{"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":"65.02.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Data laws) ","risk_subcategory":"Data usage restrictions ","description":"\"Laws and other restrictions can limit or prohibit the use of some data for specific AI use cases.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.02.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Data laws) ","risk_subcategory":"Data acquisition restrictions ","description":"\"Laws and other regulations might limit the collection of certain types of data for specific AI use cases.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.02.03","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Data laws) ","risk_subcategory":"Data transfer restrictions ","description":"\"Laws and other restrictions can limit or prohibit transferring data.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.06.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Accuracy) ","risk_subcategory":"Data contamination ","description":"\"Data contamination occurs when incorrect data is used for training. For example, data that is not aligned with model’s purpose or data that is already set aside for other development tasks such as testing and evaluation.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.06.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Accuracy) ","risk_subcategory":"Unrepresentative data ","description":"\"Unrepresentative data occurs when the training or fine-tuning data is not sufficiently representative of the underlying population or does not measure the phenomenon of interest.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.07.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Value alignment) ","risk_subcategory":"Improper retraining ","description":"\"Using undesirable output (for example, inaccurate, inappropriate, and user content) for retraining purposes can result in unexpected model behavior.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.07.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Value alignment) ","risk_subcategory":"Improper data curation ","description":"\"Improper collection and preparation of training or tuning data includes data label errors and by using data with conflicting information or misinformation.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.13.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Inference risks (Accuracy) ","risk_subcategory":"Poor model accuracy ","description":"\"Poor model accuracy occurs when a model’s performance is insufficient to the task it was designed for. Low accuracy might occur if the model is not correctly engineered, or there are changes to the model’s expected inputs.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.14.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (misuse) ","risk_subcategory":"Non-disclosure ","description":"\"Content might not be clearly disclosed as AI generated.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"65.15.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Value alignment)","risk_subcategory":"Incomplete advice ","description":"\"When a model provides advice without having enough information, resulting in possible harm if the advice is followed.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"65.17.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Explainability) ","risk_subcategory":"Inaccessible training data ","description":"\"Without access to the training data, the types of explanations a model can provide are limited and more likely to be incorrect.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"65.17.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Explainability) ","risk_subcategory":"Untraceable attribution ","description":"\"The content of the training data used for generating the model’s output is not accessible.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"65.17.03","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Explainability) ","risk_subcategory":"Unexplainable output ","description":"\"Explanations for model output decisions might be difficult, imprecise, or not possible to obtain.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"65.17.04","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Explainability) ","risk_subcategory":"Unreliable source attribution ","description":"\"Source attribution is the AI system's ability to describe from what training data it generated a portion or all its output. Since current techniques are based on approximations, these attributions might be incorrect.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"65.22.06","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Lack of model transparency ","description":"\"Lack of model transparency is due to insufficient documentation of the model design, development, and evaluation process and the absence of insights into the inner workings of the model.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.4"}]}