{"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":"65.01.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Transparency) ","risk_subcategory":"Lack of training data transparency ","description":"\"Without accurate documentation on how a model's data was collected, curated, and used to train a model, it might be harder to satisfactorily explain the behavior of the model with respect to the data.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.01.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Transparency) ","risk_subcategory":"Uncertain data provenance ","description":"\"Data provenance refers to tracing history of data, which includes its ownership, origin, and transformations. 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As the use changes, the relevant risks might correspondingly change.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.22.04","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Lack of data transparency ","description":"\"Lack of data transparency is due to insufficient documentation of training or tuning dataset details. \"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.22.05","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Incorrect risk testing ","description":"\"A metric selected to measure or track a risk is incorrectly selected, incompletely measuring the risk, or measuring the wrong risk for the given context.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.22.07","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Lack of testing diversity ","description":"\"AI model risks are socio-technical, so their testing needs input from a broad set of disciplines and diverse testing practices.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.23.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on cultural diversity ","description":"\"AI systems might overly represent certain cultures that result in a homogenization of culture and thoughts.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"65.23.03","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on Jobs ","description":"\"Widespread adoption of foundation model-based AI systems might lead to people's job loss as their work is automated if they are not reskilled.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"65.23.06","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on the environment ","description":"\"AI, and large generative models in particular, might produce increased carbon emissions and increase water usage for their training and operation.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"65.23.07","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Human exploitation ","description":"\"When workers who train AI models such as ghost workers are not provided with adequate working conditions, fair compensation, and good health care benefits that also include mental health.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.2"}]}