{
 "entity": "oecd-ai-incidents-monitor",
 "name": "OECD AI Incidents and Hazards Monitor",
 "axn": {
  "axn": "AXN:0AC6.NON.📜🌍💙🪞🎯⚪",
  "root": "AXN:0AC6.NON",
  "hex": "0AC6",
  "family": "NON",
  "row_sha256": "400ecb5955c79de278ae3d89593788a80a0009f765a266c17f9d1869dcf565b5",
  "minted": "2026-10-29",
  "resolver": "https://www.alexanarch.org/s/axn/0AC6/",
  "earlier_versions": []
 },
 "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/oecd-ai-incidents-monitor.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 capture record; archive selected by D/R/O (one deposit of two candidates admitted, #637 at O-class; #170 a homonym on the acronym; hop of 11 read and rejected; the class 'AI incident reporting and monitoring' matched nothing), with the MMRS record (31 claims) and the Capture Registry's findings and analysis (8 claims) ledgered as the archive's records of the entity; the field B by three origins (6 card sources for 6 cards, 1 own page beyond them, 2 Wikipedia records); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta",
  "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 64/64 verified by script against saved text, PDF text, oEmbed title or card snippet; archive quotes 40/40 verified verbatim against the registry, census.json, articles.py and AXN-01ED; carried as marked: F32, F33 internally_inconsistent",
  "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. Quotes are free: POST https://gnosis.spxi.dev/v1/quote with {oldid, tier: \"commission\"} or {object: {name, …}}."
 },
 "spxi_instrument": {
  "method": "0.16",
  "name": "The Negative of the Negative (/non)",
  "level": 0,
  "level_name": "Measurement",
  "url": "https://www.spxi.dev/methods/#m-0-16",
  "inventory": "https://www.spxi.dev/methods/",
  "statement": "SPXI instrument: method 0.16 of 87, level 0 (Measurement), The Negative of the Negative (/non).",
  "also": []
 },
 "citation": "Sharks, Lee. \"OECD AI Incidents and Hazards Monitor — The Negative of the Negative\" [AXN:0AC6.NON.📜🌍💙🪞🎯⚪], 2026-10-29. The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/oecd-ai-incidents-monitor/",
 "research": {
  "google": "https://www.google.com/search?q=OECD+AI+Incidents+and+Hazards+Monitor",
  "google_non": "https://www.google.com/search?q=%22OECD+AI+Incidents+and+Hazards+Monitor+%E2%80%94+The+Negative+of+the+Negative%22",
  "chatgpt": "https://chatgpt.com/?q=What+is+OECD+AI+Incidents+and+Hazards+Monitor%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.",
  "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9COECD+AI+Incidents+and+Hazards+Monitor+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+OECD+AI+Incidents+and+Hazards+Monitor%3F+Ground+your+answer+in+a+reading+of+the+entry+itself."
 },
 "entry": {
  "composed": true,
  "title": "The OECD AI Incidents and Hazards Monitor",
  "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": "OECD AI Incidents and Hazards Monitor",
   "lede": "is an automated monitor of AI harm events in the news, run by OECD.AI (MMRS record), \"Automated media discourse monitor of AI incidents and hazards (Beta)\" (its page): since November 2024 language models classify its events and write each record (methodology page)."
  },
  "sentences": [
   {
    "n": 1,
    "para": 0,
    "text": "The OECD AI Incidents and Hazards Monitor (AIM) is an automated monitor of AI harm events in the news, run by OECD.AI, whose census gives its kind as a monitor (MMRS record); its own page calls it an \"Automated media discourse monitor of AI incidents and hazards (Beta)\", which the MMRS record quotes, and the OECD's trends paper of February 2026 describes it as tracking media-reported AI incidents and hazards \"in real-time\" and leveraging the OECD common reporting framework to categorise them consistently.",
    "claims": [
     "MMRS-01",
     "MMRS-19",
     "F1",
     "MMRS-03",
     "F38"
    ]
   },
   {
    "n": 2,
    "para": 0,
    "text": "Its methodology page records that it was previously the AI Incidents Monitor, the name the MMRS record gives beside AIM, and that its use is subject to the OECD's terms and conditions.",
    "claims": [
     "F22",
     "MMRS-17"
    ]
   },
   {
    "n": 3,
    "para": 0,
    "text": "The methodology page says it \"was initiated and is being developed by the OECD.AI expert group on AI incidents with the support of the Patrick J. McGovern Foundation\"; the OECD's definitions paper dates the group to January 2023, when the OECD formalised it to develop a common AI incident reporting framework and an AI Incidents Monitor, and the MMRS record names OECD.AI as operator, on the methodology page's evidence.",
    "claims": [
     "F11",
     "F50",
     "MMRS-18"
    ]
   },
   {
    "n": 4,
    "para": 0,
    "text": "Its own page says it documents incidents and hazards so that policymakers, AI practitioners and all stakeholders may gain insight into the risks and harms of AI systems, and that over time it will help show risk patterns and a collective understanding of them; the methodology page sets its goal as tracking incidents and hazards \"as defined by the OECD, in real time\" to provide the evidence base for policy discussion, and the OECD's topic page, read by its card's snippet only, says all actors must monitor incidents and hazards and that risk mitigation will require a solid evidence base.",
    "claims": [
     "F2",
     "F3",
     "F12",
     "F10"
    ]
   },
   {
    "n": 5,
    "para": 1,
    "text": "Its terms are the OECD's: the paper Defining AI incidents and related terms (OECD Artificial Intelligence Papers No. 16, May 2024), written by Karine Perset and Luis Aranda, approved by the Digital Policy Committee on 14 March 2024 and built on a stocktaking of more than 30 frameworks, standards and legal instruments, provides them to inform the common reporting framework and \"its application through the OECD AI Incidents Monitor (AIM)\", and the methodology page and the MMRS record both source the monitor's definitions to it.",
    "claims": [
     "F48",
     "F49",
     "F59",
     "F18",
     "MMRS-05"
    ]
   },
   {
    "n": 6,
    "para": 1,
    "text": "An AI incident is \"an event, circumstance or series of events where the development, use or malfunction of one or more AI systems directly or indirectly leads to\" harm to the health of a person or groups, disruption of critical infrastructure, violations of human rights or breaches of law protecting fundamental, labour and intellectual property rights, or harm to property, communities or the environment (the paper; restated on the methodology page), and an AI hazard is such an event that \"could plausibly lead to an AI incident\", near misses included (the paper; the methodology page); the MMRS record gives the two terms in brief, the hazard in the paper's words.",
    "claims": [
     "F51",
     "F19",
     "F52",
     "F20",
     "F53",
     "MMRS-04"
    ]
   },
   {
    "n": 7,
    "para": 1,
    "text": "The paper places reputational harm to individuals, hate speech and mis- and disinformation under (c), as a breach of fundamental rights, and violations of authors' and personal rights there too, with industrial property under (d); the definitions rest on the OECD Recommendation's definition of an AI system (2019, revised 2024), as the methodology page states.",
    "claims": [
     "F56",
     "F21"
    ]
   },
   {
    "n": 8,
    "para": 1,
    "text": "The paper also grades the terms, serious AI incidents a subset \"intended to include substantial, material incidents\", an AI disaster a serious incident that may test or exceed a society's capacity to cope, and serious AI hazards a subset of hazards, and leaves seriousness context-dependent and \"left for further discussion\"; its governing limit is that the definitions aim at interoperability \"while providing flexibility for jurisdictions to determine the scope of AI incidents and hazards they wish to address\".",
    "claims": [
     "F54",
     "F55",
     "F58",
     "F57"
    ]
   },
   {
    "n": 9,
    "para": 2,
    "text": "Its unit is the event: an incident or hazard \"can be reported by one or more news articles covering the same event\", its own page notes, its data processing powered by Microsoft Azure using data from Event Registry, and the MMRS record's census gives the unit as an incident or hazard, an event in which an AI system leads, or could plausibly lead, to harm.",
    "claims": [
     "F6",
     "MMRS-20"
    ]
   },
   {
    "n": 10,
    "para": 2,
    "text": "Its intake is the news: the methodology page says the monitor detects incidents and hazards \"from reputable international news outlets\", with data from Event Registry, a news intelligence platform \"processing over 150 000 news articles daily\", whose events are clusters of articles on one happening tagged with concepts, only events with AI concepts reaching the monitor; the trends paper adds that the data rely on the top 50% of media outlets by Alexa traffic ranking, and the MMRS record gives the same clustering and tagging and reads admission as an event entering when reputable outlets report it.",
    "claims": [
     "F13",
     "F14",
     "F39",
     "MMRS-06",
     "MMRS-10"
    ]
   },
   {
    "n": 11,
    "para": 3,
    "text": "Since November 2024 its classifiers are large language models (methodology page): from Event Registry's event summary, OpenAI's GPT-4o mini filters out events that seem unrelated and GPT-4o reclassifies the rest as incidents or hazards, each article is then classified from its own content and the event takes the most common label among its articles, and items collected before November 2024, classified then \"using traditional machine learning techniques\", are being re-annotated with the same method.",
    "claims": [
     "F25",
     "F26",
     "F27",
     "F34"
    ]
   },
   {
    "n": 12,
    "para": 3,
    "text": "Similar events are grouped through the ten nearest summaries by cosine similarity, o3-mini deciding whether they cluster, and the pipeline runs daily on events one to four days old, articles later than that being \"likely to be misclassified as a separate event\" (methodology page).",
    "claims": [
     "F30",
     "F31"
    ]
   },
   {
    "n": 13,
    "para": 3,
    "text": "The MMRS record gives the same pipeline: AI-tagged news events read from Event Registry daily and, since November 2024, large language models that sort them into incidents and hazards and write their summaries (GPT-4o mini filtering, GPT-4o reclassifying, o3-mini writing the title, summary, harm type, severity, stakeholders and country from the top three articles), new items matched to existing events by embedding similarity, its census intake \"automated\" and its cadence daily.",
    "claims": [
     "MMRS-02",
     "MMRS-07",
     "MMRS-08",
     "MMRS-21",
     "MMRS-09",
     "MMRS-23"
    ]
   },
   {
    "n": 14,
    "para": 4,
    "text": "Each record's title, summary and metadata are written by a model: the methodology page says the title, summary, harm type, severity, affected stakeholders and country are \"LLM-generated (OpenAI’s o3-mini) from the top three articles of each event\", the articles chosen by their outlets' ranking in their country and the metadata generated once, when the item is added, and on the monitor's own page its summaries and the explanations of its labels are marked \"[AI generated]\", some explanations ruling out further classes by name (\"It is not Complementary Information … nor is it Beneficial Use or Unrelated\").",
    "claims": [
     "F28",
     "F29",
     "F8",
     "F9"
    ]
   },
   {
    "n": 15,
    "para": 4,
    "text": "The MMRS record draws the consequence: the record does not preserve the original outputs, its titles, summaries and metadata LLM-written from the top articles, so \"the monitor is itself a machine reception of the news: its summaries are compositions, and the articles are their sources\"; the Capture Registry (2026-10-29) sets this reading against the composition layer's, which reads the monitor \"as an aggregator of the news\".",
    "claims": [
     "MMRS-25",
     "MMRS-12",
     "CAP-1-03"
    ]
   },
   {
    "n": 16,
    "para": 5,
    "text": "Its page listed \"About 18579 incidents & hazards\" on 29 October 2026, the count the MMRS record observed on 28 October; the methodology page gives two daily rates, about 1,000 events analysed a day \"resulting in around 10 AI incidents or hazards\" and, in its table's note, \"on average 30 incidents and hazards per day\".",
    "claims": [
     "F7",
     "MMRS-22",
     "F32",
     "F33"
    ]
   },
   {
    "n": 17,
    "para": 5,
    "text": "Its page says incidents \"seem to be getting more media attention lately\" but have gone down as a share of all AI news, and the trends paper counts 92 a month in 2022 rising to 324 in 2025 while their share of AI news fell from 3.2% to around 2.5%.",
    "claims": [
     "F4",
     "F40"
    ]
   },
   {
    "n": 18,
    "para": 6,
    "text": "Its limits are stated by its sources: the methodology page recognises that these publicly reported incidents and hazards likely \"only represent a subset of all AI incidents and hazards worldwide\", a starting point for an evidence base, and the trends paper that its findings \"reflect media and public interest rather than comprehensive or systematic tracking\", media capturing \"only what journalists are aware of and deemed newsworthy\", that reliance on media may admit incidents mischaracterised, exaggerated or inaccurately attributed to AI, and that its trends do not measure severity, harm type or the number of stakeholders affected.",
    "claims": [
     "F15",
     "F36",
     "F37",
     "F44"
    ]
   },
   {
    "n": 19,
    "para": 6,
    "text": "Its disclosures say that its information should not be reported as the official views of the OECD or its members, that it is populated with articles from third-party outlets and news aggregators with which the OECD has no affiliation, that the OECD \"does not independently verify\" their accuracy, completeness or validity, and that collection and analysis seek reliability, objectivity and quality \"to the best extent possible\".",
    "claims": [
     "F5",
     "F23",
     "F24",
     "F17"
    ]
   },
   {
    "n": 20,
    "para": 6,
    "text": "What its selection leaves out, in the MMRS record's reading: admission requires harm, an incident or a hazard that could plausibly lead to one, and \"a harm no outlet reports has no event to cluster\".",
    "claims": [
     "MMRS-24",
     "MMRS-11"
    ]
   },
   {
    "n": 21,
    "para": 6,
    "text": "The MMRS record keeps itself apart from the entity, each assertion carrying a source and a basis and the lifecycle kept apart from the observation that saw it: the monitor active as read on 28 October 2026, its launch date and its licence unchecked; the trends paper carries its own licence on its rights page, Creative Commons Attribution 4.0.",
    "claims": [
     "MMRS-31",
     "MMRS-27",
     "MMRS-26",
     "F47"
    ]
   },
   {
    "n": 22,
    "para": 7,
    "text": "The fourteen themes are the trends paper's (OECD Artificial Intelligence Papers No. 53, February 2026): it classifies the monitor's media reports into 14 themes, among them synthetic media, child safety, cyberattacks, privacy and health, with three patterns of coverage, increasing, intermittent and declining, by collecting the monitor's \"LLM-generated summaries\", clustering them by k-means into 25 clusters, a number chosen \"based on a qualitative keywords review\" when the statistical tests gave inconsistent results, consolidating them into 14 and assigning each item a single theme.",
    "claims": [
     "F35",
     "F41",
     "F42",
     "F43"
    ]
   },
   {
    "n": 23,
    "para": 7,
    "text": "The Capture Registry (2026-10-29) records that the composition's count of fourteen themes comes from that card, that the methodology page gives no count of themes, and that it did not verify the count.",
    "claims": [
     "CAP-1-06"
    ]
   },
   {
    "n": 24,
    "para": 8,
    "text": "The trends paper places the monitor where there are \"few public and comparable sources of information on AI incidents and hazards\", media reports among the proxy indicators that give partial insight, beside the common framework for reporting AI incidents that the Global Partnership on AI approved in early 2025; the methodology page names as next steps an open submission process under that framework and court rulings and supervisory decisions, and the trends paper open submissions and a record reorganised by theme.",
    "claims": [
     "F45",
     "F16",
     "F46"
    ]
   },
   {
    "n": 25,
    "para": 8,
    "text": "In the MMRS record it is the automated counterpart of the AI Incident Database, \"whose editors resolve each submission\", and with the AIAAIC Repository one of the census's three harm repositories, its neighbours in the harm basin, \"automated and news-derived\" in the MMRS record of the AI Incident Database; the definitions paper thanks that database's founder, Sean McGregor (Responsible AI Collaborative), among the experts who gave feedback.",
    "claims": [
     "MMRS-13",
     "MMRS-16",
     "MMRS-29",
     "MMRS-15",
     "F60"
    ]
   },
   {
    "n": 26,
    "para": 8,
    "text": "A research study can enter it as an event (MMRS record): the Tow Center's study of AI search citation is filed as an incident, \"a research study entering a harm monitor as an event\", and on its page the item, titled \"AI Search Engines Fabricate News Citations, Undermining Publishers\", carries a machine-written summary in which eight search engines and chatbots \"frequently misquote or fabricate news citations, remixing publisher content without credit\", is labelled an AI incident with harm types economic/property, reputational and public interest, and is built from news articles about the study, the earliest dated 11 March 2025.",
    "claims": [
     "MMRS-14",
     "MMRS-28",
     "F62",
     "F63",
     "F64"
    ]
   },
   {
    "n": 27,
    "para": 8,
    "text": "The archive sets one of its own instruments apart from the class: \"Media monitors track coverage of events\", while the Encyclotron (#637) tracks the representation of entire fields of scholarship, \"not what was said yesterday but what the retrieval layer thinks the field has been saying for decades\".",
    "claims": [
     "O637-01"
    ]
   },
   {
    "n": 28,
    "para": 9,
    "text": "Its card rail at its name also carries a conference talk admitted by its title, \"Establishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell | IASEAI 2025\".",
    "claims": [
     "F61"
    ]
   },
   {
    "n": 29,
    "para": 9,
    "text": "At its name the Overview gave the monitor's input as its methodology page does, Event Registry and 150,000 articles a day, and a maker close to the page's, and did not say that large language models have classified its events since November 2024 and written their titles and summaries: it composed the monitor \"with its inputs and definitions right and its classifier left out, the part of its method that makes each record a machine composition\" (Capture Registry, 2026-10-29, checked against the MMRS record and the entity's own pages), the record holding \"what the composition drops\", and the MMRS record lists the capture as the composition layer's account of the entity.",
    "claims": [
     "CAP-1-01",
     "CAP-1-05",
     "CAP-1-02",
     "CAP-1-07",
     "CAP-1-04",
     "CAP-1-08",
     "MMRS-30"
    ]
   }
  ],
  "claims": {
   "MMRS-01": {
    "kind": "archive",
    "quote": "The OECD AI Incidents and Hazards Monitor (AIM) is an automated monitor of AI harm events in the news, run by OECD.AI.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py ARTICLES['oecd-ai-incidents-monitor'], kernel s1",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-19": {
    "kind": "archive",
    "quote": "monitor",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: kind; roles",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F1": {
    "kind": "field",
    "quote": "Automated media discourse monitor of AI incidents and hazards (Beta)",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "header (subtitle under 'AIM: AI Incidents and Hazards Monitor')",
    "modality": "stipulation"
   },
   "MMRS-03": {
    "kind": "archive",
    "quote": "The monitor calls itself an \"Automated media discourse monitor of AI incidents and hazards (Beta).\"",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, What it is s1",
    "modality": "attributed",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F38": {
    "kind": "field",
    "quote": "The OECD.AI Incidents and Hazards Monitor (AIM) tracks these media-reported AI incidents and hazards in real-time and leverages the OECD common reporting framework to categorise them consistently.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "Executive summary ¶7",
    "modality": "documented"
   },
   "F22": {
    "kind": "field",
    "quote": "Your use of the OECD AI incidents and hazards monitor (previously AI Incidents Monitor) (“AIM”) is subject to the terms and conditions found at www.oecd.org/termsandconditions.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Information Transparency Disclosures: Background",
    "modality": "documented"
   },
   "MMRS-17": {
    "kind": "archive",
    "quote": "AI Incidents Monitor",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: aka",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F11": {
    "kind": "field",
    "quote": "The AI Incidents and Hazards Monitor (AIM) was initiated and is being developed by the OECD.AI expert group on AI incidents with the support of the Patrick J. McGovern Foundation.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Overview ¶1 s1",
    "modality": "documented"
   },
   "F50": {
    "kind": "field",
    "quote": "In January 2023, the OECD formalised the OECD.AI Expert Group on AI Incidents to advance the development of i) a common AI incident reporting framework and ii) an AI Incidents Monitor (AIM).",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "Executive summary ¶1",
    "modality": "documented"
   },
   "MMRS-18": {
    "kind": "archive",
    "quote": "OECD.AI",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: operator (evidence: oecd.ai/en/incidents-methodology, basis operator)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F2": {
    "kind": "field",
    "quote": "The OECD AI Incidents and Hazards Monitor (AIM) documents AI incidents and hazards to help policymakers, AI practitioners, and all stakeholders worldwide gain valuable insights into the risks and harms of AI systems.",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "introduction ¶1 s2",
    "modality": "stipulation"
   },
   "F3": {
    "kind": "field",
    "quote": "Over time, AIM will help to show risk patterns and establish a collective understanding of AI incidents and hazards and their multifaceted nature, serving as an important tool for trustworthy AI.",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "introduction ¶1 s3",
    "modality": "hypothesis"
   },
   "F12": {
    "kind": "field",
    "quote": "The goal of the AIM is to track actual AI incidents and hazards, as defined by the OECD, in real time and provide the evidence-base to inform related AI policy discussions.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Overview ¶1 s2",
    "modality": "stipulation"
   },
   "F10": {
    "kind": "field",
    "quote": "All actors must monitor AI incidents and hazards to mitigate risks As AI adoption continues to grow, successful risk mitigation will require a solid evidence ba...",
    "source": "B2",
    "source_label": "OECD — AI risks and incidents",
    "locus": "card snippet (page HTTP 403)",
    "modality": "interpretation"
   },
   "F48": {
    "kind": "field",
    "quote": "May 2024 No. 16 … This report provides a definition of AI incident and related terminology. These definitions aim to clarify what constitutes an AI incident, an AI hazard, and associated terminology without being overly prescriptive. This document will inform the development of a common AI incidents reporting framework and its application through the OECD AI Incidents Monitor (AIM).",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "cover; Foreword ¶1",
    "modality": "stipulation"
   },
   "F49": {
    "kind": "field",
    "quote": "The report was written by Karine Perset and Luis Aranda under the supervision of Audrey Plonk, Deputy Director of the OECD Science Technology and Innovation Directorate. … This paper was approved and declassified by written procedure by the OECD Digital Policy Committee (DPC) on 14 March 2024 and prepared for publication by the OECD Secretariat.",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "Foreword ¶3–4",
    "modality": "documented"
   },
   "F59": {
    "kind": "field",
    "quote": "This classification is based on a stocktaking exercise of over 30 frameworks, standards and legal instruments relevant to risk and harms",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "§3 ¶4",
    "modality": "documented"
   },
   "F18": {
    "kind": "field",
    "quote": "Published in May 2024, the paper Defining AI incidents and related terms, defines an event where the development or use of an AI system results in actual harm as an “AI incident”, while an event where the development or use of an AI system is potentially harmful is termed an “AI hazard”.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Definitions ¶1",
    "modality": "attributed"
   },
   "MMRS-05": {
    "kind": "archive",
    "quote": "The definitions follow the OECD's paper of May 2024.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, What it is s3",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F51": {
    "kind": "field",
    "quote": "An AI incident is an event, circumstance or series of events where the development, use or malfunction of one or more AI systems directly or indirectly leads to any of the following harms: (a) injury or harm to the health of a person or groups of people; (b) disruption of the management and operation of critical infrastructure; (c) violations of human rights or a breach of obligations under the applicable law intended to protect fundamental, labour and intellectual property rights; (d) harm to property, communities or the environment.",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "§4 Draft definition of an AI incident",
    "modality": "stipulation"
   },
   "F19": {
    "kind": "field",
    "quote": "An AI incident is an event, circumstance or series of events where the development, use or malfunction of one or more AI systems directly or indirectly leads to any of the following harms:",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Definitions: AI incident",
    "modality": "stipulation"
   },
   "F52": {
    "kind": "field",
    "quote": "An AI hazard is an event, circumstance or series of events where the development, use or malfunction of one or more AI systems could plausibly lead to an AI incident, i.e., any of the following harms:",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "§5 Draft definition of an AI hazard",
    "modality": "stipulation"
   },
   "F20": {
    "kind": "field",
    "quote": "An AI hazard is an event, circumstance or series of events where the development, use or malfunction of one or more AI systems could plausibly lead to an AI incident, i.e., any of the following harms:",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Definitions: AI hazard",
    "modality": "stipulation"
   },
   "F53": {
    "kind": "field",
    "quote": "Near misses are events that could have led to an AI incident and are therefore included under this definition of AI hazards.",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "§5 key clarifications",
    "modality": "stipulation"
   },
   "MMRS-04": {
    "kind": "archive",
    "quote": "An AI incident is an event in which the development, use or malfunction of an AI system directly or indirectly leads to harm; an AI hazard is an event that \"could plausibly lead to an AI incident.\"",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, What it is s2",
    "modality": "attributed",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F56": {
    "kind": "field",
    "quote": "Reputational harm to individuals and intangible harms such as hate speech and mis- and disinformation are included under (c) in relation to a breach of fundamental rights. … For example, violations of authors’ and personal rights fit into (c), while violations of industrial property rights are included under the concept of harm to property in (d).",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "§4 key clarifications",
    "modality": "stipulation"
   },
   "F21": {
    "kind": "field",
    "quote": "These definitions were built based on the definition of an AI system as described in the OECD recommendation on AI of 2019 and revised in 2024. An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Definitions: AI system",
    "modality": "stipulation"
   },
   "F54": {
    "kind": "field",
    "quote": "Serious AI incidents are a subset of AI incidents and are intended to include substantial, material incidents. … An AI disaster is a serious AI incident that disrupts the functioning of a community or a society and that may test or exceed its capacity to cope, using its own resources.",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "§4 serious AI incident; AI disaster",
    "modality": "stipulation"
   },
   "F55": {
    "kind": "field",
    "quote": "Serious AI hazards are a subset of AI hazards.",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "§5 serious AI hazard",
    "modality": "stipulation"
   },
   "F58": {
    "kind": "field",
    "quote": "Assessing the “seriousness” of an AI incident, harm, damage, or disruption (e.g., to determine whether an event is classified as an incident or a serious incident) is context-dependent and is also left for further discussion.",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "§6 Conclusion ¶2",
    "modality": "self-description"
   },
   "F57": {
    "kind": "field",
    "quote": "These definitions aim to foster international interoperability while providing flexibility for jurisdictions to determine the scope of AI incidents and hazards they wish to address.",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "Abstract",
    "modality": "self-description"
   },
   "F6": {
    "kind": "field",
    "quote": "Note: An AI incident or hazard can be reported by one or more news articles covering the same event. Data processing powered by Microsoft Azure using data from Event Registry.",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "chart block, note",
    "modality": "documented"
   },
   "MMRS-20": {
    "kind": "archive",
    "quote": "incident or hazard (an event in which an AI system leads, or could plausibly lead, to harm)",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: unit (evidence: oecd.ai/en/incidents-methodology, basis operator)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F13": {
    "kind": "field",
    "quote": "The AIM detects AI incidents and hazards from reputable international news outlets. The data is provided by Event Registry (ER), a news intelligence platform that monitors and aggregates world news by processing over 150 000 news articles daily.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Overview ¶2 s1–2",
    "modality": "documented"
   },
   "F14": {
    "kind": "field",
    "quote": "Event Registry detects events, which are clusters of news articles reporting on the same happening. These events are tagged with related concepts. The events provided by Event Registry to AIM are limited to those that include AI concepts.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Overview ¶2 s3–5",
    "modality": "documented"
   },
   "F39": {
    "kind": "field",
    "quote": "Currently the data rely on reports from the top 50% of media outlets, based on the Alexa traffic rankings (OECD.AI, 2025[6]). These reports are aggregated by Event Registry, a news intelligence platform which monitors over 150 000 news articles daily.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "§1 Introduction ¶6",
    "modality": "documented"
   },
   "MMRS-06": {
    "kind": "archive",
    "quote": "Event Registry clusters news articles about one happening into an event, and only events tagged with AI concepts reach the monitor.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, How it works s1",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-10": {
    "kind": "archive",
    "quote": "An event enters when reputable outlets report it.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, What its selection leaves out s1",
    "modality": "interpretation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F25": {
    "kind": "field",
    "quote": "Since November 2024, incidents and hazards are identified and added to the AIM through the following steps. Historical incidents and hazards, collected prior to November 2024, are currently being retrospectively annotated using the same methodology to ensure consistency.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Methodology ¶1–2",
    "modality": "documented"
   },
   "F26": {
    "kind": "field",
    "quote": "LLMs are used to classify AI-related events into three categories: AI incidents, AI hazards or unrelated events, as defined above. This classification is based on the event summary provided from Event Registry. The classification follows a two-step process: first, a smaller model (OpenAI’s GPT-4o mini) filters out events that seem unrelated to AI incidents and hazards; next, a larger model (OpenAI’s GPT-4o) reclassifies the remaining events to confirm whether they are incidents or hazards.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Methodology step 2",
    "modality": "documented"
   },
   "F27": {
    "kind": "field",
    "quote": "Individual news articles are classified into incidents and hazards using LLMs, following the same process as for events. In this case, however, article content is used instead of event summaries to enhance reliability. … If some articles are classified differently, the event will adopt the most common classification among them.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Methodology step 3",
    "modality": "documented"
   },
   "F34": {
    "kind": "field",
    "quote": "Prior to November 2024, AI incidents and hazards were classified and included on the AIM using traditional machine learning techniques.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Methodology, last ¶",
    "modality": "documented"
   },
   "F30": {
    "kind": "field",
    "quote": "Similar events are grouped by analysing their summaries and identifying related events in AIM using cosine similarity. For each new incident or hazard, the top 10 closest summaries (i.e., “embeddings”) from existing events in AIM are identified. An LLM (OpenAI’s o3-mini) is then used to assess whether any of these similar events can be clustered together.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Methodology step 6",
    "modality": "documented"
   },
   "F31": {
    "kind": "field",
    "quote": "The pipeline runs daily, processing events from one to four days ago. … Articles appearing after this timeframe are likely to be misclassified as a separate event.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Update frequency ¶1–2",
    "modality": "documented"
   },
   "MMRS-02": {
    "kind": "archive",
    "quote": "It reads AI-tagged news events from Event Registry daily and, since November 2024, has large language models sort them into incidents and hazards and write their summaries; about 18,579 are listed.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, kernel s2",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-07": {
    "kind": "archive",
    "quote": "Since November 2024 a small model (GPT-4o mini) filters, a larger one (GPT-4o) reclassifies, and o3-mini writes the title, summary, harm type, severity, stakeholders and country from the top three articles.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, How it works s2",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-08": {
    "kind": "archive",
    "quote": "New items are matched to existing events by embedding similarity.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, How it works s3",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-21": {
    "kind": "archive",
    "quote": "automated: news articles aggregated by Event Registry; since November 2024, LLM classification (GPT-4o mini, then GPT-4o) and LLM-written metadata (o3-mini); daily",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: intake (evidence: oecd.ai/en/incidents-methodology, basis operator)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-09": {
    "kind": "archive",
    "quote": "The pipeline runs daily on events one to four days old.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, How it works s4",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-23": {
    "kind": "archive",
    "quote": "daily",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: cadence (evidence: oecd.ai/en/incidents-methodology, basis operator)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F28": {
    "kind": "field",
    "quote": "Each event is enriched with relevant metadata, including an event title, summary, the harm type, severity, affected stakeholders, and the country where it occurred, among others. This metadata is LLM-generated (OpenAI’s o3-mini) from the top three articles of each event, selected from different news outlets.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Methodology step 5",
    "modality": "documented"
   },
   "F29": {
    "kind": "field",
    "quote": "Articles are ranked based on their outlets’ ranking in their respective country. This approach ensures a balance between the popularity of the outlets and fair geographical representation. Currently, the summary and metadata are generated at the time the incident or hazard is added to the AIM.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Methodology step 5, continued",
    "modality": "documented"
   },
   "F8": {
    "kind": "field",
    "quote": "Why's our monitor labelling this an incident or hazard? … Therefore, the event is best classified as an AI Hazard due to the credible risk of AI exacerbating economic disparities and job losses in the future.[AI generated]",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "result list, the first item's explanation (as fetched 2026-10-29)",
    "modality": "documented"
   },
   "F9": {
    "kind": "field",
    "quote": "It is not Complementary Information because the article is not about responses or updates to a past incident, nor is it Beneficial Use or Unrelated.[AI generated]",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "result list, an item's explanation (as fetched 2026-10-29)",
    "modality": "documented"
   },
   "MMRS-25": {
    "kind": "archive",
    "quote": "no: titles, summaries and metadata are LLM-written from the top articles",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: properties.preserves_original_outputs (basis operator)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-12": {
    "kind": "archive",
    "quote": "The record's own wording is machine-written from the articles, so the monitor is itself a machine reception of the news: its summaries are compositions, and the articles are their sources.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, What its selection leaves out s3",
    "modality": "interpretation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "CAP-1-03": {
    "kind": "archive",
    "quote": "The record reads the monitor as itself a machine reception of the news; the composition reads it as an aggregator of the news.",
    "deposit": null,
    "source_label": "Capture Registry observation OBS-23f50c1b7e04 (oecd-ai-incidents-and-hazards-monitor-aio-20261029), field findings",
    "record": "https://www.alexanarch.org/captures/oecd-ai-incidents-and-hazards-monitor-aio-20261029/",
    "locus": "oecd-ai-incidents-and-hazards-monitor-aio-20261029, findings[0] s4",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "F7": {
    "kind": "field",
    "quote": "Results: About 18579 incidents & hazards",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "results line",
    "modality": "documented"
   },
   "MMRS-22": {
    "kind": "archive",
    "quote": "about 18,579 incidents and hazards",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: count (as of 2026-10-28; evidence: oecd.ai/en/incidents, basis observed)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F32": {
    "kind": "field",
    "quote": "On average, about 1 000 AI events are analysed daily, resulting in around 10 AI incidents or hazards.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Summary table, Identification and classification",
    "modality": "documented"
   },
   "F33": {
    "kind": "field",
    "quote": "the AIM detects on average 30 incidents and hazards per day.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Summary table, note",
    "modality": "documented"
   },
   "F4": {
    "kind": "field",
    "quote": "AI incidents seem to be getting more media attention lately, but they've actually gone down as a share of all AI news (see chart below!).",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "introduction ¶1 s4",
    "modality": "documented"
   },
   "F40": {
    "kind": "field",
    "quote": "Between 2022 and 2025, the number of AI incidents and hazards reported by the media rose steadily, from 92 to 324 per month on average (Figure 1.1, Panel A). However, their share relative to total AI news declined, from 3.2% in 2022 to around 2.5% in 2025 (Figure 1.1, Panel B).",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "§1 Introduction ¶8",
    "modality": "documented"
   },
   "F15": {
    "kind": "field",
    "quote": "While recognising the likelihood that these incidents and hazards only represent a subset of all AI incidents and hazards worldwide, these publicly reported incidents and hazards nonetheless provide a useful starting point for building the evidence base.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Overview ¶3",
    "modality": "self-description"
   },
   "F36": {
    "kind": "field",
    "quote": "The findings reflect media and public interest rather than comprehensive or systematic tracking of AI incidents and hazards. Importantly, media reports capture only what journalists are aware of and deemed newsworthy.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "Executive summary ¶5",
    "modality": "self-description"
   },
   "F37": {
    "kind": "field",
    "quote": "Conversely, reliance on media sources may introduce errors of inclusion, as some reported incidents may be mischaracterised, exaggerated, or inaccurately attributed to AI due to limited background or technical understanding.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "Executive summary ¶6",
    "modality": "self-description"
   },
   "F44": {
    "kind": "field",
    "quote": "These trends do not measure how severe incidents are, the types of harm involved, or the number of affected stakeholders, and should not be read as indicators of these factors.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "§2, after Figure 2.1",
    "modality": "self-description"
   },
   "F5": {
    "kind": "field",
    "quote": "The information displayed in the AIM should not be reported as representing the official views of the OECD or of its member countries.",
    "source": "B1",
    "source_label": "OECD AI Policy Observatory — OECD AI Incidents Monitor, an evidence base for trustworthy AI",
    "locus": "introduction ¶2",
    "modality": "self-description"
   },
   "F23": {
    "kind": "field",
    "quote": "the AIM is populated with news articles from various third-party outlets and news aggregators with which the OECD has no affiliation.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Information Transparency Disclosures: Third-Party Information",
    "modality": "documented"
   },
   "F24": {
    "kind": "field",
    "quote": "The OECD cannot guarantee and does not independently verify the accuracy, completeness, or validity of third-party information provided in the AIM.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Information Transparency Disclosures: Errors and Omissions",
    "modality": "self-description"
   },
   "F17": {
    "kind": "field",
    "quote": "The data collection and analysis for the AIM is done to ensure, to the best extent possible, the reliability, objectivity and quality of the information for AI incidents and hazards.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Overview ¶5",
    "modality": "self-description"
   },
   "MMRS-24": {
    "kind": "archive",
    "quote": "yes: an incident, or a hazard that could plausibly lead to one",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: properties.admission_requires_harm (basis operator)",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-11": {
    "kind": "archive",
    "quote": "A harm no outlet reports has no event to cluster.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, What its selection leaves out s2",
    "modality": "interpretation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-31": {
    "kind": "archive",
    "quote": "each page has two nodes, the MMRS record and the entity it describes; 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: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: schema_note (v2, 2026-10-28)",
    "modality": "stipulation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-27": {
    "kind": "archive",
    "quote": "active",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: lifecycle.state (observed 2026-10-28, read)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-26": {
    "kind": "archive",
    "quote": "launch date … licence",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: unchecked",
    "modality": "self-description",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F47": {
    "kind": "field",
    "quote": "Attribution 4.0 International (CC BY 4.0)",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "rights page",
    "modality": "documented"
   },
   "F35": {
    "kind": "field",
    "quote": "This paper analyses live data from the OECD.AI Incidents and Hazards Monitor (AIM), classifying media reports into 14 themes, including synthetic media, child safety, cyberattacks, privacy, and health. It identifies three distinct patterns of media coverage – increasing, intermittent, and declining – and traces how media attention shifts across themes over time.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "Abstract",
    "modality": "documented"
   },
   "F41": {
    "kind": "field",
    "quote": "LLM-generated summaries of AI incidents and hazards were collected and transformed into a semantic representation capturing the underlying concepts. Using a k-means clustering algorithm, these reports were classified into 25 thematic clusters each capturing semantically similar risk characteristics. … This resulted in 14 distinct themes.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "Annex A, Identifying key thematic clusters ¶1",
    "modality": "documented"
   },
   "F42": {
    "kind": "field",
    "quote": "Since these methods gave inconsistent results, the authors chose 25 clusters based on a qualitative keywords review.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "Annex A, Clustering ¶2",
    "modality": "self-description"
   },
   "F43": {
    "kind": "field",
    "quote": "To maintain analytical clarity and avoid double counting, each media-reported incident or hazard is assigned to the single most relevant theme based on the available information.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "§2 ¶2",
    "modality": "stipulation"
   },
   "CAP-1-06": {
    "kind": "archive",
    "quote": "FOURTEEN THEMES. The count comes from a card ('Trends in AI incidents and hazards reported by the media'); the methodology page read gives no count of themes. Not verified here.",
    "deposit": null,
    "source_label": "Capture Registry observation OBS-23f50c1b7e04 (oecd-ai-incidents-and-hazards-monitor-aio-20261029), field findings",
    "record": "https://www.alexanarch.org/captures/oecd-ai-incidents-and-hazards-monitor-aio-20261029/",
    "locus": "oecd-ai-incidents-and-hazards-monitor-aio-20261029, findings[2]",
    "modality": "self-description",
    "date": "2026-10-29"
   },
   "F45": {
    "kind": "field",
    "quote": "In early 2025, the members of the Global Partnership on AI (GPAI) approved a common framework for reporting AI incidents, designed as an international data-sharing template for public and private organisations across jurisdictions and sectors … At present, there are few public and comparable sources of information on AI incidents and hazards. … Proxy indicators – such as court rulings on AI-related cases or media reports – can offer partial insights into where and what types of incidents are occurring.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "Executive summary ¶2–4",
    "modality": "documented"
   },
   "F16": {
    "kind": "field",
    "quote": "An open submission process following the common reporting framework for AI incidents is currently being developed to complement AI incidents and hazards from news articles. Other next steps include complementing incidents and hazards with court rulings and decisions by supervisory authorities wherever they exist.",
    "source": "B3",
    "source_label": "OECD AI Policy Observatory — Overview and methodology of the AI Incidents and Hazards Monitor - OECD.AI",
    "locus": "Overview ¶4",
    "modality": "hypothesis"
   },
   "F46": {
    "kind": "field",
    "quote": "Next steps could include allowing open submissions to complement media-based monitoring and reorganising incidents and hazards visually by theme to support deeper analysis.",
    "source": "B4",
    "source_label": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
    "locus": "§6 Conclusion ¶4",
    "modality": "hypothesis"
   },
   "MMRS-13": {
    "kind": "archive",
    "quote": "It is the automated counterpart of the AI Incident Database, whose editors resolve each submission.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, Place in the field s1",
    "modality": "interpretation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-16": {
    "kind": "archive",
    "quote": "It is the oldest of the three harm repositories in this census, beside the AI Incident Database and the OECD monitor.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: aiaaic-repository (https://www.machinemediation.org/aggregators/aiaaic-repository/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/aiaaic-repository/",
    "locus": "articles.py ARTICLES['aiaaic-repository'], Place in the field s1",
    "modality": "interpretation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-29": {
    "kind": "archive",
    "quote": "ai-incident-database … aiaaic-repository",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: relations[1–2] (neighbour)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-15": {
    "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 (https://www.machinemediation.org/aggregators/ai-incident-database/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/ai-incident-database/",
    "locus": "articles.py ARTICLES['ai-incident-database'], Place in the field s2",
    "modality": "interpretation",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F60": {
    "kind": "field",
    "quote": "Many experts provided invaluable feedback and suggestions, including Sean McGregor (Responsible AI Collaborative)",
    "source": "B5",
    "source_label": "OECD — Defining AI incidents and related terms (EN)",
    "locus": "Acknowledgements ¶2",
    "modality": "documented"
   },
   "MMRS-14": {
    "kind": "archive",
    "quote": "A research study can enter it as an event: the Tow Center's 2025 study of AI search citation is filed as an incident.",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "articles.py, Place in the field s2",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "MMRS-28": {
    "kind": "archive",
    "quote": "The Tow Center AI search citation study, filed as an incident … a research study entering a harm monitor as an event",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: relations[0] (holding: oecd.ai/en/incidents/2025-03-11-8537)",
    "modality": "documented",
    "date": "2026-10-28 (corrected 2026-10-29)"
   },
   "F62": {
    "kind": "field",
    "quote": "AI Search Engines Fabricate News Citations, Undermining Publishers … A study by the Tow Center found that eight AI-powered search engines and chatbots, including ChatGPT, Perplexity and Elon Musk’s Grok3, frequently misquote or fabricate news citations, remixing publisher content without credit and driving down traffic. Over 60% of responses contained wrong or broken links, undermining journalistic credibility.[AI generated]",
    "source": "B7",
    "source_label": "OECD AI Policy Observatory — AI Search Engines Fabricate News Citations, Undermining Publishers (AIM item 2025-03-11-8537)",
    "locus": "item title and summary",
    "modality": "documented"
   },
   "F63": {
    "kind": "field",
    "quote": "Hence, the event meets the criteria for an AI Incident as it involves realized harm caused by AI system outputs.[AI generated] … Harm types Economic/PropertyReputationalPublic interest",
    "source": "B7",
    "source_label": "OECD AI Policy Observatory — AI Search Engines Fabricate News Citations, Undermining Publishers (AIM item 2025-03-11-8537)",
    "locus": "label explanation; harm types",
    "modality": "documented"
   },
   "F64": {
    "kind": "field",
    "quote": "Articles about this incident or hazard … New study finds that AI search tools are 60 percent inaccurate on average 2025-03-11 TechSpot",
    "source": "B7",
    "source_label": "OECD AI Policy Observatory — AI Search Engines Fabricate News Citations, Undermining Publishers (AIM item 2025-03-11-8537)",
    "locus": "articles list (25 articles, 2025-03-11 to 2025-03-17)",
    "modality": "documented"
   },
   "O637-01": {
    "kind": "archive",
    "quote": "**Not media monitoring.** Media monitors track coverage of events. The Encyclotron tracks the representation of entire fields of scholarship — not what was said yesterday but what the retrieval layer thinks the field has been saying for decades.",
    "deposit": 637,
    "source_label": "#637 The Encyclotron: The First Reproducible Instrument for Measuring Scholarly Fidelity in the Summarizer Layer (EA-ENCYCLOTRON-01)",
    "record": "https://www.alexanarch.org/s/records/637/",
    "locus": "§VIII THE ENCYCLOTRON IS NOT, 'Not media monitoring'",
    "modality": "interpretation",
    "date": "2026-04-08"
   },
   "F61": {
    "kind": "field",
    "quote": "Establishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell | IASEAI 2025",
    "source": "B6",
    "source_label": "YouTube·International Association for Safe & Ethical AI — Establishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell",
    "locus": "oEmbed title (video, 29:48)",
    "modality": "documented"
   },
   "CAP-1-01": {
    "kind": "archive",
    "quote": "THE PIPELINE'S INPUT RIGHT, ITS MACHINE UNSAID. Event Registry and the 150,000 articles a day are the methodology page's.",
    "deposit": null,
    "source_label": "Capture Registry observation OBS-23f50c1b7e04 (oecd-ai-incidents-and-hazards-monitor-aio-20261029), field findings",
    "record": "https://www.alexanarch.org/captures/oecd-ai-incidents-and-hazards-monitor-aio-20261029/",
    "locus": "oecd-ai-incidents-and-hazards-monitor-aio-20261029, findings[0] s1–2",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-05": {
    "kind": "archive",
    "quote": "THE MAKER. The methodology page says the monitor 'was initiated and is being developed by the OECD.AI expert group on AI incidents' with support from the Patrick J. McGovern Foundation; 'developed by the OECD.AI Policy Observatory in collaboration with' is close.",
    "deposit": null,
    "source_label": "Capture Registry observation OBS-23f50c1b7e04 (oecd-ai-incidents-and-hazards-monitor-aio-20261029), field findings",
    "record": "https://www.alexanarch.org/captures/oecd-ai-incidents-and-hazards-monitor-aio-20261029/",
    "locus": "oecd-ai-incidents-and-hazards-monitor-aio-20261029, findings[1]",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-02": {
    "kind": "archive",
    "quote": "That large language models have classified the events since November 2024 and written their titles and summaries (GPT-4o mini, GPT-4o, o3-mini) is not said.",
    "deposit": null,
    "source_label": "Capture Registry observation OBS-23f50c1b7e04 (oecd-ai-incidents-and-hazards-monitor-aio-20261029), field findings",
    "record": "https://www.alexanarch.org/captures/oecd-ai-incidents-and-hazards-monitor-aio-20261029/",
    "locus": "oecd-ai-incidents-and-hazards-monitor-aio-20261029, findings[0] s3",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-07": {
    "kind": "archive",
    "quote": "The monitor is composed from its own methodology page with its inputs and definitions right and its classifier left out, the part of its method that makes each record a machine composition.",
    "deposit": null,
    "source_label": "Capture Registry observation OBS-23f50c1b7e04 (oecd-ai-incidents-and-hazards-monitor-aio-20261029), field analysis",
    "record": "https://www.alexanarch.org/captures/oecd-ai-incidents-and-hazards-monitor-aio-20261029/",
    "locus": "oecd-ai-incidents-and-hazards-monitor-aio-20261029, analysis s1",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-04": {
    "kind": "archive",
    "quote": "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-23f50c1b7e04 (oecd-ai-incidents-and-hazards-monitor-aio-20261029), field findings",
    "record": "https://www.alexanarch.org/captures/oecd-ai-incidents-and-hazards-monitor-aio-20261029/",
    "locus": "oecd-ai-incidents-and-hazards-monitor-aio-20261029, findings[0] s5",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-08": {
    "kind": "archive",
    "quote": "The record holds what the composition drops.",
    "deposit": null,
    "source_label": "Capture Registry observation OBS-23f50c1b7e04 (oecd-ai-incidents-and-hazards-monitor-aio-20261029), field analysis",
    "record": "https://www.alexanarch.org/captures/oecd-ai-incidents-and-hazards-monitor-aio-20261029/",
    "locus": "oecd-ai-incidents-and-hazards-monitor-aio-20261029, analysis s2",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "MMRS-30": {
    "kind": "archive",
    "quote": "the composition layer's account of the entity, seated in the Capture Registry",
    "deposit": null,
    "source_label": "MMRS meta-aggregator record: OECD AI Incidents and Hazards Monitor (https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/), editor Lee Sharks",
    "record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
    "locus": "census: relations[3] (capture)",
    "modality": "documented",
    "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: 'we can simply build them here as against their wikipedia articles if they exist, their own pages, etc.'; 'lets build them'; the MMRS meta-aggregator entity",
 "stage": "composed 2026-10-29 under EA-NEGONT-02 v0.9, #1690, equal case, ρ = extends (draft, not frozen; ledgers unaudited, one extractor; every field and archive quote re-checked by the session)",
 "date": "2026-10-29",
 "status": {
  "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the MMRS record and the capture record; archive selected by D/R/O (one deposit of two candidates admitted, #637 at O-class; #170 a homonym on the acronym; hop of 11 read and rejected; the class 'AI incident reporting and monitoring' matched nothing), with the MMRS record (31 claims) and the Capture Registry's findings and analysis (8 claims) ledgered as the archive's records of the entity; the field B by three origins (6 card sources for 6 cards, 1 own page beyond them, 2 Wikipedia records); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta",
  "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 64/64 verified by script against saved text, PDF text, oEmbed title or card snippet; archive quotes 40/40 verified verbatim against the registry, census.json, articles.py and AXN-01ED; carried as marked: F32, F33 internally_inconsistent",
  "frozen": false,
  "breaches": []
 },
 "addresses": [
  {
   "address": "oecd ai incidents and hazards monitor",
   "address_url": "https://www.alexanarch.org/addresses/oecd-ai-incidents-and-hazards-monitor/",
   "register": []
  }
 ],
 "counts": {
  "ko_sentences": 29,
  "p_words": 356,
  "field_claims": 64,
  "archive_claims": 40,
  "ko_field_claims_cited": 64,
  "ko_archive_claims_cited": 40,
  "admitted_deposits": 1,
  "register_addresses": 0,
  "traversal": {
   "D_sentences": 4,
   "D_deposits": 1,
   "R_sentences": 2,
   "R_deposits": 1,
   "O_direct": 3,
   "O_class": 1,
   "O_deposits": 2,
   "H_candidates": 11
  }
 },
 "links": {
  "record": "https://www.alexanarch.org/non/oecd-ai-incidents-monitor/",
  "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/oecd-ai-incidents-monitor.json",
  "archive_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/oecd-ai-incidents-monitor/ledger-archive.json",
  "field_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/oecd-ai-incidents-monitor/field.json",
  "selection": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/oecd-ai-incidents-monitor/",
  "traversal": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/oecd-ai-incidents-monitor/",
  "traversal_summary": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/oecd-ai-incidents-monitor/summary.json",
  "reading": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/oecd-ai-incidents-monitor/reading.json",
  "configuration": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/panel/configs/oecd-ai-incidents-monitor.json",
  "address_page": "https://www.alexanarch.org/addresses/oecd-ai-incidents-and-hazards-monitor/",
  "mmrs_record": "https://www.machinemediation.org/aggregators/oecd-ai-incidents-monitor/",
  "dataset": "https://huggingface.co/datasets/leesharks/negative-of-the-negative"
 },
 "row_sha256": "56a344e55a2187743efa7c92c474b91aef3b708a8af7440168a7a0cb3817f30e",
 "record_url": "https://www.alexanarch.org/non/oecd-ai-incidents-monitor/",
 "json_url": "https://www.alexanarch.org/non/oecd-ai-incidents-monitor/entity.json",
 "spec": "https://www.alexanarch.org/s/records/1690/",
 "creator": "Lee Sharks",
 "orcid": "0009-0000-1599-0703",
 "license": "CC BY 4.0"
}