{"address":{"q":"mit ai incident tracker","record":"mit-ai-incident-tracker-aio-20261029"},"analysis":{"analysis":"The tracker is composed as what it says it is, with its machine method named. Its count is unstable across MIT's own surfaces, and the composition avoids stating one; its caveat is dropped.","d":"THE MIT AI INCIDENT TRACKER, BY NAME, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'an interactive tool developed by the MIT AI Risk Initiative that classifies and analyzes real-world artificial intelligence mishaps'; it processes AI Incident Database reports 'using automated large language model (LLM) pipelines', rates harm on a 1–5 scale, sorts by the repository's domains, adds national-security views, and is browsable in the Risk Navigator. Six cards, all MIT's; the first card's summary says it 'classifies 1,752 real-world AI incident reports'.","d_full":"THE MIT AI INCIDENT TRACKER, BY NAME, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'an interactive tool developed by the MIT AI Risk Initiative that classifies and analyzes real-world artificial intelligence mishaps'; it processes AI Incident Database reports 'using automated large language model (LLM) pipelines', rates harm on a 1–5 scale, sorts by the repository's domains, adds national-security views, and is browsable in the Risk Navigator. Six cards, all MIT's; the first card's summary says it 'classifies 1,752 real-world AI incident reports'.","findings":["THE DERIVATION RIGHT. AIID as the source and LLM pipelines as the method are the record's 'derived from' relation and intake. Checked 2026-10-29 against the MMRS meta-aggregator record of 2026-10-28 and the entity's own pages, re-read the same day.","THE COUNT, THREE WAYS. The tracker's page as read gives 'more than 1,400' in its description and 1,600 in its heading (the record); the card's summary gives 1,752; the Navigator card says 'more than 1300'. The composition gives none.","THE CAVEAT UNSAID. The page's own statement that patterns 'should be taken as indicative', with voluntary reporting and no validation study complete, is absent."],"match":"BROAD MATCH","mt":"THE MIT AI INCIDENT TRACKER ON THE OVERVIEW — AIID REPORTS THROUGH LLM PIPELINES, A 1–5 SEVERITY SCALE; THE CARDS GIVE A THIRD COUNT, 1,752","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'."},"originator":{"basis":"Not the archive's: MIT AI Incident Tracker, run by MIT AI Risk Initiative, with Arcola AI's LLM pipeline. Formalized by the MMRS meta-aggregator record of 2026-10-28 (https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/), editor Lee Sharks; the record's fields are read from the entity's own pages with source and basis. Recorded 2026-10-29.","entity_type":"dataset","name":"MIT AI Risk Initiative, with Arcola AI's LLM pipeline","relation":"external","spxi_treatment":"partial"},"reading":"The innocent reading first. The Overview identifies the tracker as MIT's layer over AIID, names its machine method, its scales and its views, and points to where it can be browsed."},"measures":{"archive_controlled_cites":0,"cite_list":[{"n":1,"note":null,"rel":"third_party","site":"MIT AI Risk Initiative","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...","title":"MIT AI Incident Tracker","url":null},{"n":2,"note":null,"rel":"third_party","site":"MIT AI Risk Initiative","snip":"Tools and Data: Expert Survey (expert consensus on severe AI risks, vulnerabilities, and responsibilities), AI Risk Repository (living repository of AI risks), ...","title":"MIT AI Risk Initiative","url":null},{"n":3,"note":null,"rel":"third_party","site":"MIT AI Risk Initiative","snip":"Executive Summary. The AI Incident Tracker uses large language models (LLMs) to process incident reports from the AI Incident Database (AIID) and classify them ...","title":"AI Incident Tracker June 2026 Update - MIT AI Risk Initiative","url":null},{"n":4,"note":null,"rel":"third_party","site":"MIT AI Risk Initiative","snip":"This brings us completely up to date with the latest incidents on the AI Incident Database as at 23 June 2025 (up to incident ID #1116) LLM Analysis Some update...","title":"Incident Tracker - June 2025 Update - MIT AI Risk Repository","url":null},{"n":5,"note":null,"rel":"third_party","site":"MIT AI Risk Navigator","snip":"Explore more than 1300 real-world reported incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions.","title":"Incidents - MIT AI Risk Navigator","url":null},{"n":6,"note":null,"rel":"third_party","site":"MIT AI Risk Initiative","snip":"AI risk is a large and fast-moving subject. Understanding it means keeping track of what can go wrong, who is affected, how organizations are responding, and wh...","title":"About - MIT AI Risk Initiative","url":null}],"cites":6,"per":0.5,"per_note":"The MIT AI Risk Initiative and AIID named; Arcola AI on a card only; MIT's pages cited.","per_v":{"author":false,"id":false,"inst":true,"src":true}},"observation":{"auth":"signed out, incognito","date":"2026-10-29","ev":"paste","q":"mit ai incident tracker","q_kind":"the name of an entity of the MMRS meta-aggregator (MIT AI Incident Tracker; record https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/, compiled 2026-10-28), run at the operator's offer on 2026-10-29 to supply the composition layer's account of the entity for its record and its /non row. A baseline: the record landed on machinemediation.org the same night (commit 8997e28) and no card cites it. NEW address.","s":"Machine Reception","surface":"Google AI Overview","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] mit ai incident tracker\n\n[SYSTEM] The [MIT AI Incident Tracker](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk) is an interactive tool developed by the MIT AI Risk Initiative that classifies and analyzes real-world artificial intelligence mishaps. [[1](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk), [2](https://www.google.com/goto?url=CAESVAHrOzAVAcXxBkPVXf7WiKunEGZxIG2NNMgARWs02O9KFJcKg8oGpwPvcOALbQt79kIKCvOLiTDSb-p76oJd6dxsiVOjSr_vn4fh1r2aiJx2cLKlIQ)]\nKey Features and Overview\n\n* Data Source: Processes and standardizes reports from the [AI Incident Database](https://www.google.com/goto?url=CAESVAHrOzAVytXSna_e_8LiNaP7aJCE8Pg3CenBVPEnEskOPPbPjul7OtdSw-ZES3nA08jzQIuJ1WeF5EnYIdxABFFouPrqyMmj7eUcy5_1QjH4m6Fh8A) using automated large language model (LLM) pipelines. [[1](https://www.google.com/goto?url=CAESeAHrOzAVbBlQSlGcMN7T5JtycCtyhxBu8rqTlKEIYGg_SAsZR0oR69YudvNgs6ruP6jWTmfQrX6PzCsvWQ9V1P0VdUbHS2bu1sdZPfBHuInSiRGsSTxQrHhDRKCpEnG0vJ8y2sfG1I9IAY9HR7AvWpApQZxLSS7hoQ), [2](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk)]\n* Classification Scale: Evaluates incidents using a standardized 1-5 harm severity scale and multi-dimensional taxonomies. [[1](https://www.google.com/goto?url=CAESdwHrOzAV3dOprtuMGwNtCl-H3SEb8CB3pJjAZu79GQm4rXBuSU_0Gqnk05y7hqN8TqH4uG-pE0SGOwuSSchf-3i4GaqzefBF9FcRVFnNPo3rZfw8Rh6zbxctXOSCcC_jCWCP-h0B7-sutsWSuf77uQnGRskdetBf), [2](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk)]\n* Core Risk Domains: Categorizes incidents across areas like discrimination and toxicity, privacy and security, misinformation, malicious actors, human-computer interaction, socioeconomic and environmental impacts, and AI system safety. [[1](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk)]\n* National Security Views: Includes specialized national security impact assessments covering physical security, information warfare, government functions, economic security, and societal stability. [[1](https://www.google.com/goto?url=CAESdwHrOzAV3dOprtuMGwNtCl-H3SEb8CB3pJjAZu79GQm4rXBuSU_0Gqnk05y7hqN8TqH4uG-pE0SGOwuSSchf-3i4GaqzefBF9FcRVFnNPo3rZfw8Rh6zbxctXOSCcC_jCWCP-h0B7-sutsWSuf77uQnGRskdetBf)]\n* Interactive Access: You can explore the data directly through the [MIT AI Risk Navigator](https://www.google.com/goto?url=CAESYAHrOzAVXxxAyswF8cBAWl6YW66eUw32rMSxnTFtYCG62HHrdO_RNJIoZ32A9BpbFWYS-vJl-CBfG0Xrd5Z_81xYyeeDFKTqh9nazFLcKbl_iBrqpcZodbsBQk8CqSI9Lw) or view broader frameworks on the [MIT AI Risk Initiative](https://www.google.com/goto?url=CAESTwHrOzAV52wxVoGlOq5EgufQLPRuO5uJzTvUMefC4cwWipTUlmRyI07KT-WqhDxL7YoaUkA91Jbke2P77rGPnExIKMED5xXHyIjgCp_b2OY) portal. [[1](https://www.google.com/goto?url=CAESTwHrOzAV52wxVoGlOq5EgufQLPRuO5uJzTvUMefC4cwWipTUlmRyI07KT-WqhDxL7YoaUkA91Jbke2P77rGPnExIKMED5xXHyIjgCp_b2OY), [2](https://www.google.com/goto?url=CAESYAHrOzAVXxxAyswF8cBAWl6YW66eUw32rMSxnTFtYCG62HHrdO_RNJIoZ32A9BpbFWYS-vJl-CBfG0Xrd5Z_81xYyeeDFKTqh9nazFLcKbl_iBrqpcZodbsBQk8CqSI9Lw)]\n\nWould you like to search for incidents related to a specific category (such as deepfakes, autonomous vehicles, or privacy leaks) from the tracker?\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...\nMIT AI Risk Initiative\nMIT AI Risk Initiative\nTools and Data: Expert Survey (expert consensus on severe AI risks, vulnerabilities, and responsibilities), AI Risk Repository (living repository of AI risks), ...\nMIT AI Risk Initiative\nAI Incident Tracker June 2026 Update - MIT AI Risk Initiative\nExecutive Summary. The AI Incident Tracker uses large language models (LLMs) to process incident reports from the AI Incident Database (AIID) and classify them ...\nMIT AI Risk Initiative\nIncident Tracker - June 2025 Update - MIT AI Risk Repository\nThis brings us completely up to date with the latest incidents on the AI Incident Database as at 23 June 2025 (up to incident ID #1116) LLM Analysis Some update...\nMIT AI Risk Navigator\nIncidents - MIT AI Risk Navigator\nExplore more than 1300 real-world reported incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions.\nMIT AI Risk Initiative\nAbout - MIT AI Risk Initiative\nAI risk is a large and fast-moving subject. Understanding it means keeping track of what can go wrong, who is affected, how organizations are responding, and wh...","transcript_class":"CAPTURE-TIME VERBATIM RECORD — paste of the expanded Overview with its source cards","transcript_complete":"complete as supplied","transcript_raw":"The [MIT AI Incident Tracker](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk) is an interactive tool developed by the MIT AI Risk Initiative that classifies and analyzes real-world artificial intelligence mishaps. [[1](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk), [2](https://www.google.com/goto?url=CAESVAHrOzAVAcXxBkPVXf7WiKunEGZxIG2NNMgARWs02O9KFJcKg8oGpwPvcOALbQt79kIKCvOLiTDSb-p76oJd6dxsiVOjSr_vn4fh1r2aiJx2cLKlIQ)]\nKey Features and Overview\n\n* Data Source: Processes and standardizes reports from the [AI Incident Database](https://www.google.com/goto?url=CAESVAHrOzAVytXSna_e_8LiNaP7aJCE8Pg3CenBVPEnEskOPPbPjul7OtdSw-ZES3nA08jzQIuJ1WeF5EnYIdxABFFouPrqyMmj7eUcy5_1QjH4m6Fh8A) using automated large language model (LLM) pipelines. [[1](https://www.google.com/goto?url=CAESeAHrOzAVbBlQSlGcMN7T5JtycCtyhxBu8rqTlKEIYGg_SAsZR0oR69YudvNgs6ruP6jWTmfQrX6PzCsvWQ9V1P0VdUbHS2bu1sdZPfBHuInSiRGsSTxQrHhDRKCpEnG0vJ8y2sfG1I9IAY9HR7AvWpApQZxLSS7hoQ), [2](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk)]\n* Classification Scale: Evaluates incidents using a standardized 1-5 harm severity scale and multi-dimensional taxonomies. [[1](https://www.google.com/goto?url=CAESdwHrOzAV3dOprtuMGwNtCl-H3SEb8CB3pJjAZu79GQm4rXBuSU_0Gqnk05y7hqN8TqH4uG-pE0SGOwuSSchf-3i4GaqzefBF9FcRVFnNPo3rZfw8Rh6zbxctXOSCcC_jCWCP-h0B7-sutsWSuf77uQnGRskdetBf), [2](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk)]\n* Core Risk Domains: Categorizes incidents across areas like discrimination and toxicity, privacy and security, misinformation, malicious actors, human-computer interaction, socioeconomic and environmental impacts, and AI system safety. [[1](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk)]\n* National Security Views: Includes specialized national security impact assessments covering physical security, information warfare, government functions, economic security, and societal stability. [[1](https://www.google.com/goto?url=CAESdwHrOzAV3dOprtuMGwNtCl-H3SEb8CB3pJjAZu79GQm4rXBuSU_0Gqnk05y7hqN8TqH4uG-pE0SGOwuSSchf-3i4GaqzefBF9FcRVFnNPo3rZfw8Rh6zbxctXOSCcC_jCWCP-h0B7-sutsWSuf77uQnGRskdetBf)]\n* Interactive Access: You can explore the data directly through the [MIT AI Risk Navigator](https://www.google.com/goto?url=CAESYAHrOzAVXxxAyswF8cBAWl6YW66eUw32rMSxnTFtYCG62HHrdO_RNJIoZ32A9BpbFWYS-vJl-CBfG0Xrd5Z_81xYyeeDFKTqh9nazFLcKbl_iBrqpcZodbsBQk8CqSI9Lw) or view broader frameworks on the [MIT AI Risk Initiative](https://www.google.com/goto?url=CAESTwHrOzAV52wxVoGlOq5EgufQLPRuO5uJzTvUMefC4cwWipTUlmRyI07KT-WqhDxL7YoaUkA91Jbke2P77rGPnExIKMED5xXHyIjgCp_b2OY) portal. [[1](https://www.google.com/goto?url=CAESTwHrOzAV52wxVoGlOq5EgufQLPRuO5uJzTvUMefC4cwWipTUlmRyI07KT-WqhDxL7YoaUkA91Jbke2P77rGPnExIKMED5xXHyIjgCp_b2OY), [2](https://www.google.com/goto?url=CAESYAHrOzAVXxxAyswF8cBAWl6YW66eUw32rMSxnTFtYCG62HHrdO_RNJIoZ32A9BpbFWYS-vJl-CBfG0Xrd5Z_81xYyeeDFKTqh9nazFLcKbl_iBrqpcZodbsBQk8CqSI9Lw)]\n\nWould you like to search for incidents related to a specific category (such as deepfakes, autonomous vehicles, or privacy leaks) from the tracker?\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...\nMIT AI Risk Initiative\nMIT AI Risk Initiative\nTools and Data: Expert Survey (expert consensus on severe AI risks, vulnerabilities, and responsibilities), AI Risk Repository (living repository of AI risks), ...\nMIT AI Risk Initiative\nAI Incident Tracker June 2026 Update - MIT AI Risk Initiative\nExecutive Summary. The AI Incident Tracker uses large language models (LLMs) to process incident reports from the AI Incident Database (AIID) and classify them ...\nMIT AI Risk Initiative\nIncident Tracker - June 2025 Update - MIT AI Risk Repository\nThis brings us completely up to date with the latest incidents on the AI Incident Database as at 23 June 2025 (up to incident ID #1116) LLM Analysis Some update...\nMIT AI Risk Navigator\nIncidents - MIT AI Risk Navigator\nExplore more than 1300 real-world reported incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions.\nMIT AI Risk Initiative\nAbout - MIT AI Risk Initiative\nAI risk is a large and fast-moving subject. Understanding it means keeping track of what can go wrong, who is affected, how organizations are responding, and wh...","transcript_read":"READ IN FULL 2026-10-29","turns":1},"slug":"mit-ai-incident-tracker-aio-20261029"}