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EnglishWhat AI risks does the MIT AI Risk Initiative catalog?
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The MIT AI Risk Initiative catalogs AI risks across a living repository of risk types, a ranked set of priority risks based on expert consensus, a database of real-world incidents, a mapping of global AI laws and frameworks to the risks they address, and a review of how large companies disclose these risks. The site is built for people who need to identify, prioritize, and manage AI risk — policy analysts, governance teams, and researchers — and its tools are organized around four questions: which risks matter most, what the risks are, what harms are actually occurring, and who is responding.
The four datasets and what each covers
| Tool | Question it answers | What you can filter or compare |
|---|---|---|
| Priority risks (expert survey) | Which AI risks are expected to cause the most severe harm? | Severity of expected harm, who is most vulnerable, who is responsible for addressing it |
| AI risks repository | What are the risks and how do they emerge? | Risk domains and emergence pathways in a "living" catalog |
| Incidents database | What real-world harms are occurring? | Risk domain, type and severity of harm, number of people affected, alleged developer, time, national security impact |
| Governance tracker | What laws cover what risks? | Risk domain, sector, lifecycle stage, value chain actor, legislative status, technical scope |
| Company disclosures | How are companies responding? | Which of 24 AI risks 200 of the world's largest companies acknowledge in public documents, compared with expert priorities |
How the pieces connect
The AI Risk Navigator is the entry point that links these datasets, so you can move between a risk category, the incidents associated with it, the laws that address it, and the companies that disclose it. That linkage is the site's main structural idea: a risk is not just a label but a node connecting evidence of harm, responsible actors, and regulatory coverage.
A practical example: if you are assessing exposure for a deployed hiring tool, you could start from the risk repository to identify relevant risk domains, check the incidents database for documented harms in that domain filtered by severity and affected population, then use the governance tracker to see which laws and frameworks cover that domain at your lifecycle stage. The company disclosures view tells you whether large firms in the same space acknowledge the risk at all — a useful gap check between expert priorities and public disclosure.
Priority risks and expert consensus
Rather than presenting all risks as equally weighted, the initiative publishes expert consensus on which risks are expected to have the most severe harms, who is most vulnerable, and who should be responsible for addressing them. This matters if you need to justify prioritization decisions: the survey gives you a citable basis for ranking risks rather than relying on internal judgment alone. The disclosure comparison against 24 risks then shows where corporate acknowledgment diverges from that expert ranking.
Who the site is for and how to use it
The work is cited by academic and governance figures, including Yoshua Bengio (Université de Montréal, LawZero, Mila), who describes it as filling a critical information gap, and practitioners at Partnership on AI, Oracle, and Credo AI who point to its taxonomy and evidence-based approach for policy discussion and value-chain risk management. If your task is building a risk register, briefing policymakers, or benchmarking disclosures, start with the priority survey for ranking, the repository for definitions, and the Navigator to trace risks to incidents and laws. The site also maintains a blog and newsletter for updates to the living datasets.