{"attribution":{"source":"MIT AI Risk Repository, Domain Taxonomy of AI Risks v1 (MIT AI Risk Initiative)","license":"CC BY 4.0","license_url":"https://creativecommons.org/licenses/by/4.0/","citation":"Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2025). The AI Risk Repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv:2408.12622."},"exported_at":"2026-09-27"}
{"rows":[{"ev_id":"12.04.00","slug":"explainability-transparency-sherman","quick_ref":"Sherman2023","paper_title":"AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures","level":"Risk Category","risk_category":"Explainability & Transparency","risk_subcategory":null,"description":"\"The feasibility of understanding and interpreting an AI system's decisions and actions, and the openness of the developer about the data used, algorithms employed, and decisions made. Lack of these elements can create risks of misuse, misinterpretation, and lack of accountability.\"","entity":"AI","intent":"Other","timing":"Other","domain":7,"subdomain":"7.4","url":"https://aipolicytracker.org/ai-risk/risks/explainability-transparency-sherman"}]}