{"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":"2026-09-13"}
{"rows":[{"ev_id":"59.04.00","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Category","risk_category":"Insufficient AI development documentation","risk_subcategory":null,"description":"\"Throughout the development of an AI system, it is vital to document every decision and action taken. This is not only essential to optimize the development process itself but also required for the auditability of the AI system.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"59.05.00","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Category","risk_category":"Inappropriate degree of transparency to end users","risk_subcategory":null,"description":"\"The transparency to end users of the AI system increases the user’s trust in the AI application. If not adequately integrated into the design, this might prevent the proper operation and cause potential misuse of the AI application.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"59.18.00","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Category","risk_category":"Lack of explainability","risk_subcategory":null,"description":"\"The explainability of AI systems based on so-called black-box models is often limited. This opaqueness of AI systems can prevent developers from detecting shortcomings in the data or the model itself and decrease the performance and safety levels of the AI system.\"","entity":"Other","intent":"Other","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"59.24.00","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Category","risk_category":"Concept drift","risk_subcategory":null,"description":"\"Concept drift refers to a change in the rela- tionship between input variables and model output. If not treated appropriately, concept drift can reduce the reliability of AI systems.\"","entity":"Other","intent":"Other","timing":"Other","domain":7,"subdomain":"7.4"}]}