{"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":"15.01.02","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"First-Order Risks","risk_subcategory":"Misapplication","description":"This is the risk posed by an ideal system if used for a purpose/in a manner unintended by its creators. In many situations, negative consequences arise when the system is not used in the way or for the purpose it was intended.","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"15.01.03","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"First-Order Risks","risk_subcategory":"Algorithm","description":"\"This is the risk of the ML algorithm, model architecture, optimization technique, or other aspects of the training process being unsuitable for the intended application.Since these are key decisions that influence the final ML system, we\ncapture their associated risks separately from design risks, even though they are part of the design process\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"15.01.05","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"First-Order Risks","risk_subcategory":"Robustness","description":"\"This is the risk of the system failing or being unable to recover upon encountering invalid, noisy, or out-of-distribution (OOD) inputs.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"15.01.06","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"First-Order Risks","risk_subcategory":"Design","description":"\"This is the risk of system failure due to system design choices or errors.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"15.02.01","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"Second-Order Risks","risk_subcategory":"Safety","description":"This is the risk of direct or indirect physical or psychological injury resulting from interaction with the ML system.","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.3"}]}