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  "name": "Capture Registry (longitudinal observation)",
  "level": 0,
  "level_name": "Measurement",
  "url": "https://www.spxi.dev/methods/#m-0-9",
  "inventory": "https://www.spxi.dev/methods/",
  "statement": "SPXI instrument: method 0.9 of 87, level 0 (Measurement), Capture Registry (longitudinal observation).",
  "also": []
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 "slug": "what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029",
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 "q": "what counts as an incident in the ai incident database",
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   "title": "MIT AI Incident Tracker",
   "snip": "Overview: MIT AI Incident Tracker classifies 1,752 real-world AI incident reports from the AI Incident Database using Arcola AI's LLM pipeline, MIT Risk Reposit...",
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   "url": null,
   "title": "Editorial and Research Guide for the AI Incident Database",
   "snip": "Quick Definitions The core terms used throughout the guide. Section 2 provides fuller boundary guidance and examples. Term Working definition AI incident An all...",
   "note": null
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  {
   "n": 3,
   "site": "AI Incident Database",
   "rel": "third_party",
   "url": null,
   "title": "Defining an “AI Incident”",
   "snip": "The commercial air travel industry owes much of its increasing safety to systematically analyzing and archiving past accidents and incidents within a shared dat...",
   "note": null
  },
  {
   "n": 4,
   "site": "LinkedIn·TIME",
   "rel": "third_party",
   "url": null,
   "title": "AI Incidents Rise 50% Year-Over-Year: AI Incident Database | TIME posted on the ...",
   "snip": null,
   "note": "video, 1:35"
  },
  {
   "n": 5,
   "site": "YouTube·HarrietHacks",
   "rel": "third_party",
   "url": null,
   "title": "Examples of AI real attacks",
   "snip": null,
   "note": "video, 6:59"
  },
  {
   "n": 6,
   "site": "AI Incident Database",
   "rel": "third_party",
   "url": null,
   "title": "The details about who and why for database - AI Incident Database",
   "snip": "What is an Incident? The initial set of more than 1,000 incident reports have been intentionally broad in nature. Current examples include, An autonomous car ki...",
   "note": null
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   "site": "The Association for the Advancement of Artificial Intelligence",
   "rel": "third_party",
   "url": null,
   "title": "Lessons for Editors of AI Incidents from the AI Incident Database - AAAI Publications",
   "snip": "The AI Incident Database. Inspired by the CVE pro- gram and other incident reporting practices, the AI Incident Database (AIID) catalogs AI incidents with the g...",
   "note": null
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   "site": "ACM Digital Library",
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   "url": null,
   "title": "Why We Need to Know More: Exploring the State of AI Incident Documentation ...",
   "snip": "Users can submit examples of incidents and controversies; these submissions are included based on adherence to criteria such as relevance, fairness, and accurac...",
   "note": null
  },
  {
   "n": 9,
   "site": "YouTube·International Association for Safe & Ethical AI",
   "rel": "third_party",
   "url": null,
   "title": "Establishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell",
   "snip": null,
   "note": "video, 19m"
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 "transcript": "[Google AI Overview at the query, signed out and incognito (operator defaults of 2026-10-01 and 2026-10-14), 2026-10-29. The paste is the Overview expanded (the 'AI Mode Conversation' view, with the source cards that follow the answer); the 'You said' line carries the query twice, run together, and is given once here. Inline links are Google redirect URLs, kept as pasted. The closing disclaimer cut.]\n\n[QUERENT] what counts as an incident in the ai incident database\n\n[SYSTEM] In the [AI Incident Database (AIID)](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr), an incident is defined as an alleged harm or near-harm event to people, property, or the environment where an AI system is implicated. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\nTo keep the dataset structured and avoid arbitrary tracking, the database uses an adaptive, specific set of criteria outlined in its [AIID Editor's Guide](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr). For a report to be accepted as a true incident record, it must meet three core baseline thresholds: [[1](https://www.google.com/goto?url=CAESaAHrOzAV6BQeJp83eTepbdmQvhX3c5Z0UVleE-oXaDq4-udfCIavF3bp3Rg5e1P1_FOMaQoVmcpCr4aNEQJRXjzzZAe15TjFfk1acSvZ0OvPWDJhdXIsd71u3HYyPyxHNsQctwESmSSD), [2](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n1. The Core Criteria for Ingestion\n\n* Specific Event: It must describe a distinct, real-world occurrence with clear details on what happened, to whom, and where. Theoretical vulnerabilities do not qualify. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n* AI Implication: The AI system must have played a meaningful role, where the harm likely would not have occurred without the AI's output, omission, or use. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n* Harm or Near-Harm: The event must result in an adverse effect or a credible chain of events nearly producing one. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n\n2. Common Categories of AI Harms Tracked\nUsing frameworks like CSET's taxonomy, incidents are grouped into categories such as physical safety, discrimination and fairness, financial loss, privacy and security breaches, and misinformation or scams. [[1](https://www.google.com/goto?url=CAESWgHrOzAVbDGbL9ZrISjGczvBRGgNlXUpeyxIqiF1zdmUDUeRDOVBHrwDECego7en1qEez5GdWT7N88OtRzsKHQ-P7wTsqpGOSg6shbg_eBUw13j4i2_WwvgKLA), [2](https://www.google.com/goto?url=CAESYgHrOzAVt1sAglYOAmqqebvrVHYpTO9b7rSQGhqiiAgEYgkbKsjmtsKuCdMfMEZSalmIBEAZ0qHQniNymPYHa0GxTCB9ALCcPrPsiFm5PNMendugeWLYPCZyP5yJ5_yIOigP), [3](https://www.google.com/goto?url=CAESbwHrOzAVXAkpQRoYrEDozOKncjeuH8dVoxQKH0FxvfIlMjzxLUCEfyaxbwVpHnjm_yuwaPKbTD4wWrUqJlBzxjn0PZqnlXWswHQjdehjIa15zkmQ9CPpDD9zB8HLFufjsSmePh26i6f3LarUPGGZXA), [4](https://www.google.com/goto?url=CAESYwHrOzAVqulVGrCNThw0OnihTieQxcNBnsDItwViWWL0-wMsopzzljdgeOpcCjrkyOWCy91a98lFAiXvygIF34_5ypIvuzm7g2hllo9e30E0YwIHdKwIcjvbecX84wA8F1H5ag), [5](https://www.google.com/goto?url=CAESdAHrOzAVrse2Z6pJbOuuOfH4mSLitAzLBYSDDHV__8tT91mA7gli-3UPemR86fxUxqgMsBPBurdbkvZhh-ALh0N6eToGGR7rOJVHiaFH3kqmUe04Fx3tEcmd3FHghJetp2SVb0ZdtmU3qFUGgdYLLxU1xkCr), [6](https://www.google.com/goto?url=CAESaAHrOzAVrgKwwsV8VsN02OkopPFAqIpxwe-A3uD4FPRnlMK4p6ferrk25sdbXhqDEE1VFoJYucFX73UliV3VD4CuPI_OCaLkDpZwm2rrEYQXIZ809TpawoQCg6hcVHSrk-MvRkoLCKzW), [7](https://www.google.com/goto?url=CAESrQEB6zswFVGL85wCMBo6_hPhbMsFJ1u4tJCOTz2AXBGvT8TnjLF5nRIDiVNst9TD3Yq2Vk3ZQygmdGpdDOgaFjLBrb3_Fl0scfM63J87k63xYso4npUVegMe7d4tRg9gV_xGCKYV5FdPEjXEMHXn29AngN2d4yncVxkoDISWLS0ov4NL0xiEoTcsr2FXp8-5I096YeY5_gw9yYlVbY3r1sXkNSfeY-wtPM19EZMTjQ)]\n3. Key Distinctions: \"Incidents\" vs. \"Issues\" vs. \"Variants\"\nThe database differentiates entries based on event status: [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n\n* AI Incident: A real event where AI caused or nearly caused harm.\n* AI Issue: A potential harm or vulnerability that has not yet materialized in the real world.\n* AI Incident Variant: A separate occurrence involving the same AI system, similar causes, and matching outcomes as an existing record. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n\nAre you trying to submit a specific case to the database, or are you researching a particular type of AI safety failure (such as LLMs, deepfakes, or robotics) for your own project?\nMIT AI Risk Initiative\nMIT AI Incident Tracker\nOverview: MIT AI Incident Tracker classifies 1,752 real-world AI incident reports from the AI Incident Database using Arcola AI's LLM pipeline, MIT Risk Reposit...\nAI Incident Database\nEditorial and Research Guide for the AI Incident Database\nQuick Definitions The core terms used throughout the guide. Section 2 provides fuller boundary guidance and examples. Term Working definition AI incident An all...\nAI Incident Database\nDefining an “AI Incident”\nThe commercial air travel industry owes much of its increasing safety to systematically analyzing and archiving past accidents and incidents within a shared dat...\nLinkedIn·TIME\n[1:35](https://www.google.com/goto?url=CAESrQEB6zswFVGL85wCMBo6_hPhbMsFJ1u4tJCOTz2AXBGvT8TnjLF5nRIDiVNst9TD3Yq2Vk3ZQygmdGpdDOgaFjLBrb3_Fl0scfM63J87k63xYso4npUVegMe7d4tRg9gV_xGCKYV5FdPEjXEMHXn29AngN2d4yncVxkoDISWLS0ov4NL0xiEoTcsr2FXp8-5I096YeY5_gw9yYlVbY3r1sXkNSfeY-wtPM19EZMTjQ)\nAI Incidents Rise 50% Year-Over-Year: AI Incident Database | TIME posted on the ...\nYouTube·HarrietHacks\n6:59\nExamples of AI real attacks\nAI Incident Database\nThe details about who and why for database - AI Incident Database\nWhat is an Incident? The initial set of more than 1,000 incident reports have been intentionally broad in nature. Current examples include, An autonomous car ki...\nThe Association for the Advancement of Artificial Intelligence\nLessons for Editors of AI Incidents from the AI Incident Database - AAAI Publications\nThe AI Incident Database. Inspired by the CVE pro- gram and other incident reporting practices, the AI Incident Database (AIID) catalogs AI incidents with the g...\nACM Digital Library\nWhy We Need to Know More: Exploring the State of AI Incident Documentation ...\nUsers can submit examples of incidents and controversies; these submissions are included based on adherence to criteria such as relevance, fairness, and accurac...\nYouTube·International Association for Safe & Ethical AI\n19m\nEstablishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell",
 "transcript_raw": "In the [AI Incident Database (AIID)](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr), an incident is defined as an alleged harm or near-harm event to people, property, or the environment where an AI system is implicated. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\nTo keep the dataset structured and avoid arbitrary tracking, the database uses an adaptive, specific set of criteria outlined in its [AIID Editor's Guide](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr). For a report to be accepted as a true incident record, it must meet three core baseline thresholds: [[1](https://www.google.com/goto?url=CAESaAHrOzAV6BQeJp83eTepbdmQvhX3c5Z0UVleE-oXaDq4-udfCIavF3bp3Rg5e1P1_FOMaQoVmcpCr4aNEQJRXjzzZAe15TjFfk1acSvZ0OvPWDJhdXIsd71u3HYyPyxHNsQctwESmSSD), [2](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n1. The Core Criteria for Ingestion\n\n* Specific Event: It must describe a distinct, real-world occurrence with clear details on what happened, to whom, and where. Theoretical vulnerabilities do not qualify. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n* AI Implication: The AI system must have played a meaningful role, where the harm likely would not have occurred without the AI's output, omission, or use. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n* Harm or Near-Harm: The event must result in an adverse effect or a credible chain of events nearly producing one. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n\n2. Common Categories of AI Harms Tracked\nUsing frameworks like CSET's taxonomy, incidents are grouped into categories such as physical safety, discrimination and fairness, financial loss, privacy and security breaches, and misinformation or scams. [[1](https://www.google.com/goto?url=CAESWgHrOzAVbDGbL9ZrISjGczvBRGgNlXUpeyxIqiF1zdmUDUeRDOVBHrwDECego7en1qEez5GdWT7N88OtRzsKHQ-P7wTsqpGOSg6shbg_eBUw13j4i2_WwvgKLA), [2](https://www.google.com/goto?url=CAESYgHrOzAVt1sAglYOAmqqebvrVHYpTO9b7rSQGhqiiAgEYgkbKsjmtsKuCdMfMEZSalmIBEAZ0qHQniNymPYHa0GxTCB9ALCcPrPsiFm5PNMendugeWLYPCZyP5yJ5_yIOigP), [3](https://www.google.com/goto?url=CAESbwHrOzAVXAkpQRoYrEDozOKncjeuH8dVoxQKH0FxvfIlMjzxLUCEfyaxbwVpHnjm_yuwaPKbTD4wWrUqJlBzxjn0PZqnlXWswHQjdehjIa15zkmQ9CPpDD9zB8HLFufjsSmePh26i6f3LarUPGGZXA), [4](https://www.google.com/goto?url=CAESYwHrOzAVqulVGrCNThw0OnihTieQxcNBnsDItwViWWL0-wMsopzzljdgeOpcCjrkyOWCy91a98lFAiXvygIF34_5ypIvuzm7g2hllo9e30E0YwIHdKwIcjvbecX84wA8F1H5ag), [5](https://www.google.com/goto?url=CAESdAHrOzAVrse2Z6pJbOuuOfH4mSLitAzLBYSDDHV__8tT91mA7gli-3UPemR86fxUxqgMsBPBurdbkvZhh-ALh0N6eToGGR7rOJVHiaFH3kqmUe04Fx3tEcmd3FHghJetp2SVb0ZdtmU3qFUGgdYLLxU1xkCr), [6](https://www.google.com/goto?url=CAESaAHrOzAVrgKwwsV8VsN02OkopPFAqIpxwe-A3uD4FPRnlMK4p6ferrk25sdbXhqDEE1VFoJYucFX73UliV3VD4CuPI_OCaLkDpZwm2rrEYQXIZ809TpawoQCg6hcVHSrk-MvRkoLCKzW), [7](https://www.google.com/goto?url=CAESrQEB6zswFVGL85wCMBo6_hPhbMsFJ1u4tJCOTz2AXBGvT8TnjLF5nRIDiVNst9TD3Yq2Vk3ZQygmdGpdDOgaFjLBrb3_Fl0scfM63J87k63xYso4npUVegMe7d4tRg9gV_xGCKYV5FdPEjXEMHXn29AngN2d4yncVxkoDISWLS0ov4NL0xiEoTcsr2FXp8-5I096YeY5_gw9yYlVbY3r1sXkNSfeY-wtPM19EZMTjQ)]\n3. Key Distinctions: \"Incidents\" vs. \"Issues\" vs. \"Variants\"\nThe database differentiates entries based on event status: [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n\n* AI Incident: A real event where AI caused or nearly caused harm.\n* AI Issue: A potential harm or vulnerability that has not yet materialized in the real world.\n* AI Incident Variant: A separate occurrence involving the same AI system, similar causes, and matching outcomes as an existing record. [[1](https://www.google.com/goto?url=CAESYgHrOzAVoFaATyoT135GKwbgq1JTDhfjO5oDwhNCRt79QowTeOLgpXR1P-Ml7G3U6TsvpiFS6fFlQKDaG1bB24O5tgmNHxARRp3XcX-TPcfQmF7jmShB8mWk3NVW0MDhCtNr)]\n\nAre you trying to submit a specific case to the database, or are you researching a particular type of AI safety failure (such as LLMs, deepfakes, or robotics) for your own project?\nMIT AI Risk Initiative\nMIT AI Incident Tracker\nOverview: MIT AI Incident Tracker classifies 1,752 real-world AI incident reports from the AI Incident Database using Arcola AI's LLM pipeline, MIT Risk Reposit...\nAI Incident Database\nEditorial and Research Guide for the AI Incident Database\nQuick Definitions The core terms used throughout the guide. Section 2 provides fuller boundary guidance and examples. Term Working definition AI incident An all...\nAI Incident Database\nDefining an “AI Incident”\nThe commercial air travel industry owes much of its increasing safety to systematically analyzing and archiving past accidents and incidents within a shared dat...\nLinkedIn·TIME\n[1:35](https://www.google.com/goto?url=CAESrQEB6zswFVGL85wCMBo6_hPhbMsFJ1u4tJCOTz2AXBGvT8TnjLF5nRIDiVNst9TD3Yq2Vk3ZQygmdGpdDOgaFjLBrb3_Fl0scfM63J87k63xYso4npUVegMe7d4tRg9gV_xGCKYV5FdPEjXEMHXn29AngN2d4yncVxkoDISWLS0ov4NL0xiEoTcsr2FXp8-5I096YeY5_gw9yYlVbY3r1sXkNSfeY-wtPM19EZMTjQ)\nAI Incidents Rise 50% Year-Over-Year: AI Incident Database | TIME posted on the ...\nYouTube·HarrietHacks\n6:59\nExamples of AI real attacks\nAI Incident Database\nThe details about who and why for database - AI Incident Database\nWhat is an Incident? The initial set of more than 1,000 incident reports have been intentionally broad in nature. Current examples include, An autonomous car ki...\nThe Association for the Advancement of Artificial Intelligence\nLessons for Editors of AI Incidents from the AI Incident Database - AAAI Publications\nThe AI Incident Database. Inspired by the CVE pro- gram and other incident reporting practices, the AI Incident Database (AIID) catalogs AI incidents with the g...\nACM Digital Library\nWhy We Need to Know More: Exploring the State of AI Incident Documentation ...\nUsers can submit examples of incidents and controversies; these submissions are included based on adherence to criteria such as relevance, fairness, and accurac...\nYouTube·International Association for Safe & Ethical AI\n19m\nEstablishing Effective Serious Incident Reporting Frameworks – Jimmy Farrell\nAI responses may include mistakes. [Learn more](https://support.google.com/websearch?p=ai_overviews)",
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 "sf_derived": null,
 "reading": "The innocent reading first. Asked what counts, the Overview answers from the database's own guide: the definition, the threshold for admission, the taxonomy for sorting, and the difference between an incident, an issue that has not occurred and a variant of a known incident.",
 "analysis": "At the question of its unit the Overview reads the database's own guide and keeps the guide's distinctions, with one compression: the five-part admission test arrives as three parts, and the evidence requirement is gone. The record's 'what its selection leaves out' rests on that test; the composition carries the test's harm condition and loses its evidence condition.",
 "d": "THE AI INCIDENT DATABASE'S UNIT, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: the definition from the Editor's Guide verbatim ('an alleged harm or near-harm event to people, property, or the environment where an AI system is implicated'), the adaptive criteria, 'three core baseline thresholds' (specific event, AI implication, harm or near-harm), CSET's taxonomy for categories, and the three entry kinds (incident, issue, variant). Nine cards, five of them the database's own pages (the guide, 'Defining an AI Incident', the about page) or papers on it (AAAI, ACM).",
 "d_full": "THE AI INCIDENT DATABASE'S UNIT, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: the definition from the Editor's Guide verbatim ('an alleged harm or near-harm event to people, property, or the environment where an AI system is implicated'), the adaptive criteria, 'three core baseline thresholds' (specific event, AI implication, harm or near-harm), CSET's taxonomy for categories, and the three entry kinds (incident, issue, variant). Nine cards, five of them the database's own pages (the guide, 'Defining an AI Incident', the about page) or papers on it (AAAI, ACM).",
 "d_truncated": false,
 "links": [
  {
   "url": "https://www.alexanarch.org/captures/what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029/",
   "authority": "canonical",
   "note": "the capture's own record page; cite this form"
  },
  {
   "url": "https://www.alexanarch.org/captures/#what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029",
   "authority": "gallery",
   "note": "the canonical gallery, anchored by slug"
  },
  {
   "url": "https://www.godkinggoogle.com/captures/#what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  },
  {
   "url": "https://www.leesharks.com/captures/#what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  },
  {
   "url": "https://www.machinemediation.org/captures/#what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  }
 ],
 "imgs": [],
 "img_urls": [],
 "cite": "https://www.alexanarch.org/captures/what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029/",
 "citable_unit": "address — the exact issued string on one surface, per the Surface Rule (MANUS, 2026-08-15)",
 "addr_id": "ADDR-7858f0aa2fe5",
 "obs_id": "OBS-bd807797964a",
 "n_observations": 1,
 "observations": [],
 "dates": [
  "2026-10-29"
 ],
 "defects": [],
 "findings": [
  "THE DEFINITION EXACT. The incident, the issue and the variant are the Editor's Guide's definitions (§2.1) in substance, the first verbatim. 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.",
  "FIVE QUESTIONS MADE THREE. The guide's test asks five things: a specific event, an identifiable AI system, a causal AI role, harm or credible near harm, and adequate evidence (§3). The composition gives three thresholds, folding the identifiable system into 'AI implication' and dropping adequate evidence.",
  "THE REPORT ABSENT AGAIN. As at the database's name, the report as the source attached to an incident is not stated; the unit is the incident alone."
 ],
 "series": null,
 "other_slugs": null,
 "collisions": null,
 "oq": null,
 "rounds": null,
 "turns": 1,
 "rerun": null,
 "rerun_alt": null,
 "heteronym": null,
 "model_attribution": null,
 "operator_disclosure": null,
 "longitudinal_priors": null,
 "longitudinal_successors": null,
 "related_deposits": [],
 "intended": null,
 "originator": {
  "name": "Responsible AI Collaborative",
  "relation": "external",
  "entity_type": "dataset",
  "spxi_treatment": "partial",
  "basis": "Not the archive's: AI Incident Database, run by Responsible AI Collaborative. Formalized by the MMRS meta-aggregator record of 2026-10-28 (https://www.machinemediation.org/aggregators/ai-incident-database/), editor Lee Sharks; the record's fields are read from the entity's own pages with source and basis. Recorded 2026-10-29."
 },
 "notes": {
  "prompts": "One query. Sent by the operator on 2026-10-29 with nineteen others, one per entity of the MMRS meta-aggregator, at his offer to run them: 'ill run captures if you want, just list the semantic addresses'."
 },
 "notice": null,
 "axn_root": "AXN:0AB7.CAPTURE",
 "axn": "AXN:0AB7.CAPTURE.🗿♌🪟🐝🌅⚡",
 "record_url": "https://www.alexanarch.org/captures/what-counts-as-an-incident-in-the-ai-incident-database-aio-20261029/"
}