{"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":"2026-09-11"}
{"rows":[{"ev_id":"14.05.00","quick_ref":"Steimers2022","paper_title":"Sources of Risk of AI Systems","level":"Risk Category","risk_category":"Degree of Transparency and Explainability","risk_subcategory":null,"description":"\"Transparency is the characteristic of a system that describes the degree to which appropriate information about the system is communicated to relevant stakeholders, whereas explainability describes the property of an AI system to express important factors influencing the results of the AI system in a way that is understandable for humans....Information about the model underlying the decision-making process is relevant\n for transparency. Systems with a low degree of transparency can pose risks in terms of\n their fairness, security and accountability. \"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"}]}