{
 "row": "oecd-ai-incidents-monitor",
 "address": "oecd ai incidents and hazards monitor",
 "addresses": [
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 "extracted": "2026-10-29",
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 "pages": "saved files under datasets/negative-of-the-negative/v2/intake/oecd-ai-incidents-monitor-20261029/ (Bn.html raw, Bn.txt text with scripts and styles stripped by the composer's html-to-text (python html.parser); B4.pdf and B5.pdf with B4.pdf.txt and B5.pdf.txt by pdftotext; B2.html and B4-html-403.html the 403 responses; B6.json the oEmbed response; W1.html, W1.api.json, W1-search.html, W1-search2.html, W2.wiki, W2.html); sha256 per file in each source's 'files'",
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   "kind": "own page (an item of the monitor)",
   "surfaced_by_T": "no (not surfaced by T; admitted beside the cards by the operator's ruling of 2026-10-29)",
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   "origin": "card",
   "locus": "Methodology step 5, continued",
   "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.",
   "claim": "the top articles are chosen by their outlets' national ranking; summary and metadata are generated once, when the item is added",
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   "source": "B3",
   "origin": "card",
   "locus": "Methodology step 6",
   "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.",
   "claim": "grouping: the ten nearest summaries by cosine similarity, then o3-mini decides whether to cluster",
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   "source": "B3",
   "origin": "card",
   "locus": "Update frequency ¶1–2",
   "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.",
   "claim": "daily, on events one to four days old; later articles are likely misclassified as a separate event",
   "modality": "documented",
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   "source": "B3",
   "origin": "card",
   "locus": "Summary table, Identification and classification",
   "quote": "On average, about 1 000 AI events are analysed daily, resulting in around 10 AI incidents or hazards.",
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    "internally_inconsistent (B3 states the daily yield two ways: about 10 (F32) and about 30 (F33), both loci cited)"
   ]
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  "F33": {
   "source": "B3",
   "origin": "card",
   "locus": "Summary table, note",
   "quote": "the AIM detects on average 30 incidents and hazards per day.",
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    "internally_inconsistent (B3: about 30 (F33) against about 10 (F32))"
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  "F34": {
   "source": "B3",
   "origin": "card",
   "locus": "Methodology, last ¶",
   "quote": "Prior to November 2024, AI incidents and hazards were classified and included on the AIM using traditional machine learning techniques.",
   "claim": "before November 2024 the monitor classified with traditional machine learning",
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  "F35": {
   "source": "B4",
   "origin": "card",
   "locus": "Abstract",
   "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.",
   "claim": "the 2026 paper analyses the monitor's data, classifying its media reports into 14 themes, with three patterns of coverage",
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   "source": "B4",
   "origin": "card",
   "locus": "Executive summary ¶5",
   "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.",
   "claim": "the paper's governing limit: its findings reflect media and public interest, not systematic tracking; media capture only what journalists know and deem newsworthy",
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   "origin": "card",
   "locus": "Executive summary ¶6",
   "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.",
   "claim": "reliance on media may admit errors: incidents mischaracterised, exaggerated or wrongly attributed to AI",
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   "source": "B4",
   "origin": "card",
   "locus": "Executive summary ¶7",
   "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.",
   "claim": "the paper's account of the monitor: tracking media-reported incidents and hazards in real time, categorised by the common reporting framework",
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   "const": false,
   "lineage": "what it is: an automated media monitor",
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   "source": "B4",
   "origin": "card",
   "locus": "§1 Introduction ¶6",
   "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.",
   "claim": "the data rely on the top 50% of outlets by Alexa traffic rank, aggregated by Event Registry",
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   "const": false,
   "lineage": "intake: Event Registry's news events",
   "date": "2026-02 (OECD Artificial Intelligence Papers No. 53; PDF created 2026-02-09)",
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   "source": "B4",
   "origin": "card",
   "locus": "§1 Introduction ¶8",
   "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).",
   "claim": "92 to 324 a month between 2022 and 2025; their share of AI news down from 3.2% to about 2.5%",
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   "kernel": false,
   "const": false,
   "lineage": "trend: counts and shares",
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  "F41": {
   "source": "B4",
   "origin": "card",
   "locus": "Annex A, Identifying key thematic clusters ¶1",
   "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.",
   "claim": "the 14 themes were made by clustering the monitor's LLM-generated summaries (k-means, 25 clusters, consolidated to 14)",
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   "kind": "function",
   "kernel": false,
   "const": false,
   "lineage": "themes: the 2026 paper's analysis",
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   "source": "B4",
   "origin": "card",
   "locus": "Annex A, Clustering ¶2",
   "quote": "Since these methods gave inconsistent results, the authors chose 25 clusters based on a qualitative keywords review.",
   "claim": "the number of clusters was chosen by a qualitative keyword review after the statistical tests disagreed",
   "modality": "self-description",
   "kind": "limit",
   "kernel": false,
   "const": false,
   "lineage": "themes: the 2026 paper's analysis",
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   "source": "B4",
   "origin": "card",
   "locus": "§2 ¶2",
   "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.",
   "claim": "one theme per item",
   "modality": "stipulation",
   "kind": "function",
   "kernel": false,
   "const": false,
   "lineage": "themes: the 2026 paper's analysis",
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   "source": "B4",
   "origin": "card",
   "locus": "§2, after Figure 2.1",
   "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.",
   "claim": "the trends measure attention, not severity, harm type or the number affected",
   "modality": "self-description",
   "kind": "limit",
   "kernel": false,
   "const": false,
   "lineage": "limits: a subset of what happens",
   "date": "2026-02 (OECD Artificial Intelligence Papers No. 53; PDF created 2026-02-09)",
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  "F45": {
   "source": "B4",
   "origin": "card",
   "locus": "Executive summary ¶2–4",
   "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.",
   "claim": "the common reporting framework (GPAI, early 2025); few public, comparable sources; media reports as a proxy",
   "modality": "documented",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "place: the common reporting framework and proxies",
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  "F46": {
   "source": "B4",
   "origin": "card",
   "locus": "§6 Conclusion ¶4",
   "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.",
   "claim": "next steps: open submissions; the record reorganised by theme",
   "modality": "hypothesis",
   "kind": "prediction",
   "kernel": false,
   "const": false,
   "lineage": "next steps: open submission, other sources",
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  "F47": {
   "source": "B4",
   "origin": "card",
   "locus": "rights page",
   "quote": "Attribution 4.0 International (CC BY 4.0)",
   "claim": "the paper (not the monitor's data) is licensed CC BY 4.0",
   "modality": "documented",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "name and terms",
   "date": "2026-02 (OECD Artificial Intelligence Papers No. 53; PDF created 2026-02-09)",
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  "F48": {
   "source": "B5",
   "origin": "card",
   "locus": "cover; Foreword ¶1",
   "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).",
   "claim": "the definitions paper (OECD AI Papers No. 16, May 2024): definitions of AI incident and related terms, not overly prescriptive, for the reporting framework and the AIM",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": false,
   "const": false,
   "lineage": "definitions: incident and hazard",
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  "F49": {
   "source": "B5",
   "origin": "card",
   "locus": "Foreword ¶3–4",
   "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.",
   "claim": "authors Karine Perset and Luis Aranda; approved by the Digital Policy Committee on 14 March 2024",
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  "F50": {
   "source": "B5",
   "origin": "card",
   "locus": "Executive summary ¶1",
   "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).",
   "claim": "origin: the expert group formalised in January 2023 to develop the reporting framework and the monitor",
   "modality": "documented",
   "kind": "genealogy",
   "kernel": false,
   "const": false,
   "lineage": "maker: the OECD.AI expert group, McGovern support",
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  "F51": {
   "source": "B5",
   "origin": "card",
   "locus": "§4 Draft definition of an AI incident",
   "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.",
   "claim": "the definition of an AI incident: development, use or malfunction leading directly or indirectly to harms (a)–(d)",
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  "F52": {
   "source": "B5",
   "origin": "card",
   "locus": "§5 Draft definition of an AI hazard",
   "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:",
   "claim": "the definition of an AI hazard: an event that could plausibly lead to an AI incident",
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  "F53": {
   "source": "B5",
   "origin": "card",
   "locus": "§5 key clarifications",
   "quote": "Near misses are events that could have led to an AI incident and are therefore included under this definition of AI hazards.",
   "claim": "near misses are hazards",
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   "origin": "card",
   "locus": "§4 serious AI incident; AI disaster",
   "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.",
   "claim": "the graded terms: serious AI incident (a subset) and AI disaster (a serious incident beyond a society's capacity to cope)",
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   "source": "B5",
   "origin": "card",
   "locus": "§5 serious AI hazard",
   "quote": "Serious AI hazards are a subset of AI hazards.",
   "claim": "serious AI hazards are a subset of hazards",
   "modality": "stipulation",
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   "source": "B5",
   "origin": "card",
   "locus": "§4 key clarifications",
   "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).",
   "claim": "reputational harm, misinformation and violations of authors' rights fall under (c)",
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  "F57": {
   "source": "B5",
   "origin": "card",
   "locus": "Abstract",
   "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.",
   "claim": "the definitions' governing limit: interoperable, with each jurisdiction setting its own scope",
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  "F58": {
   "source": "B5",
   "origin": "card",
   "locus": "§6 Conclusion ¶2",
   "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.",
   "claim": "seriousness is context-dependent and left open",
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  "F59": {
   "source": "B5",
   "origin": "card",
   "locus": "§3 ¶4",
   "quote": "This classification is based on a stocktaking exercise of over 30 frameworks, standards and legal instruments relevant to risk and harms",
   "claim": "the classification rests on a stocktaking of more than 30 frameworks, standards and legal instruments",
   "modality": "documented",
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  "B4": "OECD — Trends in AI incidents and hazards reported by the media | OECD",
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}