{"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":"59.01.00","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":"Inadequate specification of ODD","risk_subcategory":null,"description":"\"The operational design domain (ODD) is a technical description of the application’s operational environment, initially conceptualized for autonomous driving systems. An inadequate specification of the ODD limits essential functions such as testing the learned functionality and out-of-distribution detection.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"59.02.00","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 automation","risk_subcategory":null,"description":"\"The AI application’s degree of automation ranges from no automation to fully autonomous. AI applications with a high degree of automation may exhibit unexpected behaviour and pose risks in terms of their reliability and safety.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.2"},{"ev_id":"59.03.00","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":"Inadequate planning of performance requirements","risk_subcategory":null,"description":"\"The expected performance of the AI system should be planned adequately. Hereby, an important aspect is that chosen performance metrics are meaningful for presenting the intended functionality. Otherwise, expectations and safety requirements can be unfulfillable at later life cycle stages.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"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":"59.07.00","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":"Choice of untrustworthy data source","risk_subcategory":null,"description":"\"The choice of a trustworthy data source is a first prerequisite in order to fulfill data quality requirements. This is especially the case if third-party data sources are used to develop the AI system.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.0"},{"ev_id":"59.08.00","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 data understanding","risk_subcategory":null,"description":"\"The correct understanding of the used data for developing an AI system is a prerequisite to avoid data shortcomings and hinders the development of an AI system which is best suiting for the intended functionality.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.0"},{"ev_id":"59.11.00","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":"Incorrect data labels","risk_subcategory":null,"description":"\"Data labels are essential for any supervised learning algorithm since they preset the result of the learning process. If the correctness of the data labels is not given, the AI system is prevented from learning the ground truth and therefore the intended functionality.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"59.13.00","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 data representation","risk_subcategory":null,"description":"\"The distribution of the data used for training a model should match the operational data ́s distribution while consisting of sufficiently many samples. An important aspect of matching distributions between training and operational data is that also data which is rarely confronting the AI system in operation is represented in the training data.\"","entity":"Other","intent":"Other","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"59.14.00","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":"Problems of synthetic data","risk_subcategory":null,"description":"\"In the case of sparse data quantity, the simulation or generation of data is a valid alternative. However, it is essential to make sure that the simulated data is sufficiently similar to real data, especially in the way the AI system perceives them. Otherwise, generalization to operational data and reliable operational behavior can not be guaranteed.\"","entity":"Other","intent":"Other","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"59.15.00","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 data splitting","risk_subcategory":null,"description":"\"In data-driven AI development, the annotated data set is commonly split into training, validation, and test sets, whereby it is essential that the latter is not used for development but only for evaluation. Using the test set for training manipulates the testing strategy, which is the basis of the system’s quality assurance.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":7,"subdomain":"7.0"},{"ev_id":"59.16.00","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":"Poor model design choices","risk_subcategory":null,"description":"\"The model specifications have significant impact on the functionality of an AI system. The developer mak- ing wrong decisions might cause the AI system to behave biased and unreliable.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"59.17.00","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":"Over- and underfitting","risk_subcategory":null,"description":"\"Over- and underfitting describe the over or insufficient adaption of a model to training data. Both phenomena can cause an AI system to behave unreliably if confronted with operational data.\"","entity":"Other","intent":"Other","timing":"Other","domain":7,"subdomain":"7.3"},{"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":"59.19.00","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":"Unreliability in corner cases","risk_subcategory":null,"description":"\"AI systems tend to show unreliable behavior when confronted with rare or ambiguous input data, also called corner cases. Therefore, the controlled behavior is required whenever the AI system is faces a corner case.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.3"},{"ev_id":"59.20.00","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 robustness","risk_subcategory":null,"description":"\"Robustness characterizes the resilience of an AI system’s output against minor changes in the input domain. A great variation in an AI system’s response to small input changes indicates unreliable outputs.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.3"},{"ev_id":"59.21.00","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":"Uncertainty concerns","risk_subcategory":null,"description":"\"AI systems should be able not only to return output for a given instance but also to provide a corresponding level of confidence. If such a method is not implemented or not working correctly, this can have a negative impact on performance and safety.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.0"},{"ev_id":"59.22.00","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":"Operational data issues","risk_subcategory":null,"description":"\"Until the deployment of the AI application into its operational environment, the AI system has been tested with a test set that aims to approximate the distribution of operational data. However, an unexpected deviation in this approximation can cause an AI application to behave unreliably. Therefore, its behavior under confrontation with operational data needs to be evaluated.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"59.23.00","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":"Data drift","risk_subcategory":null,"description":"\"Data drift is a phenomenon in that distribution of operational input data departs from those used during training. This can cause a degradation in performance.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.3"},{"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"},{"ev_id":"59.26.01","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Sub-Category","risk_category":"Mode","risk_subcategory":"Technical ","description":"\"Technical AI hazards are the root causes of technical deficiencies in the AI system. An example of such an AI hazard is overfitting, which describes a model’s excessive adaptation to the training dataset. Quantitative methods to assess (metrics) and treat (mitigation means) exist for technical AI hazards, which might be performed automatically. In case of overfitting, metrics are based on the comparison of performance between the training and validation datasets, and mitigation means may include regularization techniques, among others.\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"59.26.03","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Sub-Category","risk_category":"Mode","risk_subcategory":"Procedural ","description":"\"The third class encompasses procedural AI hazards. These pertain to issues arising from processes and actions made by individuals involved in the develop- ment process. Such hazards are not readily quantifiable and necessitate alter- native mitigation strategies. An example of such an AI hazard would be ”poor model design choices,” which could be expressed, for instance, through a devel- oper’s decision to select an unsuitable AI model for a given problem. Due to the challenges in quantifying and mitigating these issues, qualitative approaches must be employed. In the case of the aforemention","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.3"}]}