonesian naval and fisheries authorities (patrol boats ORCA 2 and ORCA 3)", "location": "Indonesian exclusive economic zone, Indonesia", "domain": "Fisheries enforcement / illegal, unreported and unregulated fishing", "segment": "International police cooperation and national maritime enforcement", "benefit_type": "Reduced Violence", "taxonomy_note": "The crime is illegal fishing, the AI role is undocumented, and the good is a seizure and conviction rather than less violence.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified in any cited source." }, { "group": "organizational", "depth": null, "reason": "Indonesian authorities and INTERPOL obtained a capture, a conviction and a forfeiture. No cited source attributes any part of the result to an AI output." }, { "group": "societal", "depth": null, "reason": "One blacklisted vessel removed from Southern Ocean fishing. The contribution of any AI system is undocumented and the deterrent effect is not quantified." } ], "ai_role": { "status": "unassessed", "statement": "No cited source documents an AI or machine-learning role. INTERPOL's release says it tracked the vessel with support from OceanMind and that hourly updates were aided by contributions from Global Fishing Watch. GFW's own article credits named analysts who tracked AIS data, predicted the course and identified rendezvous points. It does not mention algorithms, machine learning or automated detection. OceanMind's homepage states only that it uses satellites, AI and human expertise in general. The AI role is thus undocumented rather than established. The account comes from INTERPOL, GFW, and the Blue Belt blog. The court outcome (Supreme Court verdict of 27 February 2020, conviction, Rp300,000,000 fine, forfeiture) comes from the Blue Belt blog. The Blue Belt post of 5 June 2021 is no longer available, and no archived copy is cited." }, "evidence": [ { "kind": "press release", "citation": "INTERPOL (2019). INTERPOL supports apprehension of vessel suspected of illegal fishing. News release, 22 July 2019.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "INTERPOL tracked the movement of the MV NIKA with support from OceanMind, sending daily reports to the relevant countries including Indonesia, New Zealand, Panama, the UK and the US." }, { "kind": "press release", "citation": "INTERPOL (2019). Same release, paragraph on the approach to Indonesian waters.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing (Global Fishing Watch paragraph)", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "As it approached Indonesian waters, additional updates were sent to the national authorities hourly, aided by contributions from Global Fishing Watch." }, { "kind": "press release", "citation": "Global Fishing Watch (2019). The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it. globalfishingwatch.org, 23 July 2019.", "title": "The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "GFW analysts helped predict the MV NIKA's course and identify rendezvous points where the patrol vessels could intercept" }, { "kind": "press release", "citation": "Global Fishing Watch (2019). Same article, paragraph on the live coordination.", "title": "The capture of the MV NIKA (coordination paragraph)", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour, swapped information on its every move" }, { "kind": "press release", "citation": "OceanMind (n.d.). Homepage, general description of methods (no mention of NIKA).", "title": "OceanMind homepage", "url": "https://oceanmind.global/", "date": null, "written_by": "makers", "quote": "For more than a decade, we have used satellites, AI, and human expertise to provide crucial insights that enable others to protect the ocean for people and planet." } ], "replication": { "produced_by": [ "A blacklisted vessel whose identity and ownership were already known to INTERPOL and CCAMLR", "Continuous tracking by OceanMind and GFW analysts with daily, then hourly, reports to coastal states", "Indonesian patrol vessels positioned at predicted rendezvous points", "Indonesian prosecution leading to a Supreme Court verdict" ], "must_hold": [ "A coastal state willing and able to intercept and prosecute", "Analyst capacity to follow a vessel that has switched off its tracking system", "Information sharing between INTERPOL, NGOs and national agencies in near real time", "Documentation of what, if anything, an automated system contributed, which is absent here" ], "echoes": [ { "date": null, "setting": "STS 50, a blacklisted vessel of the same owner, intercepted in Indonesia in 2018 (INTERPOL release); AI role equally undocumented", "model_version": null, "outcome": "unknown", "source": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing" } ] }, "paired_incident": null, "headline": "GFW analysts help Indonesia seize MV NIKA; no AI detection", "metric": "the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The reported verdict of 27 February 2020, conviction, fine, and forfeiture rest on a UK Government Blue Belt blog that is no longer available. They lack independent confirmation in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "France", "unrated_because": "the seizure is documented, the AI's part is not", "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the seizure is documented, the AI's part is not.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Global_Fishing_Watch; https://www.interpol.int/en/Who-we-are/What-is-INTERPOL", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to INTERPOL.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: two press releases from the makers and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0347", "catalog_version": "1.0.0", "source_fingerprint": "c8d0aa4fd21ddc071c29e698a7b48458113030802ec4971dabf6dec4f5108888", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0347-source-0001", "AIBD-0347-source-0002", "AIBD-0347-source-0003", "AIBD-0347-source-0004", "AIBD-0347-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Global Fishing Watch; OceanMind", "deployer": "INTERPOL (Global Fisheries Enforcement program); Indonesian naval and fisheries authorities (patrol boats ORCA 2 and ORCA 3)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "display": { "answer": "INTERPOL received vessel-tracking assistance from OceanMind and Global Fishing Watch. The cited accounts describe analysts’ work but do not identify an AI component used in this operation.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "unassessed", "statement": "No cited source documents an AI or machine-learning role. INTERPOL's release says it tracked the vessel with support from OceanMind and that hourly updates were aided by contributions from Global Fishing Watch. GFW's own article credits named analysts who tracked AIS data, predicted the course and identified rendezvous points. It does not mention algorithms, machine learning or automated detection. OceanMind's homepage states only that it uses satellites, AI and human expertise in general. The AI role is thus undocumented rather than established. The account comes from INTERPOL, GFW, and the Blue Belt blog. The court outcome (Supreme Court verdict of 27 February 2020, conviction, Rp300,000,000 fine, forfeiture) comes from the Blue Belt blog. The Blue Belt post of 5 June 2021 is no longer available, and no archived copy is cited." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: two press releases from the makers and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified in any cited source." }, { "group": "organizational", "reason": "Indonesian authorities and INTERPOL obtained a capture, a conviction and a forfeiture. No cited source attributes any part of the result to an AI output." }, { "group": "societal", "reason": "One blacklisted vessel removed from Southern Ocean fishing. The contribution of any AI system is undocumented and the deterrent effect is not quantified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified in any cited source." }, { "group": "organizational", "reason": "Indonesian authorities and INTERPOL obtained a capture, a conviction and a forfeiture. No cited source attributes any part of the result to an AI output." }, { "group": "societal", "reason": "One blacklisted vessel removed from Southern Ocean fishing. The contribution of any AI system is undocumented and the deterrent effect is not quantified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "OceanMind’s general description of its use of AI does not establish that it used AI here. The Blue Belt account of the court outcome is no longer available, and the entry has no archived copy.", "editor_note": "" } }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "The reported verdict of 27 February 2020, conviction, fine, and forfeiture rest on a UK Government Blue Belt blog that is no longer available. They lack independent confirmation in the cited sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Analysts tracked the vessel’s signals, predicted its course, and identified possible rendezvous points." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0347" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence", "taxonomy_note": "The crime is illegal fishing, the AI role is undocumented, and the good is a seizure and conviction rather than less violence." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "INTERPOL reported on 22 July 2019 that the MV NIKA had been detained by Indonesian authorities. The vessel was suspected of illegal fishing in the Southern Ocean. Its owner also owned the blacklisted STS 50 intercepted in Indonesia in 2018. INTERPOL tracked the vessel's movement with support from the UK not-for-profit OceanMind, sending daily reports to Indonesia, New Zealand, Panama, the UK and the US. Hourly updates aided by Global Fishing Watch (GFW) followed as it approached Indonesian waters. GFW's account (23 July 2019) says GFW analyst Imam Prakoso helped track the vessel from February 2019 after it illegally switched off its tracking system. It says a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour exchanged information. GFW analysts helped predict its course and identify rendezvous points. Indonesian patrol boats ORCA 2 and ORCA 3 intercepted it on 12 July 2019 at 08:06 local time. The court outcome comes from a UK Government Blue Belt blog of 5 June 2021. That blog was no longer available as of 20 October 2026. The Indonesian Supreme Court gave its verdict on 27 February 2020, the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited. None of the sources describes a machine-learning or AI detection. OceanMind's homepage says only, in general terms, that it uses satellites, AI and human expertise.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified in any cited source." }, { "group": "organizational", "reason": "Indonesian authorities and INTERPOL obtained a capture, a conviction and a forfeiture. No cited source attributes any part of the result to an AI output." }, { "group": "societal", "reason": "One blacklisted vessel removed from Southern Ocean fishing. The contribution of any AI system is undocumented and the deterrent effect is not quantified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Vessel tracking and analysis to support fisheries enforcement against illegal, unreported and unregulated fishing. The capture was the intended use, but the sources attribute it to analysts and inter-agency coordination rather than to an AI output." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Indonesian exclusive economic zone, Indonesia", "developer_country": "United States", "deployer_country": "France" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Vessel tracking and analysis to support fisheries enforcement against illegal, unreported and unregulated fishing. The capture was the intended use, but the sources attribute it to analysts and inter-agency coordination rather than to an AI output." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2019-07-12", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the seizure is documented, the AI's part is not.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Global_Fishing_Watch; https://www.interpol.int/en/Who-we-are/What-is-INTERPOL", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to INTERPOL.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A blacklisted vessel whose identity and ownership were already known to INTERPOL and CCAMLR", "Continuous tracking by OceanMind and GFW analysts with daily, then hourly, reports to coastal states", "Indonesian patrol vessels positioned at predicted rendezvous points", "Indonesian prosecution leading to a Supreme Court verdict" ], "must_hold": [ "A coastal state willing and able to intercept and prosecute", "Analyst capacity to follow a vessel that has switched off its tracking system", "Information sharing between INTERPOL, NGOs and national agencies in near real time", "Documentation of what, if anything, an automated system contributed, which is absent here" ], "echoes": [ { "setting": "STS 50, a blacklisted vessel of the same owner, intercepted in Indonesia in 2018 (INTERPOL release); AI role equally undocumented", "outcome": "unknown", "source": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A blacklisted vessel whose identity and ownership were already known to INTERPOL and CCAMLR", "Continuous tracking by OceanMind and GFW analysts with daily, then hourly, reports to coastal states", "Indonesian patrol vessels positioned at predicted rendezvous points", "Indonesian prosecution leading to a Supreme Court verdict" ], "must_hold": [ "A coastal state willing and able to intercept and prosecute", "Analyst capacity to follow a vessel that has switched off its tracking system", "Information sharing between INTERPOL, NGOs and national agencies in near real time", "Documentation of what, if anything, an automated system contributed, which is absent here" ], "echoes": [ { "setting": "STS 50, a blacklisted vessel of the same owner, intercepted in Indonesia in 2018 (INTERPOL release); AI role equally undocumented", "outcome": "unknown", "source": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the seizure is documented, the AI's part is not.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Global_Fishing_Watch; https://www.interpol.int/en/Who-we-are/What-is-INTERPOL", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to INTERPOL.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Fisheries enforcement / illegal, unreported and unregulated fishing", "segment": "International police cooperation and national maritime enforcement" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "INTERPOL (2019). INTERPOL supports apprehension of vessel suspected of illegal fishing. News release, 22 July 2019.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "INTERPOL tracked the movement of the MV NIKA with support from OceanMind, sending daily reports to the relevant countries including Indonesia, New Zealand, Panama, the UK and the US." }, { "kind": "press release", "citation": "INTERPOL (2019). Same release, paragraph on the approach to Indonesian waters.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing (Global Fishing Watch paragraph)", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "As it approached Indonesian waters, additional updates were sent to the national authorities hourly, aided by contributions from Global Fishing Watch." }, { "kind": "press release", "citation": "Global Fishing Watch (2019). The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it. globalfishingwatch.org, 23 July 2019.", "title": "The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "GFW analysts helped predict the MV NIKA's course and identify rendezvous points where the patrol vessels could intercept" }, { "kind": "press release", "citation": "Global Fishing Watch (2019). Same article, paragraph on the live coordination.", "title": "The capture of the MV NIKA (coordination paragraph)", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour, swapped information on its every move" }, { "kind": "press release", "citation": "OceanMind (n.d.). Homepage, general description of methods (no mention of NIKA).", "title": "OceanMind homepage", "url": "https://oceanmind.global/", "written_by": "makers", "quote": "For more than a decade, we have used satellites, AI, and human expertise to provide crucial insights that enable others to protect the ocean for people and planet." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0347-source-0001", "AIBD-0347-source-0002", "AIBD-0347-source-0003", "AIBD-0347-source-0004", "AIBD-0347-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "INTERPOL (2019). INTERPOL supports apprehension of vessel suspected of illegal fishing. News release, 22 July 2019.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "INTERPOL tracked the movement of the MV NIKA with support from OceanMind, sending daily reports to the relevant countries including Indonesia, New Zealand, Panama, the UK and the US." }, { "kind": "press release", "citation": "INTERPOL (2019). Same release, paragraph on the approach to Indonesian waters.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing (Global Fishing Watch paragraph)", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "As it approached Indonesian waters, additional updates were sent to the national authorities hourly, aided by contributions from Global Fishing Watch." }, { "kind": "press release", "citation": "Global Fishing Watch (2019). The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it. globalfishingwatch.org, 23 July 2019.", "title": "The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "GFW analysts helped predict the MV NIKA's course and identify rendezvous points where the patrol vessels could intercept" }, { "kind": "press release", "citation": "Global Fishing Watch (2019). Same article, paragraph on the live coordination.", "title": "The capture of the MV NIKA (coordination paragraph)", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour, swapped information on its every move" }, { "kind": "press release", "citation": "OceanMind (n.d.). Homepage, general description of methods (no mention of NIKA).", "title": "OceanMind homepage", "url": "https://oceanmind.global/", "written_by": "makers", "quote": "For more than a decade, we have used satellites, AI, and human expertise to provide crucial insights that enable others to protect the ocean for people and planet." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "MV NIKA seized by Indonesia 12 July 2019 after INTERPOL tracking with OceanMind and GFW; captain convicted, owner fined, vessel forfeited; no cited source documents an AI detection.", "account": "INTERPOL reported on 22 July 2019 that the MV NIKA had been detained by Indonesian authorities. The vessel was suspected of illegal fishing in the Southern Ocean. Its owner also owned the blacklisted STS 50 intercepted in Indonesia in 2018. INTERPOL tracked the vessel's movement with support from the UK not-for-profit OceanMind, sending daily reports to Indonesia, New Zealand, Panama, the UK and the US. Hourly updates aided by Global Fishing Watch (GFW) followed as it approached Indonesian waters. GFW's account (23 July 2019) says GFW analyst Imam Prakoso helped track the vessel from February 2019 after it illegally switched off its tracking system. It says a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour exchanged information. GFW analysts helped predict its course and identify rendezvous points. Indonesian patrol boats ORCA 2 and ORCA 3 intercepted it on 12 July 2019 at 08:06 local time. The court outcome comes from a UK Government Blue Belt blog of 5 June 2021. That blog was no longer available as of 20 October 2026. The Indonesian Supreme Court gave its verdict on 27 February 2020, the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited. None of the sources describes a machine-learning or AI detection. OceanMind's homepage says only, in general terms, that it uses satellites, AI and human expertise." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Global Fishing Watch vessel-tracking platform and analysts; OceanMind analysis for INTERPOL (no AI component documented for this case)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Global Fishing Watch; OceanMind" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Indonesia captures the blacklisted MV NIKA on 12 July 2019 after INTERPOL tracking supported by OceanMind and Global Fishing Watch; the tracking was by analysts, not an AI detection", "headline": "GFW analysts help Indonesia seize MV NIKA; no AI detection" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c8d0aa4fd21ddc071c29e698a7b48458113030802ec4971dabf6dec4f5108888" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "INTERPOL reported on 22 July 2019 that the MV NIKA had been detained by Indonesian authorities. The vessel was suspected of illegal fishing in the Southern Ocean. Its owner also owned the blacklisted STS 50 intercepted in Indonesia in 2018. INTERPOL tracked the vessel's movement with support from the UK not-for-profit OceanMind, sending daily reports to Indonesia, New Zealand, Panama, the UK and the US. Hourly updates aided by Global Fishing Watch (GFW) followed as it approached Indonesian waters. GFW's account (23 July 2019) says GFW analyst Imam Prakoso helped track the vessel from February 2019 after it illegally switched off its tracking system. It says a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour exchanged information. GFW analysts helped predict its course and identify rendezvous points. Indonesian patrol boats ORCA 2 and ORCA 3 intercepted it on 12 July 2019 at 08:06 local time. The court outcome comes from a UK Government Blue Belt blog of 5 June 2021. That blog was no longer available as of 20 October 2026. The Indonesian Supreme Court gave its verdict on 27 February 2020, the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited. None of the sources describes a machine-learning or AI detection. OceanMind's homepage says only, in general terms, that it uses satellites, AI and human expertise.", "replication": { "produced_by": [ "A blacklisted vessel whose identity and ownership were already known to INTERPOL and CCAMLR", "Continuous tracking by OceanMind and GFW analysts with daily, then hourly, reports to coastal states", "Indonesian patrol vessels positioned at predicted rendezvous points", "Indonesian prosecution leading to a Supreme Court verdict" ], "must_hold": [ "A coastal state willing and able to intercept and prosecute", "Analyst capacity to follow a vessel that has switched off its tracking system", "Information sharing between INTERPOL, NGOs and national agencies in near real time", "Documentation of what, if anything, an automated system contributed, which is absent here" ], "echoes": [ { "setting": "STS 50, a blacklisted vessel of the same owner, intercepted in Indonesia in 2018 (INTERPOL release); AI role equally undocumented", "outcome": "unknown", "source": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } } ] }