The MIT AI Risk Repository is a research initiative within MIT FutureTech that maintains a continuously updated database of AI risks drawn from academic, industry, and government sources. The repository categorizes risks using two complementary systems: a Causal Taxonomy (classifying risks by entity, intent, and timing) and a Domain Taxonomy covering seven areas including discrimination, privacy, misinformation, malicious use, human-computer interaction, socioeconomic impacts, and AI system safety. Its goal is to increase awareness and adoption of best-practice AI risk management by giving researchers, policymakers, and organizations a structured, accessible view of the AI risk landscape. The project has expanded through quarterly updates since its launch in August 2024 and also maintains an AI Incident Tracker that classifies real-world AI incidents using the repository's taxonomies.
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Theory of Change
The AI risk landscape is fragmented across dozens of academic, industry, and government frameworks, making it hard for any single actor to develop a comprehensive picture of what risks exist and how they relate to each other. By systematically extracting, categorizing, and making freely available all identified AI risks in a single structured database, the repository allows researchers, policymakers, red teams, and companies to quickly identify coverage gaps, prioritize mitigations, and build on a common vocabulary. Better-informed AI governance and risk management practices—grounded in a shared, evidence-based taxonomy—should reduce the probability that known AI harms are neglected due to siloed knowledge. The complementary AI Incident Tracker and AI Risk Index extend this by connecting the taxonomy to real-world incidents and measuring how well organizations are actually implementing recommended risk management practices.
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- Last Updated
- Apr 2, 2026, 10:01 PM UTC
- Created
- Mar 19, 2026, 10:31 PM UTC