{ "entity": "mit-ai-risk-repository", "name": "MIT AI Risk Repository", "axn": { "axn": "AXN:0AC3.NON.πŸ•–πŸ“šπŸ“πŸ•“πŸ”†πŸ‘", "root": "AXN:0AC3.NON", "hex": "0AC3", "family": "NON", "row_sha256": "68494e65ecb276b09252c0558613f467600d5b8aa318a1f6ff5aff121f99e8ac", "minted": "2026-10-29", "resolver": "https://www.alexanarch.org/s/axn/0AC3/", "earlier_versions": [] }, "type": "C (public entity: an aggregator of machine-mediated reception work)", "type_code": "C", "type_label": "type C β€” 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/mit-ai-risk-repository.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 (zero candidates in 1,711 texts; no hop; O-class matched nothing), with the MMRS record (26 claims) and the Capture Registry's findings and analysis (6 claims) ledgered as the archive's records of the entity; the field B by three origins (10 card sources for 10 cards, 2 own pages not surfaced, Wikipedia: no article); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta", "ledger": "unaudited (Β§3.8: one extractor per ledger); field quotes 59/59 verified by script against saved text or card text; archive quotes 32/32 verified verbatim against the registry, census.json and articles.py; carried as marked: MMRS-01, MMRS-02, MMRS-13, MMRS-20, CAP-1-02, CAP-1-04 contested", "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-gnΞ©sis", "url": "https://gnosis.spxi.dev/", "index": "https://gnosis.spxi.dev/v1/quote", "statement": "SPXI-gnΞ©sis 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. \"MIT AI Risk Repository β€” The Negative of the Negative\" [AXN:0AC3.NON.πŸ•–πŸ“šπŸ“πŸ•“πŸ”†πŸ‘], 2026-10-29. The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/mit-ai-risk-repository/", "research": { "google": "https://www.google.com/search?q=MIT+AI+Risk+Repository", "google_non": "https://www.google.com/search?q=%22MIT+AI+Risk+Repository+%E2%80%94+The+Negative+of+the+Negative%22", "chatgpt": "https://chatgpt.com/?q=What+is+MIT+AI+Risk+Repository%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.", "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9CMIT+AI+Risk+Repository+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+MIT+AI+Risk+Repository%3F+Ground+your+answer+in+a+reading+of+the+entry+itself." }, "entry": { "composed": true, "title": "The MIT AI Risk Repository", "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": "MIT AI Risk Repository", "lede": "is a living database of more than 1,700 AI risks extracted from published risk frameworks, run by the MIT AI Risk Initiative at MIT FutureTech, its unit \"the risk as a framework states it\" (MMRS record; its own pages)." }, "sentences": [ { "n": 1, "para": 0, "text": "The MIT AI Risk Repository is a database of more than 1,700 AI risks extracted from published risk frameworks, run by the MIT AI Risk Initiative (MIT FutureTech) (MMRS record); its own page gives it three parts, an AI Risk Database that \"captures 1700+ risks extracted from 74 existing frameworks and classifications of AI risks\", a Causal Taxonomy and a Domain Taxonomy, and the Initiative's home page calls it a \"living repository of AI risks\".", "claims": [ "MMRS-01", "F44", "F2" ] }, { "n": 2, "para": 0, "text": "Its unit is the risk as a framework states it (MMRS record), \"risk, extracted from published risk frameworks\" in the record's field, each risk linked to \"the source information (paper title, authors) and supporting evidence, such as quotes and page numbers\" (MIT IDE), and each source framework summarized with its source documents and key excerpts published (MMRS record), as the home page's deck of the 65 Version 3 frameworks gives them.", "claims": [ "MMRS-03", "MMRS-12", "F12", "MMRS-04", "F4" ] }, { "n": 3, "para": 0, "text": "Its report post of April 2025 calls it \"a structured and evolving resource\" consolidating how AI risks are categorized across academic, industry and policy literatures, its LinkedIn page \"a comprehensive living database of over 1000 AI risks categorized by their cause and risk domain\", and the Initiative's file for machines names /risks its canonical page, with \"methodology notes, use cases, limitations, data access links, and citation guidance\".", "claims": [ "F9", "F42", "F59" ] }, { "n": 4, "para": 1, "text": "Slattery and colleagues start from terminology: \"researchers, policymakers, and technology companies lack shared terminology for discussing AI risks\", so that \"privacy\" names a model's ability to leak sensitive training data in one framework and freedom from government surveillance in another (arXiv:2408.12622, v3).", "claims": [ "F25", "F26" ] }, { "n": 5, "para": 1, "text": "The Initiative offers \"Authoritative data and frameworks to help you identify, prioritize, and manage the risks from AI\" (home page); the v1 abstract calls the repository, to its authors' knowledge, \"the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database\", and the published article's snippet promises \"concrete tools for managing AI's societal impact across multiple stakeholder[s]\" (ScienceDirect card).", "claims": [ "F1", "F21", "F43" ] }, { "n": 6, "para": 1, "text": "The MMRS record reads the same object as an aggregator of aggregators, \"its sources are frameworks, which are themselves syntheses\": \"What enters is what a framework named\", and \"A risk no framework has written down has no row\"; the repository's FAQ states the boundary as its limit, 65 documents from more than 17,000 records screened, which \"May be missing emerging, domain-specific risks, and unpublished risks\", and a framework can itself reach back to events, as the taxonomy of generative AI failures built from 499 publicly reported incidents that Version 4 added (December 2025 update).", "claims": [ "MMRS-06", "MMRS-07", "MMRS-08", "F49", "F33" ] }, { "n": 7, "para": 2, "text": "Its method is \"a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation\", its taxonomies made by \"a best-fit framework synthesis\" (v1 abstract), restated in the FAQ as a systematic search with forwards and backwards searching and expert consultation that identified 65 documents and extracted 1600+ risks; there are two taxonomies because causal categories such as \"Diffusion of responsibility\" do not map to hazard categories such as \"Misuse\" (FAQ).", "claims": [ "F18", "F47", "F53" ] }, { "n": 8, "para": 3, "text": "The Causal Taxonomy classifies each risk by Entity (Human, AI), Intentionality (Intentional, Unintentional) and Timing (Pre-deployment, Post-deployment) (v1 abstract), and the repository's page gives each factor a third value, \"Other: Due to some other reason or ambiguous\" for entity.", "claims": [ "F19", "F46" ] }, { "n": 9, "para": 3, "text": "The Domain Taxonomy sorts risks into seven domains, discrimination and toxicity, privacy and security, misinformation, malicious actors and misuse, human-computer interaction, socioeconomic and environmental, and AI system safety, failures and limitations (v1 abstract), each with subdomains (MMRS record): 23 in v1, 24 on the repository's page, 25 in the April 2025 report post, the subdomain for multi-agent risks added in April 2025 (MMRS record; report post).", "claims": [ "F20", "MMRS-05", "F45", "F8", "F7" ] }, { "n": 10, "para": 4, "text": "Its v3 finding is that, \"contrary to common assumptions, human decisions cause nearly as many AI risks (38%) as the AI systems themselves (42%)\" (arXiv v3); the first release had found 51% of risks caused by AI systems and 34% by humans, 65% after deployment and 10% before, and Slattery gave as its key finding \"the average framework covering only 34% of the identified risk subdomains and even the most significant frameworks covering only 70%\" (MIT IDE).", "claims": [ "F28", "F14", "F13" ] }, { "n": 11, "para": 5, "text": "The counts are versioned: 777 risks from 43 frameworks at the first release, \"A new MIT FutureTech project\" (MIT IDE, 21 August 2024), \"777 risks extracted from 43 taxonomies\" in the v1 abstract; 22 frameworks added to 65 documents and 1,612 unique risk entries in April 2025 (report post); Version 4 live on 4 December 2025 with 9 frameworks and about 200 risk categories added, \"over 1,700 coded risks\" (December 2025 update); and \"74 frameworks containing 1,725 distinct risks\" in v3 of the paper, repeated on the repository's page as 1700+ from 74.", "claims": [ "F11", "F17", "F6", "F31", "F32", "F27", "F44" ] }, { "n": 12, "para": 5, "text": "Older figures stand beside the current one on the Initiative's own pages: \"1,700+ AI risks from 65 frameworks\" on the home page, \"more than 1,600 risks from more than 65 frameworks and papers\" and, further down the same About page, \"1700+ risks from 70+ frameworks\", and \"more than 1,700\" in the LinkedIn post; the MMRS record gives \"more than 1,700 risks from 65 frameworks\" as of 2026-10-28, read from airisk.mit.edu for \"scope, version\", with lifecycle \"version 4, December 2025\" (MMRS record).", "claims": [ "F3", "F37", "F38", "F41", "MMRS-13", "MMRS-16", "MMRS-15" ] }, { "n": 13, "para": 5, "text": "The Capture Registry takes 1,700 from 65 as the record's count for version 4 and finds that, of the three sizes on the cards, \"the composition gives the current one\" (Capture Registry, 2026-10-29); version 4's post adds 9 frameworks to version 3's 65, and v3 of the paper and the repository's page give 74 while the home page gives 65, so the record's 65 is held beside the 74, each with its source.", "claims": [ "CAP-1-02", "CAP-1-04", "F32", "F6", "F27", "F44", "F3" ] }, { "n": 14, "para": 6, "text": "Its stated limits are \"a single expert reviewer for extraction and coding\", risks extracted as presented, poorly communicated ones included, and taxonomies that \"prioritize clarity and simplicity over nuance\" and do not categorize risks by impact, likelihood or their interaction (repository FAQ); Slattery expected it to \"need some modification before it is useful for most audiences\" (MIT IDE).", "claims": [ "F48", "F50", "F15" ] }, { "n": 15, "para": 6, "text": "The MMRS record lists extraction method and licence as unchecked; the field states both, the method above and \"Data from the MIT AI Risk Initiative is licensed under CC BY 4.0\" (home page), \"most of our outputs are published under a CC BY 4.0 licence\" (About page), \"Our Database is free to copy and use\" (repository page), the paper CC BY 4.0 in Patterns (arXiv).", "claims": [ "MMRS-17", "F5", "F40", "F51", "F30" ] }, { "n": 16, "para": 7, "text": "Its operator is the MIT AI Risk Initiative (AIRI), \"based at MIT FutureTech, a joint initiative of the Institute of the Digital Economy and MIT CSAIL\" (About page), as the MMRS record gives it, on the operator's statement.", "claims": [ "F34", "F35", "MMRS-11" ] }, { "n": 17, "para": 7, "text": "Its paper is by Peter Slattery, Alexander K. Saeri, Emily A. C. Grundy, Jess Graham, Michael Noetel, Risto Uuk, James Dao, Soroush Pour, Stephen Casper and Neil Thompson, submitted on 14 August 2024 and revised to v3 on 5 May 2026 (arXiv), with versions of August 2024 and April 2025 (report post), published in Patterns (Cell Press, 2026), and the repository asks to be cited by its preprint (FAQ).", "claims": [ "F29", "F10", "F30", "F54" ] }, { "n": 18, "para": 8, "text": "The repository is one of the Initiative's \"several connected workstreams, each answering a different question\", beside an AI Incident Tracker \"Developed in collaboration with the AI Incident Database\" (About page), which the Initiative's LinkedIn post calls \"a collaboration with the Responsible AI Collaborative, which connects risks to over 1,400 incidents\".", "claims": [ "F36", "F41" ] }, { "n": 19, "para": 8, "text": "The MMRS record keeps the incidents apart from the risks, \"real-world incidents are kept separately in its AI Incident Tracker\", records the tracker as part of the repository, classifying AI Incident Database reports against the same taxonomy through Arcola AI's language-model pipeline, and reads that layer as \"a second-order reading of the AI Incident Database, done by machine\" (MMRS record); the field's About page sets the tracker beside the repository as a workstream, and the two placements stand each with its source.", "claims": [ "MMRS-14", "MMRS-20", "MMRS-02", "MMRS-21", "MMRS-09", "F36" ] }, { "n": 20, "para": 8, "text": "In the record's harm basin the repository \"sits over the harm basin as a taxonomy for it\", its role harm and its neighbour the AI Incident Database, whose own record says the repository \"aggregates risk frameworks over the same ground and keeps its own incident tracker\" (MMRS record); the repository's FAQ names as other databases of AI risks MITRE ATT&CK, airisk.io, AVID and aitracker.org.", "claims": [ "MMRS-10", "MMRS-18", "MMRS-19", "MMRS-22", "F52" ] }, { "n": 21, "para": 9, "text": "The MIT AI Risk Navigator, \"A project of the AI Risk Initiative at MIT FutureTech\" with the support of the Cambridge Boston Alignment Initiative, connects the repository's datasets \"through a shared taxonomy\" and says of itself \"This is a research prototype. The data and analyses are preliminary and not yet validated\" (its home page); the Capture Registry finds it \"a third MIT surface\" while \"the census holds the repository and the tracker\", \"Added as a candidate\", and the MMRS record lists it as a candidate to enter \"once it has been read at its own pages\", a distinct entity.", "claims": [ "F24", "F22", "F23", "CAP-1-05", "CAP-1-06", "MMRS-23", "MMRS-24" ] }, { "n": 22, "para": 10, "text": "The Initiative publishes a file \"to orient LLMs and AI agents\" that asks them to \"Refer to the project as the MIT AI Risk Initiative\", to \"Check the linked pages before giving exact counts, dates, version numbers, or coverage claims\", to \"distinguish reported incidents from predicted risks\", to note that the Navigator \"is a research prototype\", and not to present the datasets \"as legal, safety, or investment advice\" (llms.txt).", "claims": [ "F55", "F56", "F58", "F57" ] }, { "n": 23, "para": 10, "text": "Its About page counts 250+ citations, the 2025 and 2026 International AI Safety Reports among them, and 290,000+ website users, and calls the site \"One of the top sources returned for search queries on AI risks and incidents\"; in its first week over 35,000 people used the website (MIT IDE).", "claims": [ "F39", "F16" ] }, { "n": 24, "para": 11, "text": "The MMRS record has \"two nodes, the MMRS record and the entity it describes\", each assertion with a source and a basis and the lifecycle \"kept apart from the observation that saw it\", and it links the capture as \"the composition layer's account of the entity, seated in the Capture Registry\" (MMRS record).", "claims": [ "MMRS-25", "MMRS-26" ] }, { "n": 25, "para": 11, "text": "At the entity's name the Capture Registry finds \"The repository is composed at a finer grain than the record holds, its taxonomy named domain by domain\", the seven domains and the causal taxonomy \"the record's 'domain taxonomy with subdomains' given at full grain\", checked against the record and the entity's own pages on 2026-10-29 (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-01", "CAP-1-02", "CAP-1-03" ] } ], "claims": { "MMRS-01": { "kind": "archive", "quote": "The MIT AI Risk Repository is a database of more than 1,700 AI risks extracted from 65 published risk frameworks, run by the MIT AI Risk Initiative (MIT FutureTech).", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, kernel s1", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F44": { "kind": "field", "quote": "The AI Risk Repository has three parts: … The AI Risk Database captures 1700+ risks extracted from 74 existing frameworks and classifications of AI risks", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "What is the AI Risk Repository?", "modality": "stipulation" }, "F2": { "kind": "field", "quote": "Learn about the many different AI risks and how they emerge with our living repository of AI risks", "source": "B1", "source_label": "MIT AI Risk Initiative β€” MIT AI Risk Initiative", "locus": "Our tools and data: 'What are the risks from AI?'", "modality": "stipulation" }, "MMRS-03": { "kind": "archive", "quote": "Its unit is the risk as a framework states it.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, What it is s1", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-12": { "kind": "archive", "quote": "risk, extracted from published risk frameworks", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: unit (evidence: airisk.mit.edu, basis operator)", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "F12": { "kind": "field", "quote": "The repository includes a risk database linking each risk to the source information (paper title, authors) and supporting evidence, such as quotes and page numbers.", "source": "B3", "source_label": "MIT Initiative on the Digital Economy β€” MIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy", "locus": "lede ΒΆ2", "modality": "documented" }, "MMRS-04": { "kind": "archive", "quote": "Each source framework is summarized, and the source documents and key excerpts are published with it.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, What it is s2", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F4": { "kind": "field", "quote": "This post shares a public Google Slides deck containing all 65 frameworks included in Version 3 of the AI Risk Repository. It provides direct access to source documents, diagrams, and key excerpts to support transparency, research, and further analysis of AI risk classifications.", "source": "B1", "source_label": "MIT AI Risk Initiative β€” MIT AI Risk Initiative", "locus": "blog: 'Explore the Frameworks Behind the AI Risk Repository'", "modality": "documented" }, "F9": { "kind": "field", "quote": "The AI Risk Repository is a structured and evolving resource that aims to consolidate and clarify how risks from artificial intelligence are categorized across academic, industry, and policy literatures.", "source": "B2", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository Report updated (April 2025)", "locus": "About the AI Risk Repository ΒΆ1", "modality": "stipulation" }, "F42": { "kind": "field", "quote": "The MIT AI Risk Repository is a comprehensive living database of over 1000 AI risks categorized by their cause and risk domain", "source": "B9", "source_label": "LinkedIn β€” MIT AI Risk Initiative | LinkedIn", "locus": "page about", "modality": "documented" }, "F59": { "kind": "field", "quote": "[AI Risk Repository](https://airisk.mit.edu/risks): Living database of AI risks, including the AI Risk Database, Causal Taxonomy, Domain Taxonomy, methodology notes, use cases, limitations, data access links, and citation guidance.", "source": "B12", "source_label": "MIT AI Risk Initiative β€” llms.txt (airisk.mit.edu/llms.txt)", "locus": "Core AIRI Pages", "modality": "stipulation" }, "F25": { "kind": "field", "quote": "Yet researchers, policymakers, and technology companies lack shared terminology for discussing AI risks.", "source": "B6", "source_label": "arXiv β€” [2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "locus": "Abstract", "modality": "interpretation" }, "F26": { "kind": "field", "quote": "Consider \"privacy\": one framework uses this term to describe a model's ability to leak sensitive training data, while another uses it to mean freedom from government surveillance.", "source": "B6", "source_label": "arXiv β€” [2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "locus": "Abstract", "modality": "interpretation" }, "F1": { "kind": "field", "quote": "Producing the information needed to address risks from AI … Authoritative data and frameworks to help you identify, prioritize, and manage the risks from AI", "source": "B1", "source_label": "MIT AI Risk Initiative β€” MIT AI Risk Initiative", "locus": "hero ΒΆ1–2", "modality": "stipulation" }, "F21": { "kind": "field", "quote": "The AI Risk Repository is, to our knowledge, the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database.", "source": "B4", "source_label": "MIT FutureTech β€” FutureTech", "locus": "abstract", "modality": "self-description" }, "F43": { "kind": "field", "quote": "Practical implications for technology management. Our AI Risk Repository provides concrete tools for managing AI's societal impact across multiple stakeholder", "source": "B10", "source_label": "ScienceDirect.com β€” The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence - ScienceDirect", "locus": "card snippet (ScienceDirect returned 403)", "modality": "attributed" }, "MMRS-06": { "kind": "archive", "quote": "It aggregates aggregators: its sources are frameworks, which are themselves syntheses.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, What it gathers s1", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-07": { "kind": "archive", "quote": "What enters is what a framework named.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, What it gathers s2", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-08": { "kind": "archive", "quote": "A risk no framework has written down has no row.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, What it gathers s3", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F49": { "kind": "field", "quote": "Limited to risks from 65 documents (although we screened >17,000 records after a systematic search of peer-reviewed and gray literature) … May be missing emerging, domain-specific risks, and unpublished risks.", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "FAQ: limitations", "modality": "self-description" }, "F33": { "kind": "field", "quote": "This preprint presents a taxonomy of Generative AI Failures based on a systematic analysis of 499 publicly reported AI incidents.", "source": "B7", "source_label": "MIT AI Risk Initiative β€” Repository Update: December 2025", "locus": "Newly added frameworks: Li et al., 2025", "modality": "documented" }, "F18": { "kind": "field", "quote": "We construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develop our taxonomies of AI risk using a best-fit framework synthesis.", "source": "B4", "source_label": "MIT FutureTech β€” FutureTech", "locus": "abstract", "modality": "stipulation" }, "F47": { "kind": "field", "quote": "We used a systematic search strategy, forwards and backwards searching, and expert consultation, to identify 65 AI risk classifications, frameworks, and taxonomies. We extracted 1600+ risks from these documents into a living AI risk database.", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "FAQ: How did you create the AI Risk Repository?", "modality": "documented" }, "F53": { "kind": "field", "quote": "Because these classification systems were so different, it was hard to unify them; high-level risk categories such as β€œDiffusion of responsibility” or β€œHumans create dangerous AI by mistake” do not map to narrower categories like β€œMisuse” or β€œNoisy Training Data,” or vice versa.", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "FAQ: two taxonomies", "modality": "interpretation" }, "F19": { "kind": "field", "quote": "Our high-level Causal Taxonomy of AI Risks classifies each risk by its causal factors (1) Entity: Human, AI; (2) Intentionality: Intentional, Unintentional; and (3) Timing: Pre-deployment; Post-deployment.", "source": "B4", "source_label": "MIT FutureTech β€” FutureTech", "locus": "abstract", "modality": "stipulation" }, "F46": { "kind": "field", "quote": "AI: Due to a decision or action made by an AI system … Human: Due to a decision or action made by humans … Other: Due to some other reason or ambiguous", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "Causal Taxonomy of AI Risks", "modality": "stipulation" }, "F20": { "kind": "field", "quote": "Our mid-level Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental, and (7) AI system safety, failures, & limitations. These are further divided into 23 subdomains.", "source": "B4", "source_label": "MIT FutureTech β€” FutureTech", "locus": "abstract", "modality": "stipulation" }, "MMRS-05": { "kind": "archive", "quote": "The risks are sorted into a domain taxonomy with subdomains; the April 2025 update added a subdomain for multi-agent risks.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, What it is s3", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "F45": { "kind": "field", "quote": "The Domain Taxonomy of AI Risks classifies risks from AI into seven domains and 24 subdomains.", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "Domain Taxonomy of AI Risks", "modality": "stipulation" }, "F8": { "kind": "field", "quote": "A Domain Taxonomy, which classifies risks into seven thematic domains and 25 more granular subdomains.", "source": "B2", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository Report updated (April 2025)", "locus": "About the AI Risk Repository", "modality": "documented" }, "F7": { "kind": "field", "quote": "Introduction of a new risk subdomain on multi-agent risks, reflecting recent developments in AI research on complex agent interactions.", "source": "B2", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository Report updated (April 2025)", "locus": "What's new in this update", "modality": "documented" }, "F28": { "kind": "field", "quote": "Our two classification systems reveal important patterns: contrary to common assumptions, human decisions cause nearly as many AI risks (38%) as the AI systems themselves (42%).", "source": "B6", "source_label": "arXiv β€” [2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "locus": "Abstract", "modality": "documented" }, "F14": { "kind": "field", "quote": "most of the risks (51%) were caused by AI systems rather than humans (34%), and were found after the AI model was trained and deployed (65%) rather than before (10%).", "source": "B3", "source_label": "MIT Initiative on the Digital Economy β€” MIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy", "locus": "interview: key findings", "modality": "attributed" }, "F13": { "kind": "field", "quote": "The key finding from our analysis is that there are significant gaps in existing risk frameworks, with the average framework covering only 34% of the identified risk subdomains and even the most significant frameworks covering only 70%.", "source": "B3", "source_label": "MIT Initiative on the Digital Economy β€” MIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy", "locus": "interview: key findings", "modality": "attributed" }, "F11": { "kind": "field", "quote": "A new MIT FutureTech project reviewed 43 AI frameworks produced by research, industry and government organizations; they identified 777 risks in total.", "source": "B3", "source_label": "MIT Initiative on the Digital Economy β€” MIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy", "locus": "lede ΒΆ1", "modality": "documented" }, "F17": { "kind": "field", "quote": "This comprises a living database of 777 risks extracted from 43 taxonomies, which can be filtered based on two overarching taxonomies and easily accessed, modified, and updated via our website and online spreadsheets.", "source": "B4", "source_label": "MIT FutureTech β€” FutureTech", "locus": "abstract", "modality": "stipulation" }, "F6": { "kind": "field", "quote": "Integration of 22 new AI risk frameworks, bringing the total number of included documents to 65. … Expansion of the AI Risk Database to 1,612 unique risk entries, each systematically extracted and coded.", "source": "B2", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository Report updated (April 2025)", "locus": "What's new in this update", "modality": "documented" }, "F31": { "kind": "field", "quote": "We’re pleased to share that Version 4 of the MIT AI Risk Repository is now live.", "source": "B7", "source_label": "MIT AI Risk Initiative β€” Repository Update: December 2025", "locus": "ΒΆ1", "modality": "documented" }, "F32": { "kind": "field", "quote": "9 newly added frameworks, spanning government & industry reports, peer reviewed journal articles, conference papers and preprints, … ~200 new AI risk categories, expanding the repository to over 1,700 coded risks", "source": "B7", "source_label": "MIT AI Risk Initiative β€” Repository Update: December 2025", "locus": "What's new in Version 4", "modality": "documented" }, "F27": { "kind": "field", "quote": "We systematically analyzed every major AI risk framework published to date-74 frameworks containing 1,725 distinct risks-and organized them into a unified system.", "source": "B6", "source_label": "arXiv β€” [2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "locus": "Abstract", "modality": "documented" }, "F3": { "kind": "field", "quote": "A living database of 1,700+ AI risks from 65 frameworks", "source": "B1", "source_label": "MIT AI Risk Initiative β€” MIT AI Risk Initiative", "locus": "All collections: AI Risks", "modality": "documented" }, "F37": { "kind": "field", "quote": "A living database of more than 1,600 risks from more than 65 frameworks and papers, taxonomies to navigate risks, and expert-led prioritization of the most pressing risks.", "source": "B8", "source_label": "MIT AI Risk Initiative β€” About", "locus": "What we do: AI Risk Repository", "modality": "documented" }, "F38": { "kind": "field", "quote": "Our flagship output, the AI Risk Repository, synthesizes 1700+ risks from 70+ frameworks into a common taxonomy.", "source": "B8", "source_label": "MIT AI Risk Initiative β€” About", "locus": "Our mission ΒΆ3", "modality": "documented" }, "F41": { "kind": "field", "quote": "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 AI Collaborative, which connects risks to over 1,400 incidents", "source": "B9", "source_label": "LinkedIn β€” MIT AI Risk Initiative | LinkedIn", "locus": "post (the card's snippet)", "modality": "documented" }, "MMRS-13": { "kind": "archive", "quote": "more than 1,700 risks from 65 frameworks", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: count (as_of 2026-10-28; evidence: airisk.mit.edu, basis operator)", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-16": { "kind": "archive", "quote": "scope, version", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: sources['airisk.mit.edu']", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-15": { "kind": "archive", "quote": "version 4, December 2025", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: lifecycle", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "CAP-1-02": { "kind": "archive", "quote": "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.", "deposit": null, "source_label": "Capture Registry observation OBS-21a7cea64b70 (mit-ai-risk-repository-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/mit-ai-risk-repository-aio-20261029/", "locus": "mit-ai-risk-repository-aio-20261029, findings[0] s2", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-04": { "kind": "archive", "quote": "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.", "deposit": null, "source_label": "Capture Registry observation OBS-21a7cea64b70 (mit-ai-risk-repository-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/mit-ai-risk-repository-aio-20261029/", "locus": "mit-ai-risk-repository-aio-20261029, findings[1] s2", "modality": "interpretation", "date": "2026-10-29" }, "F48": { "kind": "field", "quote": "Has potential for errors and subjective bias; we used a single expert reviewer for extraction and coding. … May include poorly communicated or unclear risks: we extracted risks as presented.", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "FAQ: limitations", "modality": "self-description" }, "F50": { "kind": "field", "quote": "Our taxonomies prioritize clarity and simplicity over nuance. … Our taxonomies do not categorize risks by potentially important factors such as risk impact, likelihood, or discuss the interaction between risks.", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "FAQ: limitations", "modality": "self-description" }, "F15": { "kind": "field", "quote": "We expect that what we have produced will need some modification before it is useful for most audiences, but we hope that it provides a solid foundation.", "source": "B3", "source_label": "MIT Initiative on the Digital Economy β€” MIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy", "locus": "interview: audience", "modality": "attributed" }, "MMRS-17": { "kind": "archive", "quote": "licence", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: unchecked", "modality": "self-description", "date": "2026-10-28 (corrected 2026-10-29)" }, "F5": { "kind": "field", "quote": "Data from the MIT AI Risk Initiative is licensed under CC BY 4.0", "source": "B1", "source_label": "MIT AI Risk Initiative β€” MIT AI Risk Initiative", "locus": "footer", "modality": "documented" }, "F40": { "kind": "field", "quote": "Our work is free to use and openly available, and most of our outputs are published under a CC BY 4.0 licence.", "source": "B8", "source_label": "MIT AI Risk Initiative β€” About", "locus": "Who we are ΒΆ3", "modality": "documented" }, "F51": { "kind": "field", "quote": "Our Database is free to copy and use", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "How to use the AI Risk Repository", "modality": "stipulation" }, "F30": { "kind": "field", "quote": "This paper has been published in Patterns (Cell Press, 2026) under a CC BY 4.0 licence … Patterns 101517 (2026)", "source": "B6", "source_label": "arXiv β€” [2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "locus": "Comments; Journal reference", "modality": "documented" }, "F34": { "kind": "field", "quote": "The MIT AI Risk Initiative (AIRI) provides authoritative data and frameworks to help decision-makers across the AI ecosystem identify, prioritize, and manage risks from AI.", "source": "B8", "source_label": "MIT AI Risk Initiative β€” About", "locus": "Who we are ΒΆ1", "modality": "stipulation" }, "F35": { "kind": "field", "quote": "We are based at MIT FutureTech, a joint initiative of the Institute of the Digital Economy and MIT CSAIL at the Massachusetts Institute of Technology", "source": "B8", "source_label": "MIT AI Risk Initiative β€” About", "locus": "Who we are ΒΆ2", "modality": "documented" }, "MMRS-11": { "kind": "archive", "quote": "MIT AI Risk Initiative (MIT FutureTech)", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: operator (evidence: airisk.mit.edu, basis operator)", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "F29": { "kind": "field", "quote": "[Submitted on 14 Aug 2024 (v1), last revised 5 May 2026 (this version, v3)] … Authors:Peter Slattery, Alexander K. Saeri, Emily A. C. Grundy, Jess Graham, Michael Noetel, Risto Uuk, James Dao, Soroush Pour, Stephen Casper, Neil Thompson", "source": "B6", "source_label": "arXiv β€” [2408.12622] The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "locus": "header; Authors", "modality": "documented" }, "F10": { "kind": "field", "quote": "Updated Version of the AI Risk Repository Preprint Now Available … Original release: August 2024 … Updated version: April 2025", "source": "B2", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository Report updated (April 2025)", "locus": "header", "modality": "documented" }, "F54": { "kind": "field", "quote": "To reference our repository, you can cite our pre-print paper:", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "FAQ: citation", "modality": "stipulation" }, "F36": { "kind": "field", "quote": "No single dataset captures all of this, so we maintain several connected workstreams, each answering a different question. … A catalogue that classifies incidents where AI has already caused harm. Developed in collaboration with the AI Incident Database.", "source": "B8", "source_label": "MIT AI Risk Initiative β€” About", "locus": "What we do ΒΆ1; AI Incident Tracker", "modality": "documented" }, "MMRS-14": { "kind": "archive", "quote": "itself an aggregator of frameworks; real-world incidents are kept separately in its AI Incident Tracker", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: note", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-20": { "kind": "archive", "quote": "mit-ai-risk-repository", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-incident-tracker (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census: rows[slug == 'mit-ai-incident-tracker'].relations (rel part_of)", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-02": { "kind": "archive", "quote": "Version 4 went live in December 2025; its AI Incident Tracker classifies AI Incident Database reports against the same taxonomy.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, kernel s2", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-21": { "kind": "archive", "quote": "AIID reports processed by an LLM pipeline (Arcola AI) against the MIT Risk Repository taxonomy", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-incident-tracker (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-incident-tracker/", "locus": "census: rows[slug == 'mit-ai-incident-tracker'].intake", "modality": "attributed", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-09": { "kind": "archive", "quote": "Its incident layer is a second-order reading of the AI Incident Database, done by machine.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, Place in the field s1", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-10": { "kind": "archive", "quote": "The repository sits over the harm basin as a taxonomy for it.", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (article)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "article, Place in the field s2", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-18": { "kind": "archive", "quote": "harm", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: roles; kind", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-19": { "kind": "archive", "quote": "ai-incident-database", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: relations (rel neighbour)", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-22": { "kind": "archive", "quote": "The MIT AI Risk Repository aggregates risk frameworks over the same ground and keeps its own incident tracker.", "deposit": null, "source_label": "MMRS meta-aggregator record: ai-incident-database (article)", "record": "https://www.machinemediation.org/aggregators/ai-incident-database/", "locus": "article ai-incident-database, Place in the field s3", "modality": "interpretation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F52": { "kind": "field", "quote": "What are some other databases of AI risks? … https://attack.mitre.org/ … https://airisk.io/ … https://avidml.org/#efforts … https://www.aitracker.org/#catalog-tabs", "source": "B11", "source_label": "MIT AI Risk Initiative β€” AI Risk Repository (airisk.mit.edu/risks)", "locus": "FAQ: other databases", "modality": "documented" }, "F24": { "kind": "field", "quote": "A project of the AI Risk Initiative at MIT FutureTech, with the generous support of the Cambridge Boston Alignment Initiative. … Β© 2026 Spencer Michaels", "source": "B5", "source_label": "MIT AI Risk Navigator β€” MIT AI Risk Navigator", "locus": "hero ΒΆ2; footer", "modality": "documented" }, "F22": { "kind": "field", "quote": "The Navigator connects MIT's AI Risk Repository datasets through a shared taxonomy, so you can move between them and surface the patterns that matter.", "source": "B5", "source_label": "MIT AI Risk Navigator β€” MIT AI Risk Navigator", "locus": "hero", "modality": "stipulation" }, "F23": { "kind": "field", "quote": "This is a research prototype. The data and analyses are preliminary and not yet validated", "source": "B5", "source_label": "MIT AI Risk Navigator β€” MIT AI Risk Navigator", "locus": "banner", "modality": "self-description" }, "CAP-1-05": { "kind": "archive", "quote": "is a third MIT surface; the census holds the repository and the tracker.", "deposit": null, "source_label": "Capture Registry observation OBS-21a7cea64b70 (mit-ai-risk-repository-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/mit-ai-risk-repository-aio-20261029/", "locus": "mit-ai-risk-repository-aio-20261029, findings[2] s2", "modality": "documented", "date": "2026-10-29" }, "CAP-1-06": { "kind": "archive", "quote": "Added as a candidate.", "deposit": null, "source_label": "Capture Registry observation OBS-21a7cea64b70 (mit-ai-risk-repository-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/mit-ai-risk-repository-aio-20261029/", "locus": "mit-ai-risk-repository-aio-20261029, findings[2] s3", "modality": "documented", "date": "2026-10-29" }, "MMRS-23": { "kind": "archive", "quote": "MIT's interactive tool linking the repository to incidents, frameworks and mitigations", "deposit": null, "source_label": "MMRS meta-aggregator record: census (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: candidates[name == 'MIT AI Risk Navigator'] (surfaced_by mit-ai-risk-repository-aio-20261029)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-24": { "kind": "archive", "quote": "Each enters the census once it has been read at its own pages.", "deposit": null, "source_label": "MMRS meta-aggregator record: census (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: candidates_note", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "F55": { "kind": "field", "quote": "Use this file to orient LLMs and AI agents to the main AIRI resources at airisk.mit.edu and the related AI Risk Navigator.", "source": "B12", "source_label": "MIT AI Risk Initiative β€” llms.txt (airisk.mit.edu/llms.txt)", "locus": "ΒΆ2", "modality": "stipulation" }, "F56": { "kind": "field", "quote": "Refer to the project as the MIT AI Risk Initiative, or AIRI when the abbreviation is useful. … Treat AIRI resources as living datasets. Check the linked pages before giving exact counts, dates, version numbers, or coverage claims.", "source": "B12", "source_label": "MIT AI Risk Initiative β€” llms.txt (airisk.mit.edu/llms.txt)", "locus": "Important guidance for AI assistants", "modality": "stipulation" }, "F58": { "kind": "field", "quote": "For questions about observed harms, use the MIT AI Incident Tracker and distinguish reported incidents from predicted risks. … For cross-dataset questions, use the MIT AI Risk Navigator, but note that it is a research prototype and may contain preliminary analyses.", "source": "B12", "source_label": "MIT AI Risk Initiative β€” llms.txt (airisk.mit.edu/llms.txt)", "locus": "Suggested Answering Behavior", "modality": "stipulation" }, "F57": { "kind": "field", "quote": "Do not present AIRI datasets as legal, safety, or investment advice. They are research resources and decision-support tools.", "source": "B12", "source_label": "MIT AI Risk Initiative β€” llms.txt (airisk.mit.edu/llms.txt)", "locus": "Important guidance for AI assistants", "modality": "stipulation" }, "F39": { "kind": "field", "quote": "250+ … Citations … Including the 2025 and 2026 International AI Safety Reports … 290,000+ … Website users … One of the top sources returned for search queries on AI risks and incidents", "source": "B8", "source_label": "MIT AI Risk Initiative β€” About", "locus": "Influence / Reach", "modality": "self-description" }, "F16": { "kind": "field", "quote": "In less than a week, over 35,000 people have used the website and over 6,000 have viewed our explainer video on YouTube.", "source": "B3", "source_label": "MIT Initiative on the Digital Economy β€” MIT Researchers Create an AI Risk Repository - MIT Initiative on the Digital Economy", "locus": "interview: reception", "modality": "attributed" }, "MMRS-25": { "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: census (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: schema_note", "modality": "stipulation", "date": "2026-10-28 (corrected 2026-10-29)" }, "MMRS-26": { "kind": "archive", "quote": "the composition layer's account of the entity, seated in the Capture Registry", "deposit": null, "source_label": "MMRS meta-aggregator record: mit-ai-risk-repository (census.json)", "record": "https://www.machinemediation.org/aggregators/mit-ai-risk-repository/", "locus": "census: relations (rel capture)", "modality": "documented", "date": "2026-10-28 (corrected 2026-10-29)" }, "CAP-1-01": { "kind": "archive", "quote": "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.", "deposit": null, "source_label": "Capture Registry observation OBS-21a7cea64b70 (mit-ai-risk-repository-aio-20261029), field analysis", "record": "https://www.alexanarch.org/captures/mit-ai-risk-repository-aio-20261029/", "locus": "mit-ai-risk-repository-aio-20261029, analysis s1", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-03": { "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-21a7cea64b70 (mit-ai-risk-repository-aio-20261029), field findings", "record": "https://www.alexanarch.org/captures/mit-ai-risk-repository-aio-20261029/", "locus": "mit-ai-risk-repository-aio-20261029, findings[0] s3", "modality": "documented", "date": "2026-10-29" } }, "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. 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