A comprehensive database of over 700 artificial intelligence risks has been compiled, categorized by their cause and risk domain. The AI Risk Repository, a collaborative effort led by researchers from MIT FutureTech, The University of Queensland, and other institutions, provides an accessible overview of the AI risk landscape. The repository is divided into three parts: the AI Risk Database, which captures 700+ risks extracted from 43 existing frameworks; the Causal Taxonomy of AI Risks, which classifies how, when, and why these risks occur; and the Domain Taxonomy of AI Risks, which categorizes these risks into seven domains and 23 subdomains. Key individuals involved in this work include Peter Slattery, Alexander K. Saeri, Emily A. C. Grundy, and others. The repository is free to copy and use, providing a valuable resource for researchers, developers, businesses, evaluators, auditors, policymakers, and regulators seeking to understand and mitigate AI risks.
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