{"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":"2026-09-12"}
{"rows":[{"ev_id":"21.01.00","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Category","risk_category":"Data-level risk","risk_subcategory":null,"description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"21.01.01","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Data-level risk","risk_subcategory":"Data bias","description":"\"Specifically, data bias refers to certain groups or certain types of elements that are over-weighted or over-represented than others in AI/ ML models, or variables that are crucial to characterize a phenomenon of interest, but are not properly captured by the learned models.\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"21.01.02","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Data-level risk","risk_subcategory":"Dataset shift","description":"\"The term \"dataset shift\" was first used by Quiñonero-Candela et al. [35] to characterize the situation where the training data and the testing data (or data in runtime) of an AI/ML model demonstrate different distributions [36].\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.3"},{"ev_id":"21.01.02.a","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Additional evidence","risk_category":"Data-level risk","risk_subcategory":"Dataset shift","description":null,"entity":null,"intent":null,"timing":null,"domain":null,"subdomain":null},{"ev_id":"21.01.02.b","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Additional evidence","risk_category":"Data-level risk","risk_subcategory":"Dataset shift","description":null,"entity":null,"intent":null,"timing":null,"domain":null,"subdomain":null},{"ev_id":"21.01.02.c","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Additional evidence","risk_category":"Data-level risk","risk_subcategory":"Dataset shift","description":null,"entity":null,"intent":null,"timing":null,"domain":null,"subdomain":null},{"ev_id":"21.01.03","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Data-level risk","risk_subcategory":"Out-of-domain data","description":"\"Without proper validation and management on the input data, it is highly probable that the trained AI/ML model will make erroneous predictions with high confidence for many instances of model inputs. The unconstrained inputs together with the lack of definition of the problem domain might cause unintended outcomes and consequences, especially in risk-sensitive contexts....For example, with respect to the example shown in Fig. 5, if an image with the English letter A\" is fed to an AI/ML model that is trained to classify digits (e.g., 0, 1, …, 9), no matter how accurate the AI/ML model is, it w","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.3"},{"ev_id":"21.01.04","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Data-level risk","risk_subcategory":"Adversarial attack","description":"\"Recent advances have shown that a deep learning model with high predictive accuracy frequently misbehaves on adversarial examples [57,58]. In particular, a small perturbation to an input image, which is imperceptible to humans, could fool a well-trained deep learning model into making completely different predictions [23].\"","entity":"Human","intent":"Intentional","timing":"Other","domain":2,"subdomain":"2.2"},{"ev_id":"21.01.04.a","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Additional evidence","risk_category":null,"risk_subcategory":"Adversarial attack","description":null,"entity":null,"intent":null,"timing":null,"domain":null,"subdomain":null},{"ev_id":"21.02.00","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Category","risk_category":"Model-level risk","risk_subcategory":null,"description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"21.02.01","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Model-level risk","risk_subcategory":"Model bias","description":"\"While data bias is a major contributor of model bias, model bias actually manifests itself in different forms and shapes, such as presentation bias, model evaluation bias, and popularity bias. In addition, model bias arises from various sources [62], such as AI/ML model selection (e.g., support vector machine, decision trees), regularization methods, algorithm configurations, and optimization techniques.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"21.02.01.a","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Model-level risk","risk_subcategory":"Model misspecification","description":"\"Models that are misspecified are known to give rise to inaccurate parameter estimations, inconsistent error terms, and erroneous predictions. All these factors put together will lead to poor prediction performance on unseen data and biased consequences when making decisions [68].\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.3"},{"ev_id":"21.02.01.b","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Additional evidence","risk_category":"Model-level risk","risk_subcategory":"Model misspecification","description":null,"entity":null,"intent":null,"timing":null,"domain":null,"subdomain":null},{"ev_id":"21.02.01.c","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Additional evidence","risk_category":"Model-level risk","risk_subcategory":"Model misspecification","description":null,"entity":null,"intent":null,"timing":null,"domain":null,"subdomain":null},{"ev_id":"21.02.01.d","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Additional evidence","risk_category":"Model-level risk","risk_subcategory":"Model misspecification","description":null,"entity":null,"intent":null,"timing":null,"domain":null,"subdomain":null},{"ev_id":"21.02.02","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Model-level risk","risk_subcategory":"Model prediction uncertainty","description":"\"Uncertainty in model prediction plays an important role in affecting decision-making activities, and the quantified uncertainty is closely associated with risk assessment. In particular, uncertainty in model prediction underpins many crucial decisions related to life or safety- critical applications [73].\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.3"}]}