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The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/mit-ai-incident-tracker/", "research": { "google": "https://www.google.com/search?q=MIT+AI+Incident+Tracker", "google_non": "https://www.google.com/search?q=%22MIT+AI+Incident+Tracker+%E2%80%94+The+Negative+of+the+Negative%22", "chatgpt": "https://chatgpt.com/?q=What+is+MIT+AI+Incident+Tracker%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.", "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9CMIT+AI+Incident+Tracker+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+MIT+AI+Incident+Tracker%3F+Ground+your+answer+in+a+reading+of+the+entry+itself." }, "entry": { "composed": true, "title": "The MIT AI Incident Tracker", "genre": "knowledge object, equal case, under EA-NEGONT-02 v0.9 (#1690): the archive's sources and the field's admitted on equal terms, ontology included, in one claim space", "status": "composed 2026-10-29 under v0.9 (brief rebuild/negative-of-the-negative/v09/BRIEF-NEW-ROW-AGGREGATOR.md); not frozen; ledgers unaudited", "compression": { "title": "MIT AI Incident Tracker", "lede": "is the MIT AI Risk Initiative's machine classification of AI Incident Database reports by risk domain, cause, harm, severity and EU AI Act level, through Arcola AI's LLM pipeline (MMRS record; its page), its patterns, by its own word, \"indicative\"." }, "sentences": [ { "n": 1, "para": 0, "text": "The MIT AI Incident Tracker is a machine classification of AI Incident Database reports by risk domain, cause, harm, severity and EU AI Act risk level, made by Arcola AI's language-model pipeline against the MIT Risk Repository's taxonomy, and filed as a database (MMRS record); the MIT AI Risk Initiative's about page calls it \"a catalogue that classifies incidents where AI has already caused harm\", developed in collaboration with the AI Incident Database, and its own page says it classifies AIID incidents \"by risk, cause, harm, severity, and other relevant dimensions\".", "claims": [ "MMRS-01", "MMRS-10", "F46", "F1" ] }, { "n": 2, "para": 0, "text": "It is derived from the AI Incident Database (MMRS record): the tracker page builds on the Responsible AI Collaborative, \"who created and maintain the AI Incident Database, reviewing all submitted reports\", and sends its readers to the AIID for how incidents are defined and ingested, and the about page lists the AIID among the users of the Initiative's work, mapping real-world incidents to its taxonomy in the tracker.", "claims": [ "MMRS-18", "F8", "F50" ] }, { "n": 3, "para": 0, "text": "Its occasion, on its own page, is that incident databases \"struggle with inconsistent structure, limiting their utility for policymaking\", and its answer is a tool to classify incidents by risk and harm severity, offered as a common frame of reference for researchers, developers, businesses, evaluators, auditors, policymakers and regulators; the MMRS record restates the aim as a more consistent structure for AIID's records \"by its own account\", and the June 2026 update grounds it in what incident reporting did for safety in aviation, healthcare and nuclear power.", "claims": [ "F4", "F5", "F12", "MMRS-03", "F30" ] }, { "n": 4, "para": 1, "text": "Raw AIID reports are \"processed by Arcola AI's LLM pipeline\", which categorizes them with the MIT Risk Repository and a harm severity rating based on CSET's AI Harm Taxonomy (tracker page); the June 2026 update says the tracker uses LLMs to classify incident reports along five taxonomies, \"producing standardized data\", the Navigator that each incident is classified against shared taxonomies \"using an LLM pipeline with human spot-checking\", and the MMRS record that the classification is machine-made, its intake the AIID reports passed through Arcola AI's pipeline.", "claims": [ "F6", "F23", "F44", "MMRS-04", "MMRS-14" ] }, { "n": 5, "para": 1, "text": "The five taxonomies are harm severity across ten harm types, the EU AI Act risk tier, a causal taxonomy of lifecycle stage and intent, seven risk domains and twenty-four subdomains (June 2026 update); the tracker page shows each incident under the repository's causal and domain taxonomies and the EU AI Act's levels, with its severity across ten CSET-based harm categories and the classification's confidence, and the Initiative's home page lets incidents be compared by risk domain, harm, people affected, alleged developer, time or national-security impact.", "claims": [ "F24", "F9", "F10", "F20" ] }, { "n": 6, "para": 1, "text": "On 23 June 2025 the severity scale went from 0–10 to 1–5, its definitions disambiguated in the first person (\"I have tried to remove ambiguities\"), and the tracker added flags for ambiguities and alternative interpretations and the system's primary goal by a taxonomy based on the AIID's GMF (June 2025 update).", "claims": [ "F38", "F40" ] }, { "n": 7, "para": 1, "text": "A national-security view grades each incident's impact on its primary target country in five categories, from physical security and critical infrastructure to societal stability and human rights, with the threat classified for imminence, novelty and autonomy (tracker page; June 2025 update).", "claims": [ "F11", "F39" ] }, { "n": 8, "para": 2, "text": "It is part of the MIT AI Risk Initiative (tracker page), based at MIT FutureTech, a joint initiative of MIT's Institute of the Digital Economy and CSAIL, which keeps several connected workstreams because no single dataset captures AI risk and presents its data and frameworks as authoritative (about and home pages; card 2's summary lists among its tools an expert survey and the AI Risk Repository); the MMRS record gives the operator as the Initiative \"with Arcola AI's LLM pipeline\" and the tracker as part of the MIT AI Risk Repository.", "claims": [ "F7", "F47", "F48", "F22", "F21", "MMRS-09", "MMRS-17" ] }, { "n": 9, "para": 2, "text": "Simon Mylius \"developed and leads the AI Incident Tracker\", a classification tool and dashboard now hosted by the MIT AI Risk Repository, its analysis featured on the AIID (about page), and the tracker page's team names MIT FutureTech and University of Queensland researchers beside the AIID's Sean McGregor.", "claims": [ "F49", "F18" ] }, { "n": 10, "para": 3, "text": "The MMRS record gives the unit as the AIID report, classified by domain, cause, harm, severity and EU AI Act level; the tracker's pages count incidents (\"1,600 real-world report incidents\"), and the June 2025 update reclassified \"all incidents\" up to AIID incident ID #1116.", "claims": [ "MMRS-13", "F1", "F37", "F36" ] }, { "n": 11, "para": 3, "text": "MIT's surfaces give four figures: 1,600 in the tracker page's heading and \"more than 1,400\" in its description, which the MMRS record reports as the page giving its size twice; 1,752 \"real-world AI incident reports\" in the card's summary of that page; and on the Navigator \"more than 1300\" in its card and 1,497 in its page heading on 2026-10-29, beside headline figures of \"Up -76%\" year over year and 64% caused by AI systems.", "claims": [ "F1", "F2", "MMRS-05", "F3", "F41", "F42", "F45" ] }, { "n": 12, "para": 3, "text": "The MMRS record's count field (as of 2026-10-28, basis observed) carries the page's two figures, Google's card's 1,752 and the Navigator's \"more than 1300\", each with its surface, and the Capture Registry (2026-10-29) records the count \"three ways\" and that the composition gave none.", "claims": [ "MMRS-15", "CAP-1-04" ] }, { "n": 13, "para": 4, "text": "The tracker is versioned by reclassification: the June 2025 update brought it up to AIID incident #1116 and classified the full dataset again with the new iteration \"to ensure consistency\", and the June 2026 update names as its next step a more comprehensive validation study and announces that the full set of AIID reports will be reclassified with the updated model selection and prompts.", "claims": [ "F36", "F37", "F35" ] }, { "n": 14, "para": 4, "text": "That update (30 June 2026) reports a pilot: because the tracker relies on automated classification rather than manual review, two expert reviewers classified ten AIID incidents on the five taxonomies, eight LLMs were set against their consensus, and the human agreement was itself only \"Fair\" for harm severity (0.31) and \"Moderate\" for the EU AI Act level (0.56).", "claims": [ "F25", "F26", "F29" ] }, { "n": 15, "para": 4, "text": "Opus 4.6 and Kimi K2.5 met or exceeded the model in current use, Sonnet 4, on most taxonomies, the models did substantially worse on the EU AI Act level, and after prompt refinement Opus 4.6 met the human baseline on all five \"on this pilot sample\", a sample the update calls small; it concludes that LLM classification has accuracy comparable to the human baseline.", "claims": [ "F27", "F28", "F34", "F35" ] }, { "n": 16, "para": 5, "text": "The tracker page states its limits: the classification database explores \"the potential capabilities and limitations of a scalable incident analysis framework\", its AIID input rests on public and expert submissions of varying quality, voluntary reporting gives \"some degree of sampling bias\", spot-checks have improved it but \"a systematic validation study has not yet been completed\", and patterns \"should be taken as indicative and validated through further analysis\".", "claims": [ "F13", "F14", "F15", "F16", "F17" ] }, { "n": 17, "para": 5, "text": "The June 2026 update adds that LLMs can hallucinate details and show a score-range bias, the Navigator calls itself a research prototype whose data and analyses are \"preliminary and not yet validated\", and the MMRS record carries the page's caveat in its kernel, its article and its note.", "claims": [ "F33", "F43", "MMRS-02", "MMRS-06", "MMRS-16" ] }, { "n": 18, "para": 5, "text": "At its name Google's AI Overview composed the tracker \"as what it says it is, with its machine method named\", AIID as the source and LLM pipelines as the method, which are the MMRS record's derivation and intake, avoided stating a count, and left out the page's caveat of indicative patterns, voluntary reporting and no completed validation study (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-01", "CAP-1-03", "CAP-1-02", "CAP-1-05" ] }, { "n": 19, "para": 5, "text": "Data from the MIT AI Risk Initiative is licensed under CC BY 4.0 (tracker page), as most of its outputs are (about page).", "claims": [ "F19", "F47" ] }, { "n": 20, "para": 6, "text": "In the MMRS record it is a derived layer, \"the editor-resolved incidents of AIID, reclassified by a model\", whose admission inherits AIID's harm test and whose classifications are machine-written, so that original outputs are not preserved, and the chain from event to news report to AIID record to machine classification is \"a chain of receptions, each recorded\".", "claims": [ "MMRS-07", "MMRS-19", "MMRS-20", "MMRS-08" ] }, { "n": 21, "para": 6, "text": "The record keeps each assertion with its source and basis and the lifecycle apart from the observation that saw it (read 2026-10-28), and it rests every field on the one tracker page.", "claims": [ "MMRS-21", "MMRS-12", "MMRS-22" ] }, { "n": 22, "para": 6, "text": "The June 2026 update places the tracker among the incident databases it builds on, the AI Incident Database, the OECD's AI Incident Monitor, the AIAAIC Repository and the Political Deepfakes Database, adding to them a classification of each incident and a rating of its harm; the MMRS record gives it the role of harm.", "claims": [ "F31", "MMRS-11" ] }, { "n": 23, "para": 6, "text": "Its outputs have been used to report incident trends in the 2026 World Disasters Report and Time magazine (June 2026 update).", "claims": [ "F32" ] }, { "n": 24, "para": 6, "text": "Wikipedia has no article for the tracker, nor for the AI Risk Repository or the MIT AI Risk Initiative, and none of its articles contains the phrase \"AI Incident Tracker\"; its one mention of the work, in \"Voiceverse NFT plagiarism scandal\", says that \"The MIT AI Risk Repository catalogued the incident\" as \"economic and cultural devaluation of human effort\", referenced to the AIID's page for incident 277, which on 2026-10-29 shows the MIT classification as machine-classified under subdomain 4.3, fraud, scams and targeted manipulation, in the domain Malicious Actors & Misuse.", "claims": [ "F51", "F52", "F53", "F54" ] } ], "claims": { "MMRS-01": { "kind": "archive", "quote": "The MIT AI Incident Tracker classifies AI Incident Database reports by risk domain, cause, harm, severity and EU AI Act risk level, using Arcola AI's language-model pipeline and the MIT Risk Repository taxonomy.", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "article, kernel s1", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-10": { "kind": "archive", "quote": "database", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, kind", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F46": { "kind": "field", "quote": "A catalogue that classifies incidents where AI has already caused harm. Developed in collaboration with the AI Incident Database.", "source": "B6", "source_label": "MIT AI Risk Initiative — About - MIT AI Risk Initiative", "locus": "What we do, AI Incident Tracker", "modality": "stipulation" }, "F1": { "kind": "field", "quote": "The MIT AI Incident Tracker classifies 1,600 real-world report incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "opening ('How is AI harming us?')", "modality": "stipulation" }, "MMRS-18": { "kind": "archive", "quote": "{\"rel\": \"derived_from\", \"target\": \"ai-incident-database\"}", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, relations (derived_from)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F8": { "kind": "field", "quote": "We build on the great work done by the Responsible AI Collaborative who created and maintain the AI Incident Database, reviewing all submitted reports and ensuring the quality of the dataset. … Visit the AIID site to learn more about how incidents are defined and the ingestion process", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "What is the AI Incident Tracker? ¶3 s2–3", "modality": "attributed" }, "F50": { "kind": "field", "quote": "AI Incident Database … Maps real-world incidents to our taxonomy in the incident tracker.", "source": "B6", "source_label": "MIT AI Risk Initiative — About - MIT AI Risk Initiative", "locus": "Who uses our work, AI Incident Database", "modality": "documented" }, "F4": { "kind": "field", "quote": "AI incidents are on the rise, yet current databases struggle with inconsistent structure, limiting their utility for policymaking.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "What is the AI Incident Tracker? ¶1 s1", "modality": "interpretation" }, "F5": { "kind": "field", "quote": "The AI Incident Tracker project addresses this by creating a tool to classify AI incidents based on risks and harm severity.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "What is the AI Incident Tracker? ¶1 s2", "modality": "stipulation" }, "F12": { "kind": "field", "quote": "A common frame of reference for researchers, developers, businesses, evaluators, auditors, policymakers, and regulators", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "What can I use the AI Incident Tracker for?", "modality": "self-description" }, "MMRS-03": { "kind": "archive", "quote": "It gives AIID's records a more consistent structure for policymakers, by its own account.", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "article, What it is s1", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "F30": { "kind": "field", "quote": "Incident reporting has contributed to major safety improvements in high-stakes sectors such as aviation, healthcare, and nuclear power", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Introduction ¶1", "modality": "interpretation" }, "F6": { "kind": "field", "quote": "Raw reports from the AI Incident Database (AIID) are processed by Arcola AI's LLM pipeline, which categorizes them using established frameworks, such as the MIT Risk Repository and a harm severity rating system based on CSET’s AI Harm Taxonomy.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "What is the AI Incident Tracker? ¶1 s3", "modality": "stipulation" }, "F23": { "kind": "field", "quote": "The AI Incident Tracker uses large language models (LLMs) to process incident reports from the AI Incident Database (AIID) and classify them along five taxonomies, producing standardized data that policymakers and researchers can use to track trends.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Executive Summary s1", "modality": "stipulation" }, "F44": { "kind": "field", "quote": "The AI Incident Tracker draws on publicly reported incidents from the AI Incident Database — sourced from news coverage, academic publications, and government reports — and classifies each one against the Navigator's shared taxonomies using an LLM pipeline with human spot-checking.", "source": "B5", "source_label": "MIT AI Risk Navigator — Incidents - MIT AI Risk Navigator", "locus": "What is the AI Incident Tracker?", "modality": "stipulation" }, "MMRS-04": { "kind": "archive", "quote": "The classification is machine-made: an LLM pipeline reads the raw AIID reports and assigns the MIT taxonomy and a severity rating based on CSET's AI Harm Taxonomy.", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "article, What it is s2", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-14": { "kind": "archive", "quote": "AIID reports processed by an LLM pipeline (Arcola AI) against the MIT Risk Repository taxonomy and a severity rating based on CSET's AI Harm Taxonomy", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, intake (basis operator)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F24": { "kind": "field", "quote": "Harm Severity (how serious the harm was, rated across 10 harm-types), EU AI Act Risk Level (the regulatory risk tier under the EU AI Act), Causal Taxonomy (what stage of the AI lifecycle the harm stems from and whether it was intentional), Domain Taxonomy (7 high-level categories of risks, such as discrimination or malicious use), and Subdomain Taxonomy (24 more specific subcategories within domains)", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "What We Did ¶1", "modality": "stipulation" }, "F9": { "kind": "field", "quote": "View how incidents in the AI Incident Database are classified using the MIT AI Risk Repository’s causal and domain taxonomies, along with risk levels defined by the EU AI Act.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Incident Overview", "modality": "stipulation" }, "F10": { "kind": "field", "quote": "View detailed analysis of each AI incident in the incident database, including how the incident is classified, how severe the harm is (across 10 categories of harm), and the classification confidence. … View a harm taxonomy for AI incidents, which distinguishes between 10 types of harm (e.g., physical harm, property damage, financial loss, human rights) based on work by CSET", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Incident View; Harm Taxonomy", "modality": "stipulation" }, "F20": { "kind": "field", "quote": "Explore and compare real-world AI incidents by risk domain, type and severity of harm, number of people affected, alleged developer, time, or national security impact.", "source": "B2", "source_label": "MIT AI Risk Initiative — MIT AI Risk Initiative", "locus": "Our tools and data, 'What real-world harms are occurring?'", "modality": "stipulation" }, "F38": { "kind": "field", "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.", "source": "B4", "source_label": "MIT AI Risk Initiative — Incident Tracker - June 2025 Update - MIT AI Risk Repository", "locus": "Harm Severity", "modality": "documented" }, "F40": { "kind": "field", "quote": "‘Ambiguities identified’ and ‘Alternative interpretations’ - makes it easier to spot analyses where further review of the reports or investigation is required … Now includes the primary goal of the AI system involved in each incident, classified according to a taxonomy based on the AIID GMF.", "source": "B4", "source_label": "MIT AI Risk Initiative — Incident Tracker - June 2025 Update - MIT AI Risk Repository", "locus": "Ambiguities and Alternative Interpretations; Primary Goal of AI Systems", "modality": "stipulation" }, "F11": { "kind": "field", "quote": "National Security impact assessment for each AI incident in the database, including 5 categories of impact and threat classification (novelty, imminence, autonomy) … It is intended to be applicable across different nations, grading the impact to the primary target country and identifying other nations that were also affected.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "NatSec Incident View; NatSec Impact Framework", "modality": "stipulation" }, "F39": { "kind": "field", "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", "source": "B4", "source_label": "MIT AI Risk Initiative — Incident Tracker - June 2025 Update - MIT AI Risk Repository", "locus": "National Security Impact Assessment", "modality": "stipulation" }, "F7": { "kind": "field", "quote": "The AI Incident Tracker is part of the MIT AI Risk Initiative, which aims to increase awareness and adoption of best practice AI risk management across the AI ecosystem.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "What is the AI Incident Tracker? ¶3 s1", "modality": "documented" }, "F47": { "kind": "field", "quote": "We are based at MIT FutureTech, a joint initiative of the Institute of the Digital Economy and MIT CSAIL at the Massachusetts Institute of Technology … Our work is free to use and openly available, and most of our outputs are published under a CC BY 4.0 licence.", "source": "B6", "source_label": "MIT AI Risk Initiative — About - MIT AI Risk Initiative", "locus": "Who we are ¶2–3", "modality": "documented" }, "F48": { "kind": "field", "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.", "source": "B6", "source_label": "MIT AI Risk Initiative — About - MIT AI Risk Initiative", "locus": "What we do ¶1 (also the card 6 snippet)", "modality": "self-description" }, "F22": { "kind": "field", "quote": "Authoritative data and frameworks to help you identify, prioritize, and manage the risks from AI", "source": "B2", "source_label": "MIT AI Risk Initiative — MIT AI Risk Initiative", "locus": "hero", "modality": "self-description" }, "F21": { "kind": "field", "quote": "Tools and Data: Expert Survey (expert consensus on severe AI risks, vulnerabilities, and responsibilities), AI Risk Repository (living repository of AI risks)", "source": "B2", "source_label": "MIT AI Risk Initiative — MIT AI Risk Initiative", "locus": "card 2 snippet", "modality": "documented" }, "MMRS-09": { "kind": "archive", "quote": "MIT AI Risk Initiative, with Arcola AI's LLM pipeline", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, operator (basis operator, src airisk.mit.edu/ai-incident-tracker)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-17": { "kind": "archive", "quote": "{\"rel\": \"part_of\", \"target\": \"mit-ai-risk-repository\"}", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, relations (part_of)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F49": { "kind": "field", "quote": "He developed and leads the AI Incident Tracker - a classification tool and dashboard to add structure to datasets of reported AI safety incidents, which is now hosted by the MIT AI Risk Repository and its analysis features on AI Incident Database.", "source": "B6", "source_label": "MIT AI Risk Initiative — About - MIT AI Risk Initiative", "locus": "Team, Simon Mylius", "modality": "documented" }, "F18": { "kind": "field", "quote": "Simon Mylius MIT FutureTech Peter Slattery MIT FutureTech … Sean McGregor AI Incident Database … Neil Thompson MIT FutureTech", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Team", "modality": "documented" }, "MMRS-13": { "kind": "archive", "quote": "AI Incident Database report, classified by risk domain, cause, harm, severity and EU AI Act risk level", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, unit (basis operator)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F37": { "kind": "field", "quote": "The full dataset has been classified using this latest iteration of the tool (reclassification of all incidents that were classified with the previous version, to ensure consistency across the dataset).", "source": "B4", "source_label": "MIT AI Risk Initiative — Incident Tracker - June 2025 Update - MIT AI Risk Repository", "locus": "LLM Analysis", "modality": "documented" }, "F36": { "kind": "field", "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)", "source": "B4", "source_label": "MIT AI Risk Initiative — Incident Tracker - June 2025 Update - MIT AI Risk Repository", "locus": "Major update - 23 June 2025", "modality": "documented" }, "F2": { "kind": "field", "quote": "The MIT AI Incident Tracker classifies more than 1,400 real-world reported incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "meta description (name=description, og:description, twitter:description), extracted to B1-meta.txt", "modality": "documented" }, "MMRS-05": { "kind": "archive", "quote": "The page gives its size twice, as \"more than 1,400\" in its description and 1,600 in its heading.", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "article, What its record states about itself s1", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F3": { "kind": "field", "quote": "Overview: MIT AI Incident Tracker classifies 1,752 real-world AI incident reports from the AI Incident Database using Arcola AI's LLM pipeline", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "card 1 snippet (the count is not in the saved page)", "modality": "documented" }, "F41": { "kind": "field", "quote": "Explore more than 1300 real-world reported incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions.", "source": "B5", "source_label": "MIT AI Risk Navigator — Incidents - MIT AI Risk Navigator", "locus": "card 5 snippet (the page's meta description reads 'more than 1,300')", "modality": "documented" }, "F42": { "kind": "field", "quote": "1,497 real-world AI incidents from the AI Incident Database, classified by risk domain, cause, harm type, and severity.", "source": "B5", "source_label": "MIT AI Risk Navigator — Incidents - MIT AI Risk Navigator", "locus": "AI Incident Tracker heading", "modality": "documented" }, "F45": { "kind": "field", "quote": "Up -76% … year-over-year … 64% … caused by AI systems … high-severity in 2026 … Malicious Actors & Misuse … most affected domain", "source": "B5", "source_label": "MIT AI Risk Navigator — Incidents - MIT AI Risk Navigator", "locus": "headline figures", "modality": "documented" }, "MMRS-15": { "kind": "archive", "quote": "the page gives both 'more than 1,400' (description) and 1,600 (heading); Google's card summary gives 1,752, and the MIT AI Risk Navigator 'more than 1300'", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, count.value (basis observed; as_of 2026-10-28)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "CAP-1-04": { "kind": "archive", "quote": "THE COUNT, THREE WAYS. The tracker's page as read gives 'more than 1,400' in its description and 1,600 in its heading (the record); the card's summary gives 1,752; the Navigator card says 'more than 1300'. The composition gives none.", "deposit": null, "source_label": "Capture Registry observation OBS-65034e45b99a (mit-ai-incident-tracker-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/mit-ai-incident-tracker-aio-20261029/", "locus": "mit-ai-incident-tracker-aio-20261029, findings[1]", "modality": "documented", "date": "2026-10-29" }, "F35": { "kind": "field", "quote": "Our results suggest that LLM-based incident classification has comparable accuracy to the human baseline and is a viable approach for scaling the Incident Tracker. … The next step is to run a more comprehensive validation study based on this pilot. … The full set of reports from the AI Incident Database will be reclassified using the updated model selection and adapted prompts, to refresh the Incident Tracker with more reliable classifications.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Next steps", "modality": "prediction" }, "F25": { "kind": "field", "quote": "Because the tracker relies on automated LLM classification rather than manual review, we need to understand whether LLMs can classify incidents as consistently as expert humans. This report describes a pilot study to compare the performance of eight LLMs against expert human reviewers", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Executive Summary s2–3", "modality": "self-description" }, "F26": { "kind": "field", "quote": "We asked two expert reviewers to independently read reports of 10 incidents and classify each one using five taxonomies … We selected 10 incident reports from the AI Incident Database.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "What We Did ¶1; Methodology", "modality": "documented" }, "F29": { "kind": "field", "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).", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Results, Evaluating the human baseline", "modality": "documented" }, "F27": { "kind": "field", "quote": "Opus 4.6 and Kimi K2.5 were the two strongest models in our evaluation and met or exceeded the currently used model, Sonnet 4, on most taxonomies.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "What We Found ¶1", "modality": "documented" }, "F28": { "kind": "field", "quote": "On the EU AI Act Risk Level taxonomy they performed substantially worse. … After targeted prompt refinement, Opus 4.6 matched or exceeded the human-human agreement baseline on all five taxonomies on this pilot sample.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "What We Found ¶3–4", "modality": "documented" }, "F34": { "kind": "field", "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.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Future Work ¶1 s2", "modality": "self-description" }, "F13": { "kind": "field", "quote": "This classification database is intended to explore the potential capabilities and limitations of a scalable incident analysis framework.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Limitations of the analysis and dataset ¶1 s1", "modality": "self-description" }, "F14": { "kind": "field", "quote": "The classification analysis uses reports from the AI Incident Database (AIID) as input data which rely on submissions from the public and subject matter experts. The quality, reliability and depth of detail in the reports varies across the dataset.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Limitations ¶1 s2–3", "modality": "self-description" }, "F15": { "kind": "field", "quote": "As the reporting is voluntary, the dataset is inevitably subject to some degree of sampling bias.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Limitations ¶1 s4", "modality": "self-description" }, "F16": { "kind": "field", "quote": "Spot-checks have been used to provide feedback on misclassifications and to iterate the tool, improving its reliability, however a systematic validation study has not yet been completed.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Limitations ¶1 s5", "modality": "self-description" }, "F17": { "kind": "field", "quote": "Therefore patterns and trends observed in the data should be taken as indicative and validated through further analysis.", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Limitations ¶2", "modality": "self-description" }, "F33": { "kind": "field", "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.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Introduction ¶4–5", "modality": "self-description" }, "F43": { "kind": "field", "quote": "This is a research prototype. The data and analyses are preliminary and not yet validated", "source": "B5", "source_label": "MIT AI Risk Navigator — Incidents - MIT AI Risk Navigator", "locus": "banner", "modality": "self-description" }, "MMRS-02": { "kind": "archive", "quote": "Its own page calls the patterns indicative.", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "article, kernel s2", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-06": { "kind": "archive", "quote": "It notes that reporting is voluntary, that no systematic validation study is complete, and that patterns \"should be taken as indicative.\"", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "article, What its record states about itself s2", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-16": { "kind": "archive", "quote": "the page states that patterns 'should be taken as indicative'; reporting is voluntary and no systematic validation study is complete", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, note (basis operator)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "CAP-1-01": { "kind": "archive", "quote": "The tracker is composed as what it says it is, with its machine method named.", "deposit": null, "source_label": "Capture Registry observation OBS-65034e45b99a (mit-ai-incident-tracker-aio-20261029), field analysis", "record": "https://www.alexanarch.org/captures/mit-ai-incident-tracker-aio-20261029/", "locus": "mit-ai-incident-tracker-aio-20261029, analysis s1", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-03": { "kind": "archive", "quote": "THE DERIVATION RIGHT. AIID as the source and LLM pipelines as the method are the record's 'derived from' relation and intake. Checked 2026-10-29 against the MMRS meta-aggregator record of 2026-10-28 and the entity's own pages, re-read the same day.", "deposit": null, "source_label": "Capture Registry observation OBS-65034e45b99a (mit-ai-incident-tracker-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/mit-ai-incident-tracker-aio-20261029/", "locus": "mit-ai-incident-tracker-aio-20261029, findings[0]", "modality": "documented", "date": "2026-10-29" }, "CAP-1-02": { "kind": "archive", "quote": "Its count is unstable across MIT's own surfaces, and the composition avoids stating one; its caveat is dropped.", "deposit": null, "source_label": "Capture Registry observation OBS-65034e45b99a (mit-ai-incident-tracker-aio-20261029), field analysis", "record": "https://www.alexanarch.org/captures/mit-ai-incident-tracker-aio-20261029/", "locus": "mit-ai-incident-tracker-aio-20261029, analysis s2", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-05": { "kind": "archive", "quote": "THE CAVEAT UNSAID. The page's own statement that patterns 'should be taken as indicative', with voluntary reporting and no validation study complete, is absent.", "deposit": null, "source_label": "Capture Registry observation OBS-65034e45b99a (mit-ai-incident-tracker-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/mit-ai-incident-tracker-aio-20261029/", "locus": "mit-ai-incident-tracker-aio-20261029, findings[2]", "modality": "documented", "date": "2026-10-29" }, "F19": { "kind": "field", "quote": "Website © MIT FutureTech 2026 … Data from the MIT AI Risk Initiative is licensed under CC BY 4.0", "source": "B1", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "footer", "modality": "documented" }, "MMRS-07": { "kind": "archive", "quote": "It is a derived layer: the editor-resolved incidents of AIID, reclassified by a model.", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "article, Place in the field s1", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-19": { "kind": "archive", "quote": "inherits AIID's harm test", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, properties.admission_requires_harm (basis reading)", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-20": { "kind": "archive", "quote": "no: classifications are machine-written", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, properties.preserves_original_outputs (basis reading)", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-08": { "kind": "archive", "quote": "The chain from event to news report to AIID record to machine classification is a chain of receptions, each recorded.", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "article, Place in the field s2", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-21": { "kind": "archive", "quote": "each assertion carries a source and a basis (operator statement, third-party report, our observation, our reading); lifecycle is kept apart from the observation that saw it", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, schema_note", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-12": { "kind": "archive", "quote": "{\"on\": \"2026-10-28\", \"fetch\": \"read\"}", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, observed (lifecycle.state 'active' kept apart)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-22": { "kind": "archive", "quote": "read\": \"2026-10-28\", \"for\": \"all fields", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, sources", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F31": { "kind": "field", "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.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Introduction ¶2", "modality": "interpretation" }, "MMRS-11": { "kind": "archive", "quote": "[\"harm\"]", "deposit": null, "source_label": "MMRS meta-aggregator record: MIT AI Incident Tracker (census row)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census.json, roles", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F32": { "kind": "field", "quote": "These outputs have been used to report on incident trends, including in the 2026 World Disasters Report and Time magazine.", "source": "B3", "source_label": "MIT AI Risk Initiative — AI Incident Tracker June 2026 Update - MIT AI Risk Initiative", "locus": "Introduction ¶3 s3", "modality": "documented" }, "F51": { "kind": "field", "quote": "Wikipedia does not have an article with this exact name.", "source": "W1", "source_label": "Wikipedia — MIT AI Incident Tracker (no article)", "locus": "rendered page https://en.wikipedia.org/wiki/MIT_AI_Incident_Tracker (HTTP 404)", "modality": "documented" }, "F52": { "kind": "field", "quote": "There were no results matching the query.", "source": "W1", "source_label": "Wikipedia — MIT AI Incident Tracker (no article)", "locus": "full-text search '\"AI Incident Tracker\"' (W1-search.txt)", "modality": "documented" }, "F53": { "kind": "field", "quote": "The MIT AI Risk Repository catalogued the incident as an example of \"economic and cultural devaluation of human effort\"", "source": "W3", "source_label": "Wikipedia — Voiceverse NFT plagiarism scandal", "locus": "Voiceverse NFT plagiarism scandal, Reactions and legacy ¶ (found by full-text search '\"AI Risk Repository\"', W2-search.txt: one result)", "modality": "attributed" }, "F54": { "kind": "field", "quote": "GMF, MIT MIT Taxonomy Classifications Machine-Classified … 4.3. Fraud, scams, and targeted manipulation … Malicious Actors & Misuse", "source": "W4", "source_label": "AI Incident Database — Incident 277 (Wikipedia's reference)", "locus": "incidentdatabase.ai/cite/277 (the reference Wikipedia gives), Applied Taxonomies", "modality": "documented" } }, "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. 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