What Is the MIT AI Risk Initiative Website?

Updated

The MIT AI Risk Initiative (airisk.mit.edu) is a research initiative that produces authoritative data and frameworks to help people identify, prioritize, and manage risks from AI. It is most useful if you need structured, evidence-based material on AI risk — for example, to ground a policy discussion, benchmark company disclosures, or map which laws cover which risks — rather than general commentary. Its core offerings are a living repository of AI risks, an incident database, a governance/law mapping, a company disclosure tracker, and the AI Risk Navigator that connects these datasets.

What the site provides

The homepage organizes its work around five questions, each backed by a tool or dataset:

Question the site answers Resource
Which risks are most important? Expert consensus survey on which AI risks are expected to cause the most severe harms, who is most vulnerable, and who is responsible for addressing them
What are the risks from AI? A living repository of AI risks and how they emerge
What real-world harms are occurring? An incident database you can explore and compare by risk domain, type and severity of harm, number of people affected, alleged developer, time, or national security impact
What laws cover what risks? Global AI laws, policies, and frameworks mapped against the risks they address, filterable by risk domain, sector, lifecycle stage, value chain actor, legislative status, or technical scope
How are companies responding? Tracking of which of 24 AI risks 200 of the world's largest companies acknowledge in public documents, compared with what experts say matters most

The AI Risk Navigator

The Navigator is the site's integration layer: it connects the datasets above so you can move between them and surface patterns that matter across risk, incidents, governance, and company disclosures. If you only have time for one entry point and your question spans more than one dataset, start here.

Who it is for

The site is built for people who need to act on AI risk information rather than just read about it. Named users in the site's testimonials point to three audiences:

  • Policy and governance professionals — Madhu Srikumar of the Partnership on AI describes the taxonomy as helping "the policy community have more grounded discussions" in a field "characterized by significant uncertainty."
  • Legal and compliance teams — Kevin Fumai of Oracle cites the "practical, evidence-based approach" as offering "immediate benefits to anyone integrating risk management across the AI value chain."
  • Researchers and technical teams — Yoshua Bengio calls it "a valuable database for accessing current and comprehensive information on a wide range of AI-related risks," and Dr. Ian W. Eisenberg of Credo AI frames it as "open academic infrastructure the AI governance ecosystem needs."

How to use it in practice

A practical sequence for a typical task — say, preparing an internal briefing on a specific risk domain:

  1. Scope the risk. Use the risk repository to find the relevant risk domain and how it emerges.
  2. Check for real-world evidence. Query the incident database filtered to that domain, and note severity, number of people affected, and alleged developer.
  3. Check the legal picture. Use the governance mapping, filtered by sector or lifecycle stage, to see which laws and frameworks address that risk.
  4. Compare with corporate practice. Check the company disclosure tracker to see whether large companies acknowledge that risk publicly, and how that compares with expert consensus.
  5. Cross-reference. Use the Navigator to move between these views and identify gaps — for example, a risk experts rank as severe but that few companies disclose or few laws cover.

The expected result at each step is a citable, structured view rather than a narrative summary, which is what makes the material usable in governance and policy settings.

What to keep in mind

  • The site presents itself as a source of data and frameworks, not advice; the judgments about what to do with the findings remain yours.
  • The risk repository is described as "living," and the incident and disclosure data are tracked over time, so figures you cite should be dated to when you accessed them.
  • The site does not state pricing or login requirements in the material reviewed here, so check the individual tool pages before assuming open access to every dataset.

For a first visit, the Navigator gives the broadest view; for a narrow question, go directly to the matching dataset in the table above.