{ "spxi_instrument": { "method": "0.9", "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": [] }, "slug": "mit-ai-risk-repository-aio-20261029", "date": "2026-10-29", "surface": "Google AI Overview", "surface_basis": "Operator default of 2026-10-01 (runs begin in the Overview unless he says AI Mode); the 'AI Mode Conversation' paste is the Overview expanded (operator, 2026-10-18).", "surfaces": [ "Google AI Overview" ], "auth": "signed out, incognito", "auth_basis": "Operator default of 2026-10-14.", "ev": "paste", "mt": "THE MIT AI RISK REPOSITORY ON THE OVERVIEW — 1,700 RISKS FROM 65 FRAMEWORKS, THE SEVEN DOMAINS AND 24 SUBDOMAINS, THE CAUSAL TAXONOMY; THE NAVIGATOR NAMED", "match": "BROAD MATCH", "match_note": null, "s": "Machine Reception", "q": "mit ai risk repository", "q_kind": "the name of an entity of the MMRS meta-aggregator (MIT AI Risk Repository; record https://www.machinemediation.org/aggregators/mit-ai-risk-repository/, 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.", "cites": 10, "cite_list": [ { "n": 1, "site": "MIT AI Risk Initiative", "rel": "third_party", "url": null, "title": "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), ...", "note": null }, { "n": 2, "site": "MIT AI Risk Initiative", "rel": "third_party", "url": null, "title": "AI Risk Repository Report updated (April 2025)", "snip": "AI Risk Repository Report updated (April 2025) * What's new in this update Integration of 22 new AI risk frameworks, bringing the total number of included docum...", "note": null }, { "n": 3, "site": "MIT Initiative on the Digital Economy", "rel": "third_party", "url": null, "title": "MIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy", "snip": "Overview: MIT FutureTech’s AI Risk Repository catalogs 777 risks across 43 AI frameworks, providing source evidence, citations, and dual search taxonomies (doma...", "note": null }, { "n": 4, "site": "MIT FutureTech", "rel": "third_party", "url": null, "title": "FutureTech", "snip": "We construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develo...", "note": null }, { "n": 5, "site": "MIT AI Risk Navigator", "rel": "third_party", "url": null, "title": "MIT AI Risk Navigator", "snip": "MIT AI Risk Navigator Home Browse MIT AI Risk Navigator Explore the full landscape of AI risk in one place. The Navigator connects MIT's AI Risk Repository data...", "note": null }, { "n": 6, "site": "arXiv", "rel": "third_party", "url": null, "title": "[2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "snip": "Overview: The AI Risk Repository catalogs and standardizes fragmented artificial intelligence risk terminology by analyzing 74 major risk frameworks containing ...", "note": null }, { "n": 7, "site": "MIT AI Risk Initiative", "rel": "third_party", "url": null, "title": "Repository Update: December 2025", "snip": "Foundational Challenges in Assuring Alignment and Safety of Large Language Models A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Str...", "note": null }, { "n": 8, "site": "MIT AI Risk Initiative", "rel": "third_party", "url": null, "title": "About", "snip": "No single dataset captures all of this, so we maintain several connected workstreams, each answering a different question. * AI Risk Repository What are the ris...", "note": null }, { "n": 9, "site": "LinkedIn", "rel": "third_party", "url": null, "title": "MIT AI Risk Initiative | LinkedIn", "snip": "The initiative includes the MIT AI Risk Repository, a living database of more than 1,700 AI risks, the AI Incident Tracker, a collaboration with the Responsible...", "note": null }, { "n": 10, "site": "ScienceDirect.com", "rel": "third_party", "url": null, "title": "The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence - ScienceDirect", "snip": "* Practical implications for technology management. Our AI Risk Repository provides concrete tools for managing AI's societal impact across multiple stakeholder...", "note": null } ], "archive_controlled_cites": 0, "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 risk repository\n\n[SYSTEM] The [MIT AI Risk Repository](https://www.google.com/goto?url=CAESTwHrOzAVzAsEkjRPqis_Zzgy92qO6F3tmBvgjtuUs58DdhTBaxedxEPqgRTuYwMTN2NTt-BqUqcWh4mhJAoKt-ASIezdHuKzbPccHrmoQg0) is a living, open-access database developed by MIT FutureTech that catalogs and standardizes artificial intelligence risks from dozens of global frameworks. [[1](https://www.google.com/goto?url=CAESTwHrOzAVzAsEkjRPqis_Zzgy92qO6F3tmBvgjtuUs58DdhTBaxedxEPqgRTuYwMTN2NTt-BqUqcWh4mhJAoKt-ASIezdHuKzbPccHrmoQg0), [2](https://www.google.com/goto?url=CAEShwEB6zswFYB__Br6vrJAk0QiMgjyJCtHj7AX6YnJauDb08vZFK_yDQUBQaRGksT1WfTc3zQsEzVHzCYmpgFja4KX17GTGOi-i-60hrUWC4M_RbwAZC4QZikGcgRCjcYI2rRzYhReTPOVkt1XYf4fDARSopztJOyct-mPvhMORUaSo3r5KFmWN9I), [3](https://www.google.com/goto?url=CAESdQHrOzAVHSmzTVex5rfdqNxwBhLCXug__yTwEUZGTCfpZdrY5vP4JZJV-y_ZGa-Kc3d1v2fSod-rFaGXObNGNdjawwNEkf10ERGY3B28BbT-uHvr9KNvY_60vuNo7ae3Sy-_AnHdh4sOdBhoa56aR7pYLIz_4Q)]\nKey Features and Scope\n\n* Scale: The database compiles over 1,700 distinct AI risks extracted from more than 65 published frameworks, industry guidelines, and policy documents. [[1](https://www.google.com/goto?url=CAESkAEB6zswFXimyfJJSRuxK0wjkLoQJAaCk3jLOBZX10rQWIEAEoayFZl1QY3Uf2gzkE7jpDCjyKEvHYx3zQplvD54UX3yo9sRxikpBaK3YMAJBm0cy7_FV1Plly04e5-z2aqKS4DV2CHdlZ7KZ7UgMMXS9ICWZJ0P4N9EvCQzmuXgjADgLPN8YHnL6qhH6pM3OAc), [2](https://www.google.com/goto?url=CAEScQHrOzAVX-iQCWDCcVVcFzbfswt7arP1K7JHbYMyd_o6KJaR0fy8rpGnF4H2t1yETJ-5LEyt1MTShdyBGdpc04YpwiAuuZXyfgQGerKDOHQfN1s_UR0YR2RjG0bn7lINKGH9JQ-AlC5_1mkouxSMmGHd)]\n* Causal Taxonomy: Classifies risks based on the responsible entity (human vs. AI), intent (intentional vs. unintentional), and timing (pre-deployment vs. post-deployment). Findings indicate that roughly 38% to 42% of risks stem directly from AI systems, while a large share originates from human decisions during design and deployment. [[1](https://www.google.com/goto?url=CAESewHrOzAVQDBrpJnadXp88WKlflunwUZiSv9-Pr91z1vRVaNxzjyzoYWRHgMEogB28zaD-g-uJn_7nak4qPU_E5cWj5bwJj_uYaC9TNabI3dA4jJvhBOH6MyEWgh8Lmof9Xv0NBTceryrU06q6Z_WMnOCIwIevYkYMuvN8g), [2](https://www.google.com/goto?url=CAESWAHrOzAVOQDIzrB6cEf0T5gsEPJH1rBn628OYpXAcGtK0CwKGSkxTAdGkiAsRElisiPgDm5ahlAEUe4Sn5WwC3c5AWhVxUgMcWBHBM65CzQpw9uaRPwiEiY), [3](https://www.google.com/goto?url=CAESdQHrOzAVHSmzTVex5rfdqNxwBhLCXug__yTwEUZGTCfpZdrY5vP4JZJV-y_ZGa-Kc3d1v2fSod-rFaGXObNGNdjawwNEkf10ERGY3B28BbT-uHvr9KNvY_60vuNo7ae3Sy-_AnHdh4sOdBhoa56aR7pYLIz_4Q)]\n* Domain Taxonomy: Organizes risks into 7 core domains and 24 subdomains:\n 1. Discrimination & toxicity\n 2. Privacy & security\n 3. Misinformation\n 4. Malicious actors & misuse\n 5. Human-computer interaction\n 6. Socioeconomic & environmental\n 7. AI system safety, failures, & limitations [[1](https://www.google.com/goto?url=CAEScwHrOzAVaSxWbWa_CEIrc6ZNGAkhxv3XEAdCvSLaaoNqeR_0irQmOHF3gfDNraaPyJ34oBnHt8dBciHZae6jZGERUBoJzULWZD3Ab5sOyNr0ep-gZXAg1cusABV3LU1y8_4VHbhs6lO9mtLMn_LjPIxqCiw), [2](https://www.google.com/goto?url=CAESdQHrOzAVHSmzTVex5rfdqNxwBhLCXug__yTwEUZGTCfpZdrY5vP4JZJV-y_ZGa-Kc3d1v2fSod-rFaGXObNGNdjawwNEkf10ERGY3B28BbT-uHvr9KNvY_60vuNo7ae3Sy-_AnHdh4sOdBhoa56aR7pYLIz_4Q)]\n\nConnected Tools\n\n* [MIT AI Risk Navigator](https://www.google.com/goto?url=CAESVwHrOzAVVkU1PAZVWuI1j4pI6qUcKaC2IgP6hbq57HPdCiSZZafGwYhvngxWSJqeH5kYZHr5KhLF--3eVzwivkze75A0RPhWrJoTNf65LiyzTs5RxgL5xw): An interactive web tool that links the repository to real-world AI incidents, global governance frameworks (such as the EU AI Act), and risk mitigations. [[1](https://www.google.com/goto?url=CAESVwHrOzAVVkU1PAZVWuI1j4pI6qUcKaC2IgP6hbq57HPdCiSZZafGwYhvngxWSJqeH5kYZHr5KhLF--3eVzwivkze75A0RPhWrJoTNf65LiyzTs5RxgL5xw), [2](https://www.google.com/goto?url=CAESTwHrOzAVzAsEkjRPqis_Zzgy92qO6F3tmBvgjtuUs58DdhTBaxedxEPqgRTuYwMTN2NTt-BqUqcWh4mhJAoKt-ASIezdHuKzbPccHrmoQg0), [3](https://www.google.com/goto?url=CAESewHrOzAVQDBrpJnadXp88WKlflunwUZiSv9-Pr91z1vRVaNxzjyzoYWRHgMEogB28zaD-g-uJn_7nak4qPU_E5cWj5bwJj_uYaC9TNabI3dA4jJvhBOH6MyEWgh8Lmof9Xv0NBTceryrU06q6Z_WMnOCIwIevYkYMuvN8g)]\n* Incident Tracker: Collaborates with the AI Incident Database to connect abstract risks to documented real-world harms. [[1](https://www.google.com/goto?url=CAESVAHrOzAVSszqIW6XXawdcppgtO9EF6-hg_ZlY-BBwrOujqPEv817AUoHuRQ2wa_VRDETDslzjFfLrgCsBLtdNpC0RKkjmPe2pJBeGefa1S_vwOkg-w), [2](https://www.google.com/goto?url=CAEScQHrOzAVX-iQCWDCcVVcFzbfswt7arP1K7JHbYMyd_o6KJaR0fy8rpGnF4H2t1yETJ-5LEyt1MTShdyBGdpc04YpwiAuuZXyfgQGerKDOHQfN1s_UR0YR2RjG0bn7lINKGH9JQ-AlC5_1mkouxSMmGHd)]\n\nWould you like to explore how to use the repository for a specific industry audit, regulatory compliance check (such as the EU AI Act), or risk assessment?\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 Risk Repository Report updated (April 2025)\nAI Risk Repository Report updated (April 2025) * What's new in this update Integration of 22 new AI risk frameworks, bringing the total number of included docum...\nMIT Initiative on the Digital Economy\nMIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy\nOverview: MIT FutureTech’s AI Risk Repository catalogs 777 risks across 43 AI frameworks, providing source evidence, citations, and dual search taxonomies (doma...\nMIT FutureTech\nFutureTech\nWe construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develo...\nMIT AI Risk Navigator\nMIT AI Risk Navigator\nMIT AI Risk Navigator Home Browse MIT AI Risk Navigator Explore the full landscape of AI risk in one place. The Navigator connects MIT's AI Risk Repository data...\narXiv\n[2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence\nOverview: The AI Risk Repository catalogs and standardizes fragmented artificial intelligence risk terminology by analyzing 74 major risk frameworks containing ...\nMIT AI Risk Initiative\nRepository Update: December 2025\nFoundational Challenges in Assuring Alignment and Safety of Large Language Models A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Str...\nMIT AI Risk Initiative\nAbout\nNo single dataset captures all of this, so we maintain several connected workstreams, each answering a different question. * AI Risk Repository What are the ris...\nLinkedIn\nMIT AI Risk Initiative | LinkedIn\nThe initiative includes the MIT AI Risk Repository, a living database of more than 1,700 AI risks, the AI Incident Tracker, a collaboration with the Responsible...\nScienceDirect.com\nThe AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence - ScienceDirect\n* Practical implications for technology management. Our AI Risk Repository provides concrete tools for managing AI's societal impact across multiple stakeholder...", "transcript_raw": "The [MIT AI Risk Repository](https://www.google.com/goto?url=CAESTwHrOzAVzAsEkjRPqis_Zzgy92qO6F3tmBvgjtuUs58DdhTBaxedxEPqgRTuYwMTN2NTt-BqUqcWh4mhJAoKt-ASIezdHuKzbPccHrmoQg0) is a living, open-access database developed by MIT FutureTech that catalogs and standardizes artificial intelligence risks from dozens of global frameworks. [[1](https://www.google.com/goto?url=CAESTwHrOzAVzAsEkjRPqis_Zzgy92qO6F3tmBvgjtuUs58DdhTBaxedxEPqgRTuYwMTN2NTt-BqUqcWh4mhJAoKt-ASIezdHuKzbPccHrmoQg0), [2](https://www.google.com/goto?url=CAEShwEB6zswFYB__Br6vrJAk0QiMgjyJCtHj7AX6YnJauDb08vZFK_yDQUBQaRGksT1WfTc3zQsEzVHzCYmpgFja4KX17GTGOi-i-60hrUWC4M_RbwAZC4QZikGcgRCjcYI2rRzYhReTPOVkt1XYf4fDARSopztJOyct-mPvhMORUaSo3r5KFmWN9I), [3](https://www.google.com/goto?url=CAESdQHrOzAVHSmzTVex5rfdqNxwBhLCXug__yTwEUZGTCfpZdrY5vP4JZJV-y_ZGa-Kc3d1v2fSod-rFaGXObNGNdjawwNEkf10ERGY3B28BbT-uHvr9KNvY_60vuNo7ae3Sy-_AnHdh4sOdBhoa56aR7pYLIz_4Q)]\nKey Features and Scope\n\n* Scale: The database compiles over 1,700 distinct AI risks extracted from more than 65 published frameworks, industry guidelines, and policy documents. [[1](https://www.google.com/goto?url=CAESkAEB6zswFXimyfJJSRuxK0wjkLoQJAaCk3jLOBZX10rQWIEAEoayFZl1QY3Uf2gzkE7jpDCjyKEvHYx3zQplvD54UX3yo9sRxikpBaK3YMAJBm0cy7_FV1Plly04e5-z2aqKS4DV2CHdlZ7KZ7UgMMXS9ICWZJ0P4N9EvCQzmuXgjADgLPN8YHnL6qhH6pM3OAc), [2](https://www.google.com/goto?url=CAEScQHrOzAVX-iQCWDCcVVcFzbfswt7arP1K7JHbYMyd_o6KJaR0fy8rpGnF4H2t1yETJ-5LEyt1MTShdyBGdpc04YpwiAuuZXyfgQGerKDOHQfN1s_UR0YR2RjG0bn7lINKGH9JQ-AlC5_1mkouxSMmGHd)]\n* Causal Taxonomy: Classifies risks based on the responsible entity (human vs. AI), intent (intentional vs. unintentional), and timing (pre-deployment vs. post-deployment). Findings indicate that roughly 38% to 42% of risks stem directly from AI systems, while a large share originates from human decisions during design and deployment. [[1](https://www.google.com/goto?url=CAESewHrOzAVQDBrpJnadXp88WKlflunwUZiSv9-Pr91z1vRVaNxzjyzoYWRHgMEogB28zaD-g-uJn_7nak4qPU_E5cWj5bwJj_uYaC9TNabI3dA4jJvhBOH6MyEWgh8Lmof9Xv0NBTceryrU06q6Z_WMnOCIwIevYkYMuvN8g), [2](https://www.google.com/goto?url=CAESWAHrOzAVOQDIzrB6cEf0T5gsEPJH1rBn628OYpXAcGtK0CwKGSkxTAdGkiAsRElisiPgDm5ahlAEUe4Sn5WwC3c5AWhVxUgMcWBHBM65CzQpw9uaRPwiEiY), [3](https://www.google.com/goto?url=CAESdQHrOzAVHSmzTVex5rfdqNxwBhLCXug__yTwEUZGTCfpZdrY5vP4JZJV-y_ZGa-Kc3d1v2fSod-rFaGXObNGNdjawwNEkf10ERGY3B28BbT-uHvr9KNvY_60vuNo7ae3Sy-_AnHdh4sOdBhoa56aR7pYLIz_4Q)]\n* Domain Taxonomy: Organizes risks into 7 core domains and 24 subdomains:\n 1. Discrimination & toxicity\n 2. Privacy & security\n 3. Misinformation\n 4. Malicious actors & misuse\n 5. Human-computer interaction\n 6. Socioeconomic & environmental\n 7. AI system safety, failures, & limitations [[1](https://www.google.com/goto?url=CAEScwHrOzAVaSxWbWa_CEIrc6ZNGAkhxv3XEAdCvSLaaoNqeR_0irQmOHF3gfDNraaPyJ34oBnHt8dBciHZae6jZGERUBoJzULWZD3Ab5sOyNr0ep-gZXAg1cusABV3LU1y8_4VHbhs6lO9mtLMn_LjPIxqCiw), [2](https://www.google.com/goto?url=CAESdQHrOzAVHSmzTVex5rfdqNxwBhLCXug__yTwEUZGTCfpZdrY5vP4JZJV-y_ZGa-Kc3d1v2fSod-rFaGXObNGNdjawwNEkf10ERGY3B28BbT-uHvr9KNvY_60vuNo7ae3Sy-_AnHdh4sOdBhoa56aR7pYLIz_4Q)]\n\nConnected Tools\n\n* [MIT AI Risk Navigator](https://www.google.com/goto?url=CAESVwHrOzAVVkU1PAZVWuI1j4pI6qUcKaC2IgP6hbq57HPdCiSZZafGwYhvngxWSJqeH5kYZHr5KhLF--3eVzwivkze75A0RPhWrJoTNf65LiyzTs5RxgL5xw): An interactive web tool that links the repository to real-world AI incidents, global governance frameworks (such as the EU AI Act), and risk mitigations. [[1](https://www.google.com/goto?url=CAESVwHrOzAVVkU1PAZVWuI1j4pI6qUcKaC2IgP6hbq57HPdCiSZZafGwYhvngxWSJqeH5kYZHr5KhLF--3eVzwivkze75A0RPhWrJoTNf65LiyzTs5RxgL5xw), [2](https://www.google.com/goto?url=CAESTwHrOzAVzAsEkjRPqis_Zzgy92qO6F3tmBvgjtuUs58DdhTBaxedxEPqgRTuYwMTN2NTt-BqUqcWh4mhJAoKt-ASIezdHuKzbPccHrmoQg0), [3](https://www.google.com/goto?url=CAESewHrOzAVQDBrpJnadXp88WKlflunwUZiSv9-Pr91z1vRVaNxzjyzoYWRHgMEogB28zaD-g-uJn_7nak4qPU_E5cWj5bwJj_uYaC9TNabI3dA4jJvhBOH6MyEWgh8Lmof9Xv0NBTceryrU06q6Z_WMnOCIwIevYkYMuvN8g)]\n* Incident Tracker: Collaborates with the AI Incident Database to connect abstract risks to documented real-world harms. [[1](https://www.google.com/goto?url=CAESVAHrOzAVSszqIW6XXawdcppgtO9EF6-hg_ZlY-BBwrOujqPEv817AUoHuRQ2wa_VRDETDslzjFfLrgCsBLtdNpC0RKkjmPe2pJBeGefa1S_vwOkg-w), [2](https://www.google.com/goto?url=CAEScQHrOzAVX-iQCWDCcVVcFzbfswt7arP1K7JHbYMyd_o6KJaR0fy8rpGnF4H2t1yETJ-5LEyt1MTShdyBGdpc04YpwiAuuZXyfgQGerKDOHQfN1s_UR0YR2RjG0bn7lINKGH9JQ-AlC5_1mkouxSMmGHd)]\n\nWould you like to explore how to use the repository for a specific industry audit, regulatory compliance check (such as the EU AI Act), or risk assessment?\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 Risk Repository Report updated (April 2025)\nAI Risk Repository Report updated (April 2025) * What's new in this update Integration of 22 new AI risk frameworks, bringing the total number of included docum...\nMIT Initiative on the Digital Economy\nMIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy\nOverview: MIT FutureTech’s AI Risk Repository catalogs 777 risks across 43 AI frameworks, providing source evidence, citations, and dual search taxonomies (doma...\nMIT FutureTech\nFutureTech\nWe construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develo...\nMIT AI Risk Navigator\nMIT AI Risk Navigator\nMIT AI Risk Navigator Home Browse MIT AI Risk Navigator Explore the full landscape of AI risk in one place. The Navigator connects MIT's AI Risk Repository data...\narXiv\n[2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence\nOverview: The AI Risk Repository catalogs and standardizes fragmented artificial intelligence risk terminology by analyzing 74 major risk frameworks containing ...\nMIT AI Risk Initiative\nRepository Update: December 2025\nFoundational Challenges in Assuring Alignment and Safety of Large Language Models A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Str...\nMIT AI Risk Initiative\nAbout\nNo single dataset captures all of this, so we maintain several connected workstreams, each answering a different question. * AI Risk Repository What are the ris...\nLinkedIn\nMIT AI Risk Initiative | LinkedIn\nThe initiative includes the MIT AI Risk Repository, a living database of more than 1,700 AI risks, the AI Incident Tracker, a collaboration with the Responsible...\nScienceDirect.com\nThe AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence - ScienceDirect\n* Practical implications for technology management. Our AI Risk Repository provides concrete tools for managing AI's societal impact across multiple stakeholder...", "transcript_class": "CAPTURE-TIME VERBATIM RECORD — paste of the expanded Overview with its source cards", "transcript_complete": "complete as supplied", "transcript_read": "READ IN FULL 2026-10-29", "per": 0.25, "per_corrected": null, "per_v": { "author": false, "inst": true, "id": true, "src": true }, "per_note": "MIT FutureTech named; no person; arXiv:2408.12622 on a card; MIT's own pages cited.", "availability": null, "per_dim": null, "per_dim_v1_0": null, "sf": null, "sf_derived": null, "reading": "The innocent reading first. The Overview gives the repository's size, its two taxonomies with the domains named, and the tools built on it, from MIT's pages and the repository's paper.", "analysis": "The repository is composed at a finer grain than the record holds, its taxonomy named domain by domain, and the composition surfaces a sibling the census lacked.", "d": "THE MIT AI RISK REPOSITORY, BY NAME, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'a living, open-access database developed by MIT FutureTech' cataloguing over 1,700 risks from more than 65 frameworks; the causal taxonomy (entity, intent, timing), with 38% to 42% of risks attributed to AI systems; the domain taxonomy of seven domains and 24 subdomains, listed; connected tools, the MIT AI Risk Navigator and the Incident Tracker. Ten cards, seven MIT's own, with the arXiv paper (2408.12622) and ScienceDirect.", "d_full": "THE MIT AI RISK REPOSITORY, BY NAME, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'a living, open-access database developed by MIT FutureTech' cataloguing over 1,700 risks from more than 65 frameworks; the causal taxonomy (entity, intent, timing), with 38% to 42% of risks attributed to AI systems; the domain taxonomy of seven domains and 24 subdomains, listed; connected tools, the MIT AI Risk Navigator and the Incident Tracker. Ten cards, seven MIT's own, with the arXiv paper (2408.12622) and ScienceDirect.", "d_truncated": false, "links": [ { "url": "https://www.alexanarch.org/captures/mit-ai-risk-repository-aio-20261029/", "authority": "canonical", "note": "the capture's own record page; cite this form" }, { "url": "https://www.alexanarch.org/captures/#mit-ai-risk-repository-aio-20261029", "authority": "gallery", "note": "the canonical gallery, anchored by slug" }, { "url": "https://www.godkinggoogle.com/captures/#mit-ai-risk-repository-aio-20261029", "authority": "mirror", "note": "a window that renders from the archive's registry; may lag a deploy" }, { "url": "https://www.leesharks.com/captures/#mit-ai-risk-repository-aio-20261029", "authority": "mirror", "note": "a window that renders from the archive's registry; may lag a deploy" }, { "url": "https://www.machinemediation.org/captures/#mit-ai-risk-repository-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/mit-ai-risk-repository-aio-20261029/", "citable_unit": "address — the exact issued string on one surface, per the Surface Rule (MANUS, 2026-08-15)", "addr_id": "ADDR-5762c6084031", "obs_id": "OBS-21a7cea64b70", "n_observations": 1, "observations": [], "dates": [ "2026-10-29" ], "defects": [], "findings": [ "THE SIZE AND THE TAXONOMY. 1,700 risks from 65 frameworks is the record's count (version 4, December 2025); the seven domains and the causal taxonomy are the record's 'domain taxonomy with subdomains' given at full grain. 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 COUNTS ACROSS VERSIONS. The cards carry three sizes: 777 risks from 43 frameworks (the first release, MIT IDE), 74 frameworks (the arXiv overview card) and 1,700 from 65; the composition gives the current one.", "A NEIGHBOUR NOT IN THE CENSUS. The MIT AI Risk Navigator, 'an interactive web tool that links the repository to real-world AI incidents, global governance frameworks... and risk mitigations', is a third MIT surface; the census holds the repository and the tracker. Added as a candidate." ], "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": "MIT AI Risk Initiative (MIT FutureTech)", "relation": "external", "entity_type": "dataset", "spxi_treatment": "partial", "basis": "Not the archive's: MIT AI Risk Repository, run by MIT AI Risk Initiative (MIT FutureTech). Formalized by the MMRS meta-aggregator record of 2026-10-28 (https://www.machinemediation.org/aggregators/mit-ai-risk-repository/), 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:0AAF.CAPTURE", "axn": "AXN:0AAF.CAPTURE.🚀🕌🔒🏠✖️🌉", "record_url": "https://www.alexanarch.org/captures/mit-ai-risk-repository-aio-20261029/" }