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The AI Risk Repository
The MIT AI Risk Initiative produces authoritative data and frameworks to help you identify, prioritize, and manage the risks from AI.
About The AI Risk Repository
The MIT AI Risk Initiative provides data and frameworks to identify, prioritize, and manage risks from AI. It includes a living repository of AI risks, an expert survey on expected harms and responsibility, a database of real-world AI incidents, and a collection of global AI laws, policies, and frameworks mapped to the risks they address. The AI Risk Navigator links these datasets for cross-dataset exploration using shared taxonomies.
Key features
- Living repository of AI risks and how they emerge
- Expert survey on expected harms, vulnerability, and responsibility
- Incident explorer filterable by harm, developer, time, and security impact
- Governance collection mapping laws and policies to risks addressed
- Governance filters for sector, lifecycle stage, and legislative status
- Navigator linking datasets for cross-dataset exploration using shared taxonomies
Best for: Researchers, policymakers, and practitioners working on AI risk identification and management.
Good for
- Combines risks, incidents, expert survey, and governance data in one place
- Supports detailed filtering and comparison across incidents and governance documents
- Uses shared taxonomies to allow movement between datasets
The AI Risk Repository at a glance
- Website
- airisk.mit.edu
- Category
- Privacy & Security › Security Research
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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The AI Risk Repository FAQ
What real-world harms are tracked?
The incident explorer allows comparison by risk domain, type and severity of harm, number of people affected, alleged developer, time, or national security impact.
What laws cover what risks?
The governance collection maps global AI laws, policies, and frameworks against the risks they address, with filters for risk domain, sector, lifecycle stage, value chain actor, legislative status, or technical scope.
How can the datasets be explored together?
The AI Risk Navigator connects the datasets so users can move between them using shared taxonomies.
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