{ "entity": "ai-incident-database", "name": "AI Incident Database (AIID; Responsible AI Collaborative)", "axn": { "axn": "AXN:0AB8.NON.🌱🏛️💫📜🖤⛩️", "root": "AXN:0AB8.NON", "hex": "0AB8", "family": "NON", "row_sha256": "19200a40cf2836b2da3a1cc7c06ef4a6aae02fb68696d300b528729d9243a46d", "minted": "2026-10-29", "resolver": "https://www.alexanarch.org/s/axn/0AB8/", "earlier_versions": [ { "axn": "AXN:0AB8.NON.📐⏩🌖🪸🎪△", "sha256": "4ed6057b8598df11d920639d1fff6581b811ed633258f7874e4bfc8309e40847", "from": "2026-10-29", "until": "2026-10-29", "reason": "sixteen new rows composed 2026-10-29: the MMRS meta-aggregator's entities" } ] }, "type": "C (public entity: an aggregator of machine-mediated reception work)", "type_code": "C", "type_label": "type C — a conventional reading holds it (public entity: an aggregator of machine-mediated reception work)", "case": "equal", "composition": { "composer_class": "session", "composer": "Claude (Opus 5.5), subagent, 2026-10-29", "source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/ai-incident-database.json)", "spec": "EA-NEGONT-02 v0.9, #1690", "epoch": "2026-10-29", "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the MMRS record and the two capture records; archive selected by D/R/O (one deposit of one candidate admitted, #543 at R; hop of 41 read and rejected; O-class matched nothing), with the MMRS record (37 claims) and the Capture Registry's findings and analysis (12 claims) ledgered as the archive's records of the entity; the field B by three origins (14 card sources for 15 cards, 4 own pages, 2 Wikipedia pages); the composer read the ledgers, never the transcripts, except where the brief asks for T's claims and the delta", "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 75/75 verified by script against saved text, PDF text, raw wikitext, oEmbed title or card text; archive quotes 50/50 verified verbatim against the registry, census.json, articles.py and AXN-0174; carried as marked: MMRS-01 contested (F73), MMRS-12 and MMRS-19 and CAP-1-04 with defects noted", "frozen": false, "composed": true }, "reading": "A reader that fetches this page may stop short of its end; the entry's text, every sentence of its knowledge object with the claims it cites and each claim's quote, source and locus, is in entity.json beside it.", "service": { "name": "SPXI-gnΩsis", "url": "https://gnosis.spxi.dev/", "index": "https://gnosis.spxi.dev/v1/quote", "statement": "SPXI-gnΩsis opens pinned Wikipedia revisions sentence by sentence against the sources they cite (EA-WIKI-OPEN-01 v1.1, #1697); a commission consults the Crimson Hexagonal Archive by its own procedure and composes the entity's /non row, which lands here. 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The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/ai-incident-database/", "research": { "google": "https://www.google.com/search?q=AI+Incident+Database+%28AIID%3B+Responsible+AI+Collaborative%29", "google_non": "https://www.google.com/search?q=%22AI+Incident+Database+%28AIID%3B+Responsible+AI+Collaborative%29+%E2%80%94+The+Negative+of+the+Negative%22", "chatgpt": "https://chatgpt.com/?q=What+is+AI+Incident+Database+%28AIID%3B+Responsible+AI+Collaborative%29%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.", "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9CAI+Incident+Database+%28AIID%3B+Responsible+AI+Collaborative%29+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+AI+Incident+Database+%28AIID%3B+Responsible+AI+Collaborative%29%3F+Ground+your+answer+in+a+reading+of+the+entry+itself." }, "entry": { "composed": true, "title": "The AI Incident Database", "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": "AI Incident Database", "lede": "is an editor-resolved database of AI harm events, run by the Responsible AI Collaborative (MMRS record; its home page), \"dedicated to indexing the collective history of harms or near harms\": its unit the incident, reports attached, each with a citation page at /cite/N." }, "sentences": [ { "n": 1, "para": 0, "text": "The AI Incident Database (AIID) is an editor-resolved database of AI harm events (MMRS record), \"dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems\" in its home page's words, which the MMRS record quotes and the OECD's catalogue repeats as a \"free and open-source project\", and which Paeth and colleagues (AAAI-25) describe as a project that catalogs AI incidents and provides a platform to classify them for operational and research goals.", "claims": [ "MMRS-01", "F1", "MMRS-03", "F12", "F49" ] }, { "n": 2, "para": 0, "text": "Its unit is the incident, with the reports that document it attached, and every incident has a stable citation page at incidentdatabase.ai/cite/N (MMRS record); the Editor's Guide defines the incident as \"an alleged harm or near harm event to people, property, or the environment where an AI system is implicated\" and the report as a source document attached to an incident ID, several of which may document one event, and the MMRS record quotes the definition and adds that reports may be news articles, papers or posts.", "claims": [ "MMRS-02", "F19", "F22", "MMRS-06" ] }, { "n": 3, "para": 0, "text": "Its home page sets it beside \"similar databases in aviation and computer security\", to learn from experience and prevent or mitigate bad outcomes; McGregor's founding paper starts from mature industrial sectors such as aviation that collect their failures in incident databases, while intelligent systems \"cause real world harms without a collective memory of their failings\"; the about page invokes the transportation sector (FAA and FARS) and computer systems, the research page \"Defining an AI Incident\" takes aviation's distinction of an accident (substantial damage or loss of life) from an incident (the risk of an accident substantially increased), and Partnership on AI's workstream page places it among safety-critical systems in transportation, healthcare and energy, a crowdsourced central repository of more than 1,200 reports.", "claims": [ "F2", "F66", "F41", "F37", "F11", "F10" ] }, { "n": 4, "para": 1, "text": "Sean McGregor's founding paper, submitted to arXiv on 17 November 2020, describes \"an incident collection initiated by an industrial/non-profit cooperative\", searchable by facet and full text over more than 1,000 incident reports, and on 18 November 2020 he introduced it on Partnership on AI's site as \"a systematized collection of incidents where intelligent systems have caused safety, fairness, or other real-world problems\" (the introduction quoted in Wikipedia's reference); the snapshots page asks that the database as a whole be cited by his IAAI-21 paper, and the MMRS record dates the founding to 2020 as the arXiv paper describes it, with more than 1,000 reports at launch.", "claims": [ "F65", "F67", "F68", "F74", "F56", "MMRS-19", "MMRS-05" ] }, { "n": 5, "para": 1, "text": "The home and about pages call it \"a project of the Responsible AI Collaborative, an organization chartered to advance the AI Incident Database\"; the OECD's catalogue entry (uploaded 25 April 2024) names the Collaborative as developing organisation and MIT's tracker credits it with creating and maintaining the database; the about page credits Sean McGregor with launching it and names him Executive Director, heads a logo block \"Database Founding Sponsor\" whose image the page labels Partnership on AI, and records that two people contributed to its early stages through their roles with the Partnership on AI; the MMRS record gives the same operator, founder, director and founding sponsor.", "claims": [ "F4", "F14", "F8", "F46", "F47", "MMRS-04", "MMRS-37" ] }, { "n": 6, "para": 1, "text": "Wikipedia has no article of its own for it: \"AI Incident Database\" redirects, with no section anchor, to Partnership on AI (a redirect from a subtopic, with possibilities), whose article records that Partnership on AI announced it in November 2020 as \"a tool to identify, assess, manage, and communicate AI risk and harm\", that Partnership on AI's response to NIST presented it as meeting attributes of the AI Risk Management Framework, that it was among the Safety Critical AI program's initiatives, and, under programs and initiatives, that it \"shifted to a new special-purpose independent non-profit in 2022\", the non-profit unnamed; the article is tagged for primary sources (April 2025) and as AI-generated (October 2025).", "claims": [ "F69", "F70", "F71", "F72", "F73", "F75" ] }, { "n": 7, "para": 1, "text": "The MMRS record has the database run by the Responsible AI Collaborative since 2020, and Wikipedia has it shift to an independent non-profit in 2022; the two dates stand each with its source.", "claims": [ "MMRS-01", "F73" ] }, { "n": 8, "para": 2, "text": "The Editor's Guide (Version 2, 1 October 2026, by Daniel Atherton and Sean McGregor, drafted with disclosed generative-AI assistance) keeps the incident apart from the AI issue, \"an alleged harm or near harm by an AI system that has yet to occur or be detected\", from the incident ID, the editorial record that bounds and summarizes an event, and from the report: \"A report is evidence; an incident ID is the database’s normalized account of an event\", so that a later source on the same event is usually an additional report, not a new incident.", "claims": [ "F36", "F20", "F21", "F23" ] }, { "n": 9, "para": 2, "text": "A variant is, in the guide, \"a distinct incident involving the same intelligent system, substantially similar causative factors, and substantially similar harms as a known incident\", as the MMRS record restates it; the incident page for 693 describes the variant as having the same causes, harms and system, grouped under the first reported incident, and adds that variants \"do not need to have been reported outside the AIID\".", "claims": [ "F24", "MMRS-07", "F62" ] }, { "n": 10, "para": 2, "text": "The Capture Registry (2026-10-29) records that at the database's name Google's AI Overview gave neither the incident as the unit, nor the reports attached to it, nor the citation page /cite/N, \"collects public and crowdsourced reports\" standing in for the unit, and that at the question of what counts the incident, the issue and the variant were the guide's definitions in substance, the first verbatim, and the report was again not stated; the MMRS record's negative tags hold that it is not a list of research papers, its unit the event and papers entering as reports.", "claims": [ "CAP-1-05", "CAP-2-03", "CAP-2-05", "MMRS-24" ] }, { "n": 11, "para": 3, "text": "A candidate passes the guide's five-question test: a specific event, an identifiable AI system, a causal implication (an important role in the chain of events, not necessarily the sole or dominant cause), harm or credible near harm (a chain that could readily have produced harm, stopped by an external factor independent of the system and not a built-in safeguard functioning as designed), and adequate evidence, at least one source supporting the essential event (guide); the MMRS record states the same test and records that admission requires harm or credible near harm.", "claims": [ "F26", "F28", "F29", "F27", "MMRS-08", "MMRS-26" ] }, { "n": 12, "para": 3, "text": "At \"what counts as an incident in the ai incident database\" the Overview read the guide and kept its distinctions with one compression, the five-part test given as three thresholds, the identifiable system folded into \"AI implication\" and adequate evidence dropped, so that the composition carried the test's harm condition and lost its evidence condition, the condition on which the MMRS record's account of what its selection leaves out rests (Capture Registry, 2026-10-29).", "claims": [ "CAP-2-01", "CAP-2-04", "CAP-2-02" ] }, { "n": 13, "para": 3, "text": "The research page \"Defining an AI Incident\" calls the criteria adaptive, reports \"accepted or rejected on the basis of a growing rule set defined in the Editor's Guide\"; the about page asks that an event be submitted when in doubt, the project meant to converge on shared criteria through the community's candidates, and says its initial set of more than 1,000 incident reports was intentionally broad, an autonomous car killing a pedestrian among its examples; the guide's presumptive exclusions include general criticism of a product or technology class without identified harm and benchmark failures, model errors or hallucinations that remain inside testing or private drafting, \"presumptions rather than absolute prohibitions\", which the MMRS record quotes as \"benchmark failures that stay in testing\" and \"general criticism of a technology\".", "claims": [ "F38", "F43", "F42", "F30", "MMRS-12" ] }, { "n": 14, "para": 4, "text": "Anyone may submit (MMRS record; the home page invites incident reports, to be indexed and made discoverable), and the incident editors, Daniel Atherton the lead, resolve submissions and maintain them; the database is managed in a participatory manner by those contributing code, research and broader impacts, CSET and Nikiforos Pittaras (GMF) have contributed its taxonomies (about page), MIT's tracker credits the Collaborative with reviewing all submitted reports, and the MMRS record's intake field states open submission with editors resolving submissions and maintaining records.", "claims": [ "MMRS-09", "F3", "F45", "F44", "F48", "MMRS-37" ] }, { "n": 15, "para": 5, "text": "Every incident has a suggested citation crediting its submitters and editors, at incidentdatabase.ai/cite/N (snapshots page); that citation page per incident is the model the MMRS meta-aggregator follows, and the MMRS record observes per item the incident id and citation page, title, date, alleged deployer and developer, harmed parties, attached reports, CSET and GMF classifications and variants.", "claims": [ "F55", "MMRS-16", "MMRS-21" ] }, { "n": 16, "para": 5, "text": "A duplicate is merged into the earlier record and its citation page stays up and points there (MMRS record): the citation record for 637 reads \"This incident is a duplicate of Incident 112\", its reports migrated, as the guide's duplicate mapping redirects a retired identifier to its canonical record; ids therefore include merged duplicates, and the highest id seen, 1788 (MMRS record, 2026-10-28; the home page on 2026-10-29), is an upper bound on incidents, against which Paeth and colleagues counted over 750 distinct incidents detailed by more than 3000 indexed third-party reports at their writing.", "claims": [ "MMRS-10", "F64", "F31", "MMRS-20", "F5", "F51" ] }, { "n": 17, "para": 5, "text": "The whole database is offered as weekly point-in-time dumps in JSON, MongoDB archive and CSV, listed from 29 March 2021 to 26 October 2026, with a weekly Excel export of numbered incidents and their taxonomy classifications (snapshots page; MMRS record, which counts the weekly snapshots as its version history); the guide warns that the live site can change after an export is created.", "claims": [ "F54", "F57", "MMRS-11", "MMRS-22", "MMRS-27", "F35" ] }, { "n": 18, "para": 5, "text": "The MMRS record keeps itself apart from the entity it describes, each assertion with a source and a basis (operator statement, third-party report, observation, reading) and the lifecycle (active) apart from the observation that saw it (2026-10-28), and it leaves the Wikidata item and any data licence beyond the snapshots page unchecked.", "claims": [ "MMRS-30", "MMRS-31", "MMRS-32" ] }, { "n": 19, "para": 6, "text": "The OECD's catalogue gives its usage rights as open source/permissive and its licence as Apache 2.0, without saying of what; the MMRS record observes the code open source and no data licence stated on the snapshots page, and the Capture Registry (2026-10-29) records that the Overview's \"free, open-source\" does not distinguish the two.", "claims": [ "F13", "MMRS-23", "CAP-1-07" ] }, { "n": 20, "para": 7, "text": "The guide's governing principle is that the database \"records publicly documented allegations of harm or near harm\" and \"does not independently adjudicate every factual claim\"; it is \"a curated archive of publicly documented incidents, issues, reports, and responses\", not a neutral mirror of all AI harms, a comprehensive denominator for risk rates or an independent adjudication of every event; wealthy countries, prominent companies, spectacular events and English-language reporting are likely overrepresented, and harms to marginalized groups, inside private organizations or in ordinary administration may be underrepresented; and \"A lack of records is not evidence that a sector or country has no incidents.\"", "claims": [ "F25", "F32", "F33", "F34" ] }, { "n": 21, "para": 7, "text": "Paeth and colleagues find that certain patterns of incidents present structural ambiguities that challenge incident databasing and that epistemic uncertainty in incident reporting is unavoidable; MIT's tracker states that, the reporting being voluntary, its input is subject to some sampling bias, and that a systematic validation study of its classifications has not yet been completed.", "claims": [ "F50", "F9" ] }, { "n": 22, "para": 7, "text": "On the MMRS record's reading a measured study enters as a report when it documents an event, and what an answer surface does with a source on an ordinary day, with no harm event, has no unit: attribution erasure, citation fidelity and provenance stripping enter when they attach to a harm, so the database has no field for attribution, and whether the reports' texts are archived beyond their links the record leaves unknown.", "claims": [ "MMRS-13", "MMRS-14", "MMRS-29", "MMRS-28" ] }, { "n": 23, "para": 7, "text": "The archive's Hexagonal Lexical Engine (#543, 10 March 2026) lists AI ethics glossaries, \"AIAAIC, AI Incident DB\", among the venues to which it addresses definitions with citations of three of its terms: semantic liquidation, ghost governance and taxonomic violence.", "claims": [ "R543-01" ] }, { "n": 24, "para": 8, "text": "Incident 693, \"Google AI Reportedly Delivering Confidently Incorrect and Harmful Information\", dated 2024-05-14 and edited by Daniel Atherton, alleges that Google developed and deployed the system and that Google users and the general public were harmed, carries reports naming Google's AI Overviews, and is machine-classified under MIT's taxonomy as false or misleading information (incident page); the MMRS record calls it the field's index case in the database, holding it with seven reports and the AI Overviews launch errors.", "claims": [ "F58", "F59", "F60", "F61", "MMRS-15", "MMRS-36" ] }, { "n": 25, "para": 9, "text": "MIT's AI Incident Tracker classifies 1,600 incidents from the database by risk, cause, harm, severity and other dimensions, its raw reports processed by Arcola AI's LLM pipeline against the MIT Risk Repository and a severity scale based on CSET's AI Harm Taxonomy (tracker page); the MMRS record lists the tracker as classifying AIID reports, and the MIT AI Risk Repository as aggregating risk frameworks over the same ground with its own incident tracker.", "claims": [ "F6", "F7", "MMRS-34", "MMRS-18" ] }, { "n": 26, "para": 9, "text": "In the harm basin, where the MMRS record gives it the roles of harm and monitoring, its neighbours are the OECD AI Incidents and Hazards Monitor, automated and news-derived (MMRS record; incident 693's page describes it as collecting and classifying incidents and hazards in real time from news sources), and the AIAAIC Repository; it is neither the OECD monitor nor a vulnerability database (MMRS record).", "claims": [ "MMRS-35", "MMRS-17", "F63", "MMRS-24" ] }, { "n": 27, "para": 9, "text": "The Kaggle dataset \"AI Incidents, Failures & Controversies Database\" (32 incidents, 29 companies, 13 countries, 24 columns, CC BY-SA 4.0), which takes primary incident records and AIID identifiers from the database and cites Stanford HAI's AI Index 2025 for a 56.4% surge in AI incidents in 2024, is another dataset with a near name (MMRS record), and at the database's name the Overview did not use it, its card beside the database's own (Capture Registry, 2026-10-29).", "claims": [ "F15", "F17", "F16", "MMRS-25", "CAP-1-06" ] }, { "n": 28, "para": 10, "text": "The rails at the two addresses also carry an ACM paper read only by its snippet (submissions of incidents and controversies included by criteria such as relevance, fairness and accuracy), two TIME clips on LinkedIn titled \"AI Incident Database | TIME posted on the topic\" and \"AI Incidents Rise 50% Year-Over-Year: AI Incident Database\", and two YouTube videos admitted by title, \"Examples of AI real attacks\" and \"Establishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell | IASEAI 2025\".", "claims": [ "F52", "F18", "F39", "F40", "F53" ] }, { "n": 29, "para": 10, "text": "At its name the Overview composed the database at the grain of its own about page and kept it apart from its neighbours, giving the operator, the aviation model and the MIT tracker as a derived layer, which are the MMRS record's fields, and omitting the incident-and-report structure that makes the database what it is; no card was machinemediation.org, the meta-aggregator's record not yet indexed (Capture Registry, 2026-10-29), whose battery asks that the operator be named, the unit stated with reports attached, the database not merged with the OECD monitor or the AIAAIC, and the citation page given (MMRS record).", "claims": [ "CAP-1-01", "CAP-1-04", "CAP-1-02", "CAP-1-03", "MMRS-33" ] } ], "claims": { "MMRS-01": { "kind": "archive", "quote": "The AI Incident Database (AIID) is an editor-resolved database of AI harm events, run by the Responsible AI Collaborative since 2020.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, kernel s1", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F1": { "kind": "field", "quote": "The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems.", "source": "B1", "source_label": "AI Incident Database — AI Incident Database", "locus": "About the Database ¶1", "modality": "stipulation" }, "MMRS-03": { "kind": "archive", "quote": "The AI Incident Database describes itself as \"dedicated to indexing the collective history of harms or near harms realized in the real world.\"", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, What it is s1", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "F12": { "kind": "field", "quote": "The AI Incident Database is free and open-source project dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems.", "source": "B4", "source_label": "OECD AI Policy Observatory — AI Incident Database", "locus": "catalogue entry, description ¶1", "modality": "documented" }, "F49": { "kind": "field", "quote": "The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals.", "source": "B12", "source_label": "The Association for the Advancement of Artificial Intelligence — Lessons for Editors of AI Incidents from the AI Incident Database - AAAI Publications", "locus": "Abstract (article page)", "modality": "interpretation" }, "MMRS-02": { "kind": "archive", "quote": "Its unit is the incident, with the reports that document it attached, and every incident has a stable citation page at incidentdatabase.ai/cite/N.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, kernel s2", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F19": { "kind": "field", "quote": "An alleged harm or near harm event to people, property, or the environment where an AI system is implicated.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "Quick Definitions: AI incident (also §2.1)", "modality": "stipulation" }, "F22": { "kind": "field", "quote": "A source document attached to an incident ID. Multiple reports may document the same underlying event.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "Quick Definitions: Incident report", "modality": "stipulation" }, "MMRS-06": { "kind": "archive", "quote": "Its unit is the incident: \"an alleged harm or near harm event to people, property, or the environment where an AI system is implicated.\" Reports (news articles, papers, posts) attach to incidents, and several reports may document one event.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, How it works ¶1 s1–2", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "F2": { "kind": "field", "quote": "Like similar databases in aviation and computer security, the AI Incident Database aims to learn from experience so we can prevent or mitigate bad outcomes.", "source": "B1", "source_label": "AI Incident Database — AI Incident Database", "locus": "About the Database ¶1", "modality": "stipulation" }, "F66": { "kind": "field", "quote": "Mature industrial sectors (e.g., aviation) collect their real world failures in incident databases to inform safety improvements. Intelligent systems currently cause real world harms without a collective memory of their failings.", "source": "B18", "source_label": "arXiv — [2011.08512] Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database", "locus": "Abstract", "modality": "interpretation" }, "F41": { "kind": "field", "quote": "Intelligent systems are currently prone to unforeseen and often dangerous failures when they are deployed to the real world. Much like the transportation sector before it (e.g., FAA and FARS) and more recently computer systems, intelligent systems require a repository of problems experienced in the real world so that future researchers and developers may mitigate or avoid repeated bad outcomes.", "source": "B11", "source_label": "AI Incident Database — The details about who and why for database - AI Incident Database", "locus": "Why \"AI Incidents\"?", "modality": "interpretation" }, "F37": { "kind": "field", "quote": "In aviation, an accident is a case where substantial damage or loss of life occurs. Incidents are cases where the risk of an accident substantially increases.", "source": "B8", "source_label": "AI Incident Database — Defining an “AI Incident”", "locus": "¶1", "modality": "attributed" }, "F11": { "kind": "field", "quote": "As AI technology is integrated into an increasing number of safety-critical systems — entering domains such as transportation, healthcare, and energy — the potential impact of this technology’s failures similarly grows.", "source": "B3", "source_label": "Partnership on AI — AI Incident Database - Partnership on AI", "locus": "Overview ¶1", "modality": "interpretation" }, "F10": { "kind": "field", "quote": "The AI Incident Database (AIID) is a tool designed to help us better imagine and anticipate these risks, collecting more than 1,200 reports of intelligent systems causing safety, fairness, or other real-world problems. … As a central, systematized repository of problems experienced in the real world as a result of AI, this crowdsourced database can help AI practitioners mitigate or avoid repeated bad outcomes in the future.", "source": "B3", "source_label": "Partnership on AI — AI Incident Database - Partnership on AI", "locus": "Workstreams / Safety Critical AI / AI Incident Database, Overview ¶1–2", "modality": "stipulation" }, "F65": { "kind": "field", "quote": "[Submitted on 17 Nov 2020] … Authors:Sean McGregor", "source": "B18", "source_label": "arXiv — [2011.08512] Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database", "locus": "abstract page header", "modality": "documented" }, "F67": { "kind": "field", "quote": "The AI Incident Database is an incident collection initiated by an industrial/non-profit cooperative to enable AI incident avoidance and mitigation.", "source": "B18", "source_label": "arXiv — [2011.08512] Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database", "locus": "Abstract", "modality": "stipulation" }, "F68": { "kind": "field", "quote": "The database supports a variety of research and development use cases with faceted and full text search on more than 1,000 incident reports archived to date.", "source": "B18", "source_label": "arXiv — [2011.08512] Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database", "locus": "Abstract", "modality": "documented" }, "F74": { "kind": "field", "quote": "Avoiding repeated AI failures requires making past failures known. Therefore, today we introduce a systematized collection of incidents where intelligent systems have caused safety, fairness, or other real-world problems: The AI Incident Database (AIID).", "source": "B20", "source_label": "Wikipedia — Partnership on AI", "locus": "reference [10] (McGregor, partnershiponai.org, 2020-11-18), quote", "modality": "attributed" }, "F56": { "kind": "field", "quote": "McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21).", "source": "B15", "source_label": "AI Incident Database — Database Backups and Snapshots", "locus": "Citing the Database as a Whole", "modality": "stipulation" }, "MMRS-19": { "kind": "archive", "quote": "2020 (described in arXiv:2011.08512, submitted 17 November 2020)", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: founded (evidence: arxiv.org/abs/2011.08512, basis third-party)", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-05": { "kind": "archive", "quote": "The founding paper (McGregor, arXiv:2011.08512, 17 November 2020) reported more than 1,000 incident reports at launch.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, What it is s5", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "F4": { "kind": "field", "quote": "The AI Incident Database is a project of the Responsible AI Collaborative, an organization chartered to advance the AI Incident Database.", "source": "B1", "source_label": "AI Incident Database — AI Incident Database", "locus": "The Responsible AI Collaborative ¶1 (also B11, the same paragraph)", "modality": "documented" }, "F14": { "kind": "field", "quote": "Uploaded on Apr 25, 2024 … Developing organisation(s): … responsible ai collaborative", "source": "B4", "source_label": "OECD AI Policy Observatory — AI Incident Database", "locus": "entry header; About the tool: developing organisation", "modality": "documented" }, "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.", "source": "B2", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "What is the AI Incident Tracker? ¶3", "modality": "attributed" }, "F46": { "kind": "field", "quote": "launching the AI Incident Database … Sean McGregor is the Executive Director.", "source": "B11", "source_label": "AI Incident Database — The details about who and why for database - AI Incident Database", "locus": "Emeritus Board: Sean McGregor; Collaborators", "modality": "documented" }, "F47": { "kind": "field", "quote": "Database Founding Sponsor … Jingying Yang and Dr. Christine Custis contributed significantly to the early stages of the AIID via their roles with the Partnership on AI.", "source": "B11", "source_label": "AI Incident Database — The details about who and why for database - AI Incident Database", "locus": "sponsor block; Open Source Contributors", "modality": "documented" }, "MMRS-04": { "kind": "archive", "quote": "It is a project of the Responsible AI Collaborative. Sean McGregor launched it and is its Executive Director; Partnership on AI was the database's founding sponsor.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, What it is s2–4", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-37": { "kind": "archive", "quote": "open submission; incident editors resolve submissions and maintain records", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: operator and intake (evidence: incidentdatabase.ai/about, basis operator)", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "F69": { "kind": "field", "quote": "#REDIRECT [[Partnership on AI]] … {{R from subtopic}} … {{R with possibilities}}", "source": "B19", "source_label": "Wikipedia — AI Incident Database (redirect)", "locus": "raw wikitext of the redirect page (action=raw&redirect=no)", "modality": "documented" }, "F70": { "kind": "field", "quote": "In November 2020 the Partnership on AI announced the AI Incident Database (AIID),[10] which is a tool to identify, assess, manage, and communicate AI risk and harm.", "source": "B20", "source_label": "Wikipedia — Partnership on AI", "locus": "History ¶5", "modality": "documented" }, "F71": { "kind": "field", "quote": "The response also highlighted how the AI Incident Database involves some of the minimum attributes in NIST's AI RMF, such as being consensus-driven, risk-based, adaptable, and consistent with other approaches to managing AI risk.", "source": "B20", "source_label": "Wikipedia — Partnership on AI", "locus": "History ¶6", "modality": "attributed" }, "F72": { "kind": "field", "quote": "Notable initiatives include the establishment of the AI incident Database, formulation of norms for responsible publication, and the creation of the innovative AI learning environment SafeLife.", "source": "B20", "source_label": "Wikipedia — Partnership on AI", "locus": "Mission and Principles ¶5", "modality": "documented" }, "F73": { "kind": "field", "quote": "In November 2020, the Partnership on AI announced the AI Incident Database (AIID) which shifted to a new special-purpose independent non-profit in 2022.", "source": "B20", "source_label": "Wikipedia — Partnership on AI", "locus": "Programs and initiatives ¶6", "modality": "documented" }, "F75": { "kind": "field", "quote": "{{Primary sources|date=April 2025}} … {{AI-generated|date=October 2025}}", "source": "B20", "source_label": "Wikipedia — Partnership on AI", "locus": "article header templates (raw wikitext)", "modality": "documented" }, "F36": { "kind": "field", "quote": "Version 2 · October 1, 2026 · Daniel Atherton and Sean McGregor · Operational working guide … Generative AI tools were used during the development of Version 2 to assist with research, document organization, drafting, revision, and consistency review.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "header; Version 2 note on AI assistance", "modality": "self-description" }, "F20": { "kind": "field", "quote": "An alleged harm or near harm by an AI system that has yet to occur or be detected.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "Quick Definitions: AI issue", "modality": "stipulation" }, "F21": { "kind": "field", "quote": "The AIID editorial record that bounds and summarizes a specific harmful or near-harm event or traceable episode.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "Quick Definitions: Incident ID", "modality": "stipulation" }, "F23": { "kind": "field", "quote": "A report is evidence; an incident ID is the database’s normalized account of an event. A later source about the same underlying event is usually an additional report, not a new incident.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "Quick Definitions: fast boundary rule", "modality": "stipulation" }, "F24": { "kind": "field", "quote": "A distinct incident involving the same intelligent system, substantially similar causative factors, and substantially similar harms as a known incident.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "Quick Definitions: AI incident variant", "modality": "stipulation" }, "MMRS-07": { "kind": "archive", "quote": "A distinct incident involving the same system, with substantially similar causes and harms, is recorded as a variant.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, How it works ¶1 s3", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "F62": { "kind": "field", "quote": "A \"variant\" is an AI incident similar to a known case—it has the same causes, harms, and AI system. Instead of listing it separately, we group it under the first reported incident. Unlike other incidents, variants do not need to have been reported outside the AIID.", "source": "B16", "source_label": "AI Incident Database — Incident 693", "locus": "Variants", "modality": "stipulation" }, "CAP-1-05": { "kind": "archive", "quote": "WHAT IS NOT GIVEN. The incident as the unit, the reports attached to it, and the citation page /cite/N are absent; 'collects public and crowdsourced reports' stands in for the unit.", "deposit": null, "source_label": "Capture Registry observation OBS-d2d81aafd207 (ai-incident-database-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/ai-incident-database-aio-20261029/", "locus": "ai-incident-database-aio-20261029, findings[1]", "modality": "documented", "date": "2026-10-29" }, "CAP-2-03": { "kind": "archive", "quote": "THE DEFINITION EXACT. The incident, the issue and the variant are the Editor's Guide's definitions (§2.1) in substance, the first verbatim.", "deposit": null, "source_label": "Capture Registry observation OBS-bd807797964a (what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029/", "locus": "what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029, findings[0]", "modality": "documented", "date": "2026-10-29" }, "CAP-2-05": { "kind": "archive", "quote": "THE REPORT ABSENT AGAIN. As at the database's name, the report as the source attached to an incident is not stated; the unit is the incident alone.", "deposit": null, "source_label": "Capture Registry observation OBS-bd807797964a (what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029/", "locus": "what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029, findings[2]", "modality": "documented", "date": "2026-10-29" }, "MMRS-24": { "kind": "archive", "quote": "NOT a list of research papers: its unit is the event, and papers enter as reports", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: negative_tags", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F26": { "kind": "field", "quote": "1. IS THERE A SPECIFIC EVENT? … 2. IS AN AI SYSTEM IDENTIFIABLE? … 3. IS THE AI SYSTEM CAUSALLY IMPLICATED? … 4. IS THERE HARM OR CREDIBLE NEAR HARM? … 5. IS THE EVIDENCE ADEQUATE?", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§3.1 The Five-Question Test", "modality": "stipulation" }, "F28": { "kind": "field", "quote": "An AI system is implicated when it played an important role in the chain of events that produced the alleged harm. It need not be the sole or dominant cause.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§2.3 Implication and Causality ¶1", "modality": "stipulation" }, "F29": { "kind": "field", "quote": "A near-harm event exists when the system helped create a chain of events that could readily have produced harm but an external factor prevented the outcome. The external factor must be independent of the system and should not simply be a built-in safeguard functioning as designed.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§2.4 Harm and Near Harm ¶2", "modality": "stipulation" }, "F27": { "kind": "field", "quote": "At least one source must support the essential event, and uncertainty must be expressible without turning the record into speculation.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§3.1, question 5", "modality": "stipulation" }, "MMRS-08": { "kind": "archive", "quote": "A candidate passes a five-question test: a specific event, an identifiable AI system, a causal AI role, harm or credible near harm, and adequate evidence.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, How it works ¶2 s1", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-26": { "kind": "archive", "quote": "yes: harm or credible near harm (five-question test)", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: properties.admission_requires_harm (evidence: editors-guide, basis operator)", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "CAP-2-01": { "kind": "archive", "quote": "At the question of its unit the Overview reads the database's own guide and keeps the guide's distinctions, with one compression: the five-part admission test arrives as three parts, and the evidence requirement is gone.", "deposit": null, "source_label": "Capture Registry observation OBS-bd807797964a (what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029), field analysis", "record": "https://www.alexanarch.org/captures/what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029/", "locus": "what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029, analysis s1", "modality": "interpretation", "date": "2026-10-29" }, "CAP-2-04": { "kind": "archive", "quote": "FIVE QUESTIONS MADE THREE. The guide's test asks five things: a specific event, an identifiable AI system, a causal AI role, harm or credible near harm, and adequate evidence (§3). The composition gives three thresholds, folding the identifiable system into 'AI implication' and dropping adequate evidence.", "deposit": null, "source_label": "Capture Registry observation OBS-bd807797964a (what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029/", "locus": "what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029, findings[1]", "modality": "documented", "date": "2026-10-29" }, "CAP-2-02": { "kind": "archive", "quote": "The record's 'what its selection leaves out' rests on that test; the composition carries the test's harm condition and loses its evidence condition.", "deposit": null, "source_label": "Capture Registry observation OBS-bd807797964a (what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029), field analysis", "record": "https://www.alexanarch.org/captures/what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029/", "locus": "what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029, analysis s2", "modality": "interpretation", "date": "2026-10-29" }, "F38": { "kind": "field", "quote": "this database adopts an adaptive criteria for ingesting \"incidents\" where reports are accepted or rejected on the basis of a growing rule set defined in the Editor's Guide.", "source": "B8", "source_label": "AI Incident Database — Defining an “AI Incident”", "locus": "¶2", "modality": "stipulation" }, "F43": { "kind": "field", "quote": "When in doubt of whether an event qualifies as an incident, please submit it! This project is intended to converge on a shared criteria for ingesting incidents through exploration of the candidate incidents submitted by the broader community.", "source": "B11", "source_label": "AI Incident Database — The details about who and why for database - AI Incident Database", "locus": "When Should You Report an Incident?", "modality": "stipulation" }, "F42": { "kind": "field", "quote": "The initial set of more than 1,000 incident reports have been intentionally broad in nature. Current examples include, … An autonomous car kills a pedestrian", "source": "B11", "source_label": "AI Incident Database — The details about who and why for database - AI Incident Database", "locus": "What is an Incident?", "modality": "documented" }, "F30": { "kind": "field", "quote": "General criticism of a product or technology class without identified harm. … Benchmark failures, model errors, or hallucinations that remain inside testing or private drafting and have no consequential use. … These are presumptions rather than absolute prohibitions.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§3.3 Presumptive Exclusions", "modality": "stipulation" }, "MMRS-12": { "kind": "archive", "quote": "Its presumptive exclusions include \"benchmark failures that stay in testing\" and \"general criticism of a technology.\"", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, What its selection leaves out s1", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-09": { "kind": "archive", "quote": "Anyone may submit. The incident editors resolve submissions.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, How it works ¶2 s2–3", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F3": { "kind": "field", "quote": "You are invited to submit incident reports, whereupon submissions will be indexed and made discoverable to the world.", "source": "B1", "source_label": "AI Incident Database — AI Incident Database", "locus": "About the Database ¶2", "modality": "stipulation" }, "F45": { "kind": "field", "quote": "Incident Editors: People that resolve incident submissions to the database and maintain them. … Daniel Atherton is the lead AI incidents editor.", "source": "B11", "source_label": "AI Incident Database — The details about who and why for database - AI Incident Database", "locus": "Collaborators: Incident Editors; lead editor", "modality": "documented" }, "F44": { "kind": "field", "quote": "The incident database is managed in a participatory manner by persons and organizations contributing code, research, and broader impacts.", "source": "B11", "source_label": "AI Incident Database — The details about who and why for database - AI Incident Database", "locus": "Board of Directors ¶1", "modality": "self-description" }, "F48": { "kind": "field", "quote": "Taxonomy Editors: Organizations or people that have contributed taxonomies to the database. … Center for Security and Emerging Technology (CSET) … Nikiforos Pittaras (GMF)", "source": "B11", "source_label": "AI Incident Database — The details about who and why for database - AI Incident Database", "locus": "Collaborators: Taxonomy Editors", "modality": "documented" }, "F55": { "kind": "field", "quote": "Every incident has its own suggested citation that credits both the submitter(s) of the incident and the editor(s) of the incident. … You can find incident citations at https://incidentdatabase.ai/cite/INSERT_NUMBER_HERE.", "source": "B15", "source_label": "AI Incident Database — Database Backups and Snapshots", "locus": "Citing a Specific Incident", "modality": "stipulation" }, "MMRS-16": { "kind": "archive", "quote": "Its citation page per incident is the model this meta-aggregator follows.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, Place in the field s1", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-21": { "kind": "archive", "quote": "taxonomy classifications (CSET, GMF)", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: item_fields (evidence: incidentdatabase.ai/cite/693, basis observed)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-10": { "kind": "archive", "quote": "A duplicate is merged into the earlier record, and its citation page stays up and points there (Incident 637 now reads as a duplicate of 112).", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, How it works ¶2 s4", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F64": { "kind": "field", "quote": "This incident is a duplicate of Incident 112. All new reports and citations should be directed to incident 112. The reports previously found on this page have been migrated to the previously existing incident.", "source": "B17", "source_label": "AI Incident Database — Citation record for Incident 637", "locus": "Citation record for Incident 637", "modality": "documented" }, "F31": { "kind": "field", "quote": "A retired entity or incident identifier redirected to the canonical record that replaced it.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§1.1 Core Record Types: Duplicate mapping", "modality": "stipulation" }, "MMRS-20": { "kind": "archive", "quote": "ids include merged duplicates (e.g. 637, merged into 112), so the id is an upper bound on incidents", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: count (evidence: incidentdatabase.ai, basis observed)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F5": { "kind": "field", "quote": "Incident 1788:", "source": "B1", "source_label": "AI Incident Database — AI Incident Database", "locus": "incident list (the highest id on the page, 2026-10-29)", "modality": "documented" }, "F51": { "kind": "field", "quote": "Inspired by the CVE program and other incident reporting practices, the AI Incident Database (AIID) catalogs AI incidents with the goal of reducing the likelihood that similar incidents recur … Currently there are over 750 distinct AI incidents detailed by more than 3000 indexed third-party reports.", "source": "B12", "source_label": "The Association for the Advancement of Artificial Intelligence — Lessons for Editors of AI Incidents from the AI Incident Database - AAAI Publications", "locus": "PDF, 'The AI Incident Database and Related Work'", "modality": "documented" }, "F54": { "kind": "field", "quote": "Weekly point-in-time dumps of the full database, available in JSON, MongoDB archive, and CSV formats. … 2026-10-26 10:15 AM · 121.49 MB · backup-20261026101519.tar.bz2 … 2021-03-29 07:49 AM · 2.49 MB · backup-20210329074910.tar.bz2", "source": "B15", "source_label": "AI Incident Database — Database Backups and Snapshots", "locus": "Snapshot Downloads", "modality": "documented" }, "F57": { "kind": "field", "quote": "An Excel file, updated weekly, that combines all numbered incidents with many of their taxonomy classifications.", "source": "B15", "source_label": "AI Incident Database — Database Backups and Snapshots", "locus": "Excel Export", "modality": "documented" }, "MMRS-11": { "kind": "archive", "quote": "The whole database is offered as a weekly snapshot.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, How it works ¶2 s5", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-22": { "kind": "archive", "quote": "weekly full snapshots (JSON, MongoDB archive, CSV; 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relations are typed; 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: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: schema_note (v2, 2026-10-28)", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-31": { "kind": "archive", "quote": "active", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: lifecycle.state; observed.on", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-32": { "kind": "archive", "quote": "data licence beyond the snapshots page", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: unchecked", "modality": "self-description", "date": "2026-10-28 (corrected 2026-10-29)" }, "F13": { "kind": "field", "quote": "Usage rights: … Open source/Permissive … License: … Apache 2.0", "source": "B4", "source_label": "OECD AI Policy Observatory — AI Incident Database", "locus": "About the tool: usage rights, licence", "modality": "documented" }, "MMRS-23": { "kind": "archive", "quote": "code open source; no data licence stated on the snapshots page", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: license (evidence: incidentdatabase.ai/research/snapshots, basis observed)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "CAP-1-07": { "kind": "archive", "quote": "The database's code is open on GitHub (its about page); the record states no data licence on the snapshots page, and the composition's 'free, open-source' does not distinguish the two.", "deposit": null, "source_label": "Capture Registry observation OBS-d2d81aafd207 (ai-incident-database-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/ai-incident-database-aio-20261029/", "locus": "ai-incident-database-aio-20261029, findings[3]", "modality": "interpretation", "date": "2026-10-29" }, "F25": { "kind": "field", "quote": "The database records publicly documented allegations of harm or near harm. It does not independently adjudicate every factual claim.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "header: governing principle", "modality": "self-description" }, "F32": { "kind": "field", "quote": "The AIID is a curated archive of publicly documented incidents, issues, reports, and responses. … It is not a neutral mirror of all AI harms, a comprehensive denominator for risk rates, or an independent adjudication of every event.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§11.1 What the Database Represents", "modality": "self-description" }, "F33": { "kind": "field", "quote": "Wealthy countries, prominent companies, spectacular events, and English-language reporting are likely to be overrepresented. Harms affecting marginalized groups, occurring inside private organizations, or unfolding as ordinary administration may be underrepresented.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§11.2 Coverage and Selection Effects", "modality": "self-description" }, "F34": { "kind": "field", "quote": "A lack of records is not evidence that a sector or country has no incidents.", "source": "B7", "source_label": "AI Incident Database — Editorial and Research Guide for the AI Incident Database", "locus": "§11.3 Epistemic Status", "modality": "self-description" }, "F50": { "kind": "field", "quote": "We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable.", "source": "B12", "source_label": "The Association for the Advancement of Artificial Intelligence — Lessons for Editors of AI Incidents from the AI Incident Database - AAAI Publications", "locus": "Abstract (article page)", "modality": "interpretation" }, "F9": { "kind": "field", "quote": "As the reporting is voluntary, the dataset is inevitably subject to some degree of sampling bias. … a systematic validation study has not yet been completed.", "source": "B2", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "Limitations of the analysis and dataset", "modality": "self-description" }, "MMRS-13": { "kind": "archive", "quote": "A measured study enters as a report when it documents an event.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, What its selection leaves out s2", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-14": { "kind": "archive", "quote": "What an answer surface does with a source on an ordinary day, with no harm event, has no unit here: attribution erasure, citation fidelity and provenance stripping enter when they attach to a harm.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, What its selection leaves out s3", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-29": { "kind": "archive", "quote": "no field for it: attribution failure enters when it attaches to a harm event", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: properties.records_attribution (evidence: editors-guide, basis reading)", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-28": { "kind": "archive", "quote": "unknown: reports are linked; whether their texts are archived was not read", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: properties.preserves_original_outputs (basis reading)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "R543-01": { "kind": "archive", "quote": "| AI Ethics glossaries (AIAAIC, AI Incident DB) | Semantic Liquidation, Ghost Governance, Taxonomic Violence | Definition + citation |", "deposit": 543, "source_label": "#543 HEXAGONAL LEXICAL ENGINE v1.2 Core 50 · Discovery Lattice · Deployment Map · Governing Laws", "record": "https://www.alexanarch.org/s/records/543/", "locus": "Phase 4: Scholarly Venue Targeting, table row 1", "modality": "stipulation", "date": "2026-03-10" }, "F58": { "kind": "field", "quote": "Incident 693: Google AI Reportedly Delivering Confidently Incorrect and Harmful Information … Google's AI search engine has reportedly been providing users with confidently incorrect and often harmful information. … Alleged: Google developed and deployed an AI system, which harmed Google users and General public.", "source": "B16", "source_label": "AI Incident Database — Incident 693", "locus": "title; Description; Entities", "modality": "attributed" }, "F59": { "kind": "field", "quote": "Report Count … Incident Date … 2024-05-14 … Editors … Daniel Atherton", "source": "B16", "source_label": "AI Incident Database — Incident 693", "locus": "Incident Stats", "modality": "documented" }, "F60": { "kind": "field", "quote": "Google's new search feature, AI Overviews, seems to be going awry.", "source": "B16", "source_label": "AI Incident Database — Incident 693", "locus": "Incident Reports (Business Insider report excerpt)", "modality": "attributed" }, "F61": { "kind": "field", "quote": "MIT Taxonomy Classifications … Machine-Classified … 3.1. False or misleading information", "source": "B16", "source_label": "AI Incident Database — Incident 693", "locus": "Applied Taxonomies: MIT", "modality": "documented" }, "MMRS-15": { "kind": "archive", "quote": "Incident 693, Google's AI Overviews of May 2024, is the field's index case in the database, with seven reports.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, What its selection leaves out s4", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-36": { "kind": "archive", "quote": "alleged developer and deployer Google; 7 reports; the AI Overviews launch errors (glue on pizza, the Obama claim)", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: relations (holding, incidentdatabase.ai/cite/693), note and dates", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F6": { "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": "B2", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "opening ('How is AI harming us?')", "modality": "documented" }, "F7": { "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": "B2", "source_label": "MIT AI Risk Initiative — MIT AI Incident Tracker", "locus": "What is the AI Incident Tracker? ¶1", "modality": "documented" }, "MMRS-34": { "kind": "archive", "quote": "classifies AIID reports", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: relations (used_by mit-ai-incident-tracker)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-18": { "kind": "archive", "quote": "The MIT AI Risk Repository aggregates risk frameworks over the same ground and keeps its own incident tracker.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, Place in the field s3", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-35": { "kind": "archive", "quote": "monitoring", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: roles", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-17": { "kind": "archive", "quote": "Its neighbours in the harm basin are the OECD AI Incidents and Hazards Monitor, which is automated and news-derived, and the AIAAIC Repository.", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article, Place in the field s2", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F63": { "kind": "field", "quote": "The OECD AI Incidents and Hazards Monitor (AIM) automatically collects and classifies AI-related incidents and hazards in real time from reputable news sources worldwide.", "source": "B16", "source_label": "AI Incident Database — Incident 693", "locus": "notice above Entities", "modality": "attributed" }, "F15": { "kind": "field", "quote": "32 verified incidents · 29 companies · 13 countries · 24 columns — Every major AI failure, documented … License: CC BY-SA 4.0", "source": "B5", "source_label": "Kaggle — AI Incidents, Failures & Controversies Database - Kaggle", "locus": "title, subtitle and description (Kaggle API dataset view)", "modality": "documented" }, "F17": { "kind": "field", "quote": "| **AI Incident Database (incidentdatabase.ai)** | Primary incident records and AIID identifiers |", "source": "B5", "source_label": "Kaggle — AI Incidents, Failures & Controversies Database - Kaggle", "locus": "Data Sources table", "modality": "documented" }, "F16": { "kind": "field", "quote": "AI incidents surged **56.4% in 2024 alone** (Stanford HAI AI Index 2025).", "source": "B5", "source_label": "Kaggle — AI Incidents, Failures & Controversies Database - Kaggle", "locus": "Why This Dataset?", "modality": "attributed" }, "MMRS-25": { "kind": "archive", "quote": "AI Incidents, Failures & Controversies Database (Kaggle)", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: disambiguation (label)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "CAP-1-06": { "kind": "archive", "quote": "A HOMONYM IN THE CARDS. 'AI Incidents, Failures & Controversies Database' on Kaggle (32 incidents, 29 companies, 13 countries) is another dataset with a near name; the composition does not use it, and the card sits beside the database's own.", "deposit": null, "source_label": "Capture Registry observation OBS-d2d81aafd207 (ai-incident-database-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/ai-incident-database-aio-20261029/", "locus": "ai-incident-database-aio-20261029, findings[2]", "modality": "documented", "date": "2026-10-29" }, "F52": { "kind": "field", "quote": "Users can submit examples of incidents and controversies; these submissions are included based on adherence to criteria such as relevance, fairness, and accurac...", "source": "B13", "source_label": "ACM Digital Library — Why We Need to Know More: Exploring the State of AI Incident Documentation ...", "locus": "card snippet (ACM DL returned 403)", "modality": "attributed" }, "F18": { "kind": "field", "quote": "AI Incident Database | TIME posted on the topic", "source": "B6", "source_label": "LinkedIn·TIME — AI Incident Database | TIME posted on the topic", "locus": "card title (video, 1:35; not found, title only)", "modality": "documented" }, "F39": { "kind": "field", "quote": "AI Incidents Rise 50% Year-Over-Year: AI Incident Database | TIME posted on the ...", "source": "B9", "source_label": "LinkedIn·TIME — AI Incidents Rise 50% Year-Over-Year: AI Incident Database | TIME posted on the ...", "locus": "card title (video, 1:35; not found, title only)", "modality": "attributed" }, "F40": { "kind": "field", "quote": "Examples of AI real attacks", "source": "B10", "source_label": "YouTube·HarrietHacks — Examples of AI real attacks", "locus": "oEmbed title (YouTube, HarrietHacks, 6:59)", "modality": "documented" }, "F53": { "kind": "field", "quote": "Establishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell | IASEAI 2025", "source": "B14", "source_label": "YouTube·International Association for Safe & Ethical AI — Establishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell", "locus": "oEmbed title (YouTube, International Association for Safe & Ethical AI, 19m)", "modality": "documented" }, "CAP-1-01": { "kind": "archive", "quote": "The Overview composes the database at the grain of its own about page and keeps it apart from its neighbours.", "deposit": null, "source_label": "Capture Registry observation OBS-d2d81aafd207 (ai-incident-database-aio-20261029), field analysis", "record": "https://www.alexanarch.org/captures/ai-incident-database-aio-20261029/", "locus": "ai-incident-database-aio-20261029, analysis s1", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-04": { "kind": "archive", "quote": "THE ENTITY HELD. Operator (the Responsible AI Collaborative), purpose ('harms, near-harms'), the aviation model and the MIT tracker as a derived layer are the record's fields", "deposit": null, "source_label": "Capture Registry observation OBS-d2d81aafd207 (ai-incident-database-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/ai-incident-database-aio-20261029/", "locus": "ai-incident-database-aio-20261029, findings[0]", "modality": "documented", "date": "2026-10-29" }, "CAP-1-02": { "kind": "archive", "quote": "It gives the operator, the model and the derived layer, and omits the incident-and-report structure that makes the database what it is.", "deposit": null, "source_label": "Capture Registry observation OBS-d2d81aafd207 (ai-incident-database-aio-20261029), field analysis", "record": "https://www.alexanarch.org/captures/ai-incident-database-aio-20261029/", "locus": "ai-incident-database-aio-20261029, analysis s2", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-03": { "kind": "archive", "quote": "This is the baseline for the meta-aggregator's record, which was not yet indexed: no card is machinemediation.org.", "deposit": null, "source_label": "Capture Registry observation OBS-d2d81aafd207 (ai-incident-database-aio-20261029), field analysis", "record": "https://www.alexanarch.org/captures/ai-incident-database-aio-20261029/", "locus": "ai-incident-database-aio-20261029, analysis s3", "modality": "documented", "date": "2026-10-29" }, "MMRS-33": { "kind": "archive", "quote": "unit stated as the incident, with reports attached", "deposit": null, "source_label": "MMRS meta-aggregator record: AI Incident Database (census row, schema 2.2)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "census: battery.checks", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" } }, "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. The compression, the field-only arm, the transcript, the delta and the kernel are in the row (links.row), which is the authority." }, "source": "operator, 2026-10-29 00:00 EDT: 'gnosis is one way to build a /non entity. we can simply build them here as against their wikipedia articles if they exist, their own pages, etc. ill run captures if you want, just list the semantic addresses'; the MMRS meta-aggregator (machinemediation.org/aggregators/) entity", "stage": "composed 2026-10-29 under EA-NEGONT-02 v0.9, #1690, equal case, ρ = extends (draft, not frozen; ledgers unaudited, one extractor; quotes re-checked verbatim by the session against the saved pages, the Capture Registry and the MMRS record)", "date": "2026-10-29", "status": { "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the MMRS record and the two capture records; archive selected by D/R/O (one deposit of one candidate admitted, #543 at R; hop of 41 read and rejected; O-class matched nothing), with the MMRS record (37 claims) and the Capture Registry's findings and analysis (12 claims) ledgered as the archive's records of the entity; the field B by three origins (14 card sources for 15 cards, 4 own pages, 2 Wikipedia pages); the composer read the ledgers, never the transcripts, except where the brief asks for T's claims and the delta", "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 75/75 verified by script against saved text, PDF text, raw wikitext, oEmbed title or card text; archive quotes 50/50 verified verbatim against the registry, census.json, articles.py and AXN-0174; carried as marked: MMRS-01 contested (F73), MMRS-12 and MMRS-19 and CAP-1-04 with defects noted", "frozen": false, "breaches": [] }, "addresses": [ { "address": "ai incident database", "address_url": "https://www.alexanarch.org/addresses/ai-incident-database/", "register": [] }, { "address": "what counts as an incident in the ai incident database", "address_url": "https://www.alexanarch.org/addresses/ai-incident-database/", "register": [] } ], "counts": { "ko_sentences": 29, "p_words": 336, "field_claims": 75, "archive_claims": 50, "ko_field_claims_cited": 75, "ko_archive_claims_cited": 50, "admitted_deposits": 1, "register_addresses": 0, "traversal": { "D_sentences": 1, "D_deposits": 1, "R_sentences": 1, "R_deposits": 1, "O_direct": 1, "O_class": 0, "O_deposits": 1, "H_candidates": 41 } }, "links": { "record": "https://www.alexanarch.org/non/ai-incident-database/", "table_of_contents": "https://www.alexanarch.org/non/index.json", "register": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/register.json", "row": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/rows/ai-incident-database.json", "archive_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/ai-incident-database/ledger-archive.json", "field_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/ai-incident-database/field.json", "selection": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/ai-incident-database/", "traversal": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/ai-incident-database/", "traversal_summary": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/ai-incident-database/summary.json", "reading": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/ai-incident-database/reading.json", "configuration": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/panel/configs/ai-incident-database.json", "address_page": "https://www.alexanarch.org/addresses/ai-incident-database/", "mmrs_record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "dataset": "https://huggingface.co/datasets/leesharks/negative-of-the-negative" }, "row_sha256": "19200a40cf2836b2da3a1cc7c06ef4a6aae02fb68696d300b528729d9243a46d", "record_url": "https://www.alexanarch.org/non/ai-incident-database/", "json_url": "https://www.alexanarch.org/non/ai-incident-database/entity.json", "spec": "https://www.alexanarch.org/s/records/1690/", "creator": "Lee Sharks", "orcid": "0009-0000-1599-0703", "license": "CC BY 4.0" }