{
 "row": "mit-ai-incident-tracker",
 "address": "mit ai incident tracker",
 "addresses": [
  "mit ai incident tracker"
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 "extracted": "2026-10-29",
 "extractor": "Claude (Opus 5.5), subagent, one extractor (unaudited, §3.8)",
 "rule": "v0.9 §3.5, source-neutral: opening, defined terms, headline claims, stated limits; quotes verbatim from the saved page texts (whitespace-normalised for the check; multi-part quotes joined by ' … ', each part checked), the extracted meta descriptions (B1-meta.txt), or the card's title and snippet where the card's text is not in the page; 54/54 verified by script; third-party pages quoted only in short spans",
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 "lineage_note": "Lineages: what it is (B1, B6); source, the AIID (B1, B6); purpose (B1, B3); method (B1, B3, B5); dimensions (B1, B2, B3, B4); the national-security view (B1, B4); operator (B1, B2, B6); origin (B1, B6); count, four figures across MIT's surfaces (B1, B1's card, B5, B5's card); updates (B3, B4); validation (B3); limits (B1, B3, B5); openness (B1, B6); neighbours (B3); uptake (B3); the encyclopedia (W1, W3, W4).",
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 "field": [
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   "origin": "card",
   "role": "Overview card 1; also the own page the MMRS record cites (its only source)",
   "card": "MIT AI Risk Initiative — MIT AI Incident Tracker",
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   "names_operator": "yes ('MIT AI Risk Initiative' in the saved text or card)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "date": "2026-10-29 (undated; as fetched; the MMRS record read it 2026-10-28)",
   "note": "URL the MMRS record's source and the task's; the card's snippet ('classifies 1,752 real-world AI incident reports … using Arcola AI's LLM pipeline') is not in the saved page, whose heading gives 1,600 and whose meta description (B1-meta.txt, extracted verbatim from the name=description, og:description and twitter:description tags) gives 'more than 1,400'; the page carries unfinished template blocks ('[TO UPDATE / REMOVE] Frequently Asked Questions … Question 1 … Response 1', '[TO UPDATE / REMOVE] Acknowledgments'), recorded, not ledgered"
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   "card": "MIT AI Risk Initiative — MIT AI Risk Initiative",
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   "card_title": "MIT AI Risk Initiative",
   "card_snippet": "Tools and Data: Expert Survey (expert consensus on severe AI risks, vulnerabilities, and responsibilities), AI Risk Repository (living repository of AI risks), ...",
   "card_note": null,
   "names_operator": "yes ('MIT AI Risk Initiative' in the saved text or card)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "date": "2026-10-29 (undated; as fetched)",
   "note": "URL by title and site (the page's <title> is 'MIT AI Risk Initiative'); the card's snippet ('Tools and Data: Expert Survey (…), AI Risk Repository (living repository of AI risks), …') is a summary not found verbatim in the saved page, whose tools block reads 'Which risks are most important? … What are the risks from AI? … What real-world harms are occurring?'"
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   "note": "URL found by WebSearch on the title; dated June 30, 2026; the card's snippet is the Executive Summary's first sentence; a PDF download is linked, not fetched"
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   ],
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   "quote": "By the Landis & Koch interpretation scale, the human-human agreement is itself only \"Fair\" for Harm Severity (0.31) and \"Moderate\" for EU AI Act Risk Level (0.56).",
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   "quote": "Incident reporting has contributed to major safety improvements in high-stakes sectors such as aviation, healthcare, and nuclear power",
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   "quote": "Several databases already collect reports of AI-related incidents, including the AI Incident Database, the OECD’s AI Incident Monitor, the AIAAIC Repository, the Political Deepfakes Database, and others. The MIT AI Incident Tracker project builds on these efforts by classifying each incident against a set of risk taxonomies and rating the severity of the harm involved.",
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   "quote": "The Incident Tracker uses large language models (LLMs) to make incident classification scalable. … However, LLMs can make mistakes when interpreting incident reports and assigning classifications. For instance, they can occasionally hallucinate details and can exhibit a score-range bias, where ratings cluster within a narrow band of an ordinal scale rather than using the full range.",
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   "quote": "The human consensus labels used as the benchmark for evaluating LLM performance were based on a small review sample: two reviewers per incident across 10 incidents.",
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   "locus": "Next steps",
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   "claim": "its conclusion and plan: comparable accuracy, a fuller validation study next, and a reclassification of every AIID report",
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   "locus": "Major update - 23 June 2025",
   "quote": "This brings us completely up to date with the latest incidents on the AI Incident Database as at 23 June 2025 (up to incident ID #1116)",
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   "quote": "The harm severity analysis uses a new scale, simplifying scoring to 1-5 points (previously 0-10). … I have tried to remove ambiguities from the definitions in the scale and to make it possible to grade incidents consistently with greater objectivity.",
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   "locus": "National Security Impact Assessment",
   "quote": "Assesses NatSec impact of each incident in 5 categories: Physical Security & Critical Infrastructure / Information Warfare & Intelligence Security / Sovereignty & Government Functions / Economic & Technological Security / Societal Stability & Human Rights using this framework. … Classifies threat for Imminence, Novelty and Autonomy",
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   "quote": "AI risk is a large and fast-moving subject. Understanding it means keeping track of what can go wrong, who is affected, how organizations are responding, and where the gaps are. No single dataset captures all of this, so we maintain several connected workstreams, each answering a different question.",
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   "locus": "Team, Simon Mylius",
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   "locus": "Who uses our work, AI Incident Database",
   "quote": "AI Incident Database … Maps real-world incidents to our taxonomy in the incident tracker.",
   "claim": "the AIID listed as a user: it maps incidents to the Initiative's taxonomy in the tracker",
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   "origin": "wikipedia",
   "locus": "rendered page https://en.wikipedia.org/wiki/MIT_AI_Incident_Tracker (HTTP 404)",
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   "source": "W1",
   "origin": "wikipedia",
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   "quote": "There were no results matching the query.",
   "claim": "no article text in the encyclopedia contains the phrase 'AI Incident Tracker'",
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   "origin": "wikipedia",
   "locus": "Voiceverse NFT plagiarism scandal, Reactions and legacy ¶ (found by full-text search '\"AI Risk Repository\"', W2-search.txt: one result)",
   "quote": "The MIT AI Risk Repository catalogued the incident as an example of \"economic and cultural devaluation of human effort\"",
   "claim": "the encyclopedia's one mention: the repository (not the tracker) catalogued AIID incident 277 under 'economic and cultural devaluation of human effort', referenced to the AIID's citation page",
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   "source": "W4",
   "origin": "wikipedia",
   "locus": "incidentdatabase.ai/cite/277 (the reference Wikipedia gives), Applied Taxonomies",
   "quote": "GMF, MIT MIT Taxonomy Classifications Machine-Classified … 4.3. Fraud, scams, and targeted manipulation … Malicious Actors & Misuse",
   "claim": "on 2026-10-29 the AIID's page for incident 277 shows the MIT classification as machine-classified, subdomain 4.3 (fraud, scams, targeted manipulation), domain Malicious Actors & Misuse",
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 },
 "field_sources": {
  "B1": "MIT AI Risk Initiative — MIT AI Incident Tracker",
  "B2": "MIT AI Risk Initiative — MIT AI Risk Initiative",
  "B3": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative",
  "B4": "MIT AI Risk Initiative — Incident Tracker - June 2025 Update - MIT AI Risk Repository",
  "B5": "MIT AI Risk Navigator — Incidents - MIT AI Risk Navigator",
  "B6": "MIT AI Risk Initiative — About - MIT AI Risk Initiative",
  "W1": "Wikipedia — MIT AI Incident Tracker (no article)",
  "W3": "Wikipedia — Voiceverse NFT plagiarism scandal",
  "W4": "AI Incident Database — Incident 277 (Wikipedia's reference)"
 }
}