{"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":"05.13.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Transparency - Explainability","risk_subcategory":null,"description":"Being a multifaceted concept, the term 'transparency' is both used to refer to technical explainability as well as organizational openness. Regarding the former, papers underscore the need for mechanistic interpretability and for explaining internal mechanisms in generative models. On the organizational front, transparency relates to practices such as informing users about capabilities and shortcomings of models, as well as adhering to documentation and reporting requirements for data collection processes or risk evaluations.","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.4"},{"ev_id":"06.06.00","quick_ref":"Hogenhout2021","paper_title":"A framework for ethical Ai at the United Nations","level":"Risk Category","risk_category":"Lack of transparency","risk_subcategory":null,"description":"\"The idea of a \"black box\" making decisions without any explanation, without offering insight in the process, has a couple of disadvantages: it may fail to gain the trust of its users and it may fail to meet regulatory standards such as the ability to audit.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"09.02.03","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Sub-Category","risk_category":"Domain-specific AI - Effects on humans and other living beings: Non-existential risks","risk_subcategory":"Decision making transparency","description":"\"We face significant challenges bringing transparency to artificial network decisionmaking processes. Will we have transparency in AI decision making?\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"10.05.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Lack of transparency","risk_subcategory":null,"description":"\"In situations in which the development and use of AI are not explained to the user, or in which the decision processes do not provide the criteria or steps that constitute the decision, the use of AI becomes inexplicable.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"12.04.00","quick_ref":"Sherman2023","paper_title":"AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures","level":"Risk Category","risk_category":"Explainability & Transparency","risk_subcategory":null,"description":"\"The feasibility of understanding and interpreting an AI system's decisions and actions, and the openness of the developer about the data used, algorithms employed, and decisions made. Lack of these elements can create risks of misuse, misinterpretation, and lack of accountability.\"","entity":"AI","intent":"Other","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"14.05.00","quick_ref":"Steimers2022","paper_title":"Sources of Risk of AI Systems","level":"Risk Category","risk_category":"Degree of Transparency and Explainability","risk_subcategory":null,"description":"\"Transparency is the characteristic of a system that describes the degree to which appropriate information about the system is communicated to relevant stakeholders, whereas explainability describes the property of an AI system to express important factors influencing the results of the AI system in a way that is understandable for humans....Information about the model underlying the decision-making process is relevant\n for transparency. Systems with a low degree of transparency can pose risks in terms of\n their fairness, security and accountability. \"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"30.05.00","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Category","risk_category":"Explainability & Reasoning","risk_subcategory":null,"description":"The ability to explain the outputs to users and reason correctly","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"30.05.01","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Explainability & Reasoning","risk_subcategory":"Lack of Interpretability","description":"Due to the black box nature of most machine learning models, users typically are not able to understand the reasoning behind the model decisions","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"33.02.03","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Technology concerns","risk_subcategory":"Explainability","description":"\"A recurrent concern about AI algorithms is the lack of explainability for the model, which means information about how the algorithm arrives at its results is deficient (Deeks, 2019). Specifically, for generative AI models, there is no transparency to the reasoning of how the model arrives at the results (Dwivedi et al., 2023). The lack of transparency raises several issues. First, it might be difficult for users to interpret and understand the output (Dwivedi et al., 2023). It would also be difficult for users to discover potential mistakes in the output (Rudin, 2019). Further, when the inte","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"33.02.05","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Technology concerns","risk_subcategory":"Prompt engineering","description":"\"With the wide application of generative AI, the ability to interact with AI efficiently and effectively has become one of the most important media literacies. Hence, it is imperative for generative AI users to learn and apply the principles of prompt engineering, which refers to a systematic process of carefully designing prompts or inputs to generative AI models to elicit valuable outputs. Due to the ambiguity of human languages, the interaction between humans and machines through prompts may lead to errors or misunderstandings. Hence, the quality of prompts is important. Another challenge i","entity":"Human","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"37.02.04","quick_ref":"Giarmoleo2024","paper_title":"What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review","level":"Risk Sub-Category","risk_category":"Human-AI interaction","risk_subcategory":"Attributing the responsibility for AI's failures","description":"\"This section, constituting almost 8% of the articles, addresses the implications arising from AI acting and learning without direct human supervision, encompassing two main issues: a responsibility gap and AI's moral status.\"","entity":"Other","intent":"Other","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"38.03.00","quick_ref":"Kumar2023","paper_title":"Ethical Issues in the Development of Artificial Intelligence: Recognizing the Risks","level":"Risk Category","risk_category":"Transparency and explainability","risk_subcategory":null,"description":"\"A recurring complaint among participants was a lack of knowledge about how AI systems made judgements. They emphasized the significance of making AI systems more visible and explainable so that people may have confidence in their outputs and hold them accountable for their activities. Because AI systems are typically opaque, making it difficult for users to understand the rationale behind their judgements, ethical concerns about AI, as well as issues of transparency and explainability, arise. This lack of understanding can generate suspicion and reluctance to adopt AI technology, as well as m","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"38.05.00","quick_ref":"Kumar2023","paper_title":"Ethical Issues in the Development of Artificial Intelligence: Recognizing the Risks","level":"Risk Category","risk_category":"Trust and reliability","risk_subcategory":null,"description":"\"The participants of the study emphasized the importance of trustworthiness and reliability in AI systems. The authors emphasized the importance of preserving precision and objectivity in the outcomes produced by AI systems, while also ensuring transparency in their decision-making procedures. The significance of reliability and credibility in AI systems is escalating in tandem with the proliferation of these technologies across diverse domains of society. This underscores the importance of ensuring user confidence. The concern regarding the dependability of AI systems and their inherent biase","entity":"AI","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"39.19.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Accountability","risk_subcategory":null,"description":"An essential feature of decision-making in humans, AI, and also HLI-based agents is accountability. Implementing this feature in machines is a difficult task because many challenges should be considered to organize an AI-based model that is accountable. It should be noted that this issue in human decision-making is not ideal, and many factors such as bias, diversity, fairness, paradox, and ambiguity may affect it. In addition, the human decision-making process is based on personal flexibility, context-sensitive paradigms, empathy, and complex moral judgments. Therefore, all of these challenges","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"39.20.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Transparency","risk_subcategory":null,"description":"an external entity of an AI-based ecosystem may want to know which parts of data affect the final decision in a learning model","entity":"AI","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"39.21.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Reproducibility","risk_subcategory":null,"description":"How a learning model can be reproduced when it is obtained based on various sets of data and a large space of parameters. This problem becomes more challenging in data-driven learning procedures without transparent instructions","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"39.25.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Verifiability","risk_subcategory":null,"description":"In many applications of AI-based systems such as medical healthcare and military services, the lack of verification of code may not be tolerable... due to some characteristics such as the non-linear and complex structure of AI-based solutions, existing solutions have been generally considered “black boxes”, not providing any information about what exactly makes them appear in their predictions and decision-making processes.","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"42.01.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Accountability","risk_subcategory":null,"description":"\"The ability to determine whether a decision was made in accordance with procedural and substantive standards and to hold someone responsible if those standards are not met.\"","entity":"Other","intent":"Other","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"42.06.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Opacity","risk_subcategory":null,"description":"\"Stems from the mismatch between mathematical optimization in high-dimensionality characteristic of machine learning and the demands of human-scale reasoning and styles of semantic interpretation.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"42.21.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Explainability","risk_subcategory":null,"description":"\"Any action or procedure performed by a model with the intention of clarifying or detailing its internal functions.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"45.01.01","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from models and algorithms (Risks of explainability)","description":"\"AI algorithms, represented by deep learning, have complex internal workings. Their black-box or grey-box inference process results in unpredictable and untraceable outputs, making it challenging to quickly rectify them or trace their origins for accountability should any anomalies arise.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"47.01.05","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Technical and operational risks ","risk_subcategory":"Opacity (the black box problem)","description":"\"Opacity surrounding the technical, internal decision-making processes of generative AI models is popularly known as the “black box problem.”277 Generative AI models, most ubiquitously built on deep neural networks with hundreds of billions of internal connections,278 have become so complex that their internal decision-making processes are no longer traceable or interpretable to even the most advanced expert observers. This means that, while the inputs and outputs of a system can be observed, developers cannot explain in detail why specific inputs correspond to specific outputs.\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"48.12.00","quick_ref":"NIST2024","paper_title":"Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile","level":"Risk Category","risk_category":"Value Chain and Component Integration ","risk_subcategory":null,"description":"\"Non-transparent or untraceable integration of \nupstream third-party components, including data that has been improperly obtained or not \nprocessed and cleaned due to increased automation from GAI; improper supplier vetting across the AI lifecycle; or other issues that diminish transparency or accountability for downstream \nusers.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"51.06.00","quick_ref":"Everitt2018 ","paper_title":"AGI Safety Literature Review ","level":"Risk Category","risk_category":"Intelligibility ","risk_subcategory":null,"description":"\"How can we build agent’s whose decisions we can understand? Con-\nnects explainable decisions (Berkeley) and informed oversight (MIRI).\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"56.06.00","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Lack of transparency and interpretability ","risk_subcategory":null,"description":"\"Today's Frontier AI is difficult to interpret and lacks transparency. Contextual understanding of the training data is not explicitly embedded within these models. They can fail to capture perspectives of underrepresented groups or the limitations within which they are expected to perform without fine tuning or reinforcement learning with human feedback (RLHF).\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":7,"subdomain":"7.4"},{"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":"59.18.00","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":"59.24.00","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":"61.02.15","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":"Complexity-induced knowledge gap","description":"\"The complexity of AI models and systems makes it challenging to demonstrate harm or establish a clear causal link between AI actions and their consequences.\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"61.02.37","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":"Opaque AI networks","description":"\"The complexity and opacity of AI models and systems make it difficult to predict and manage their behavior.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"62.16.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"General Evaluations (Difficulty of identification and measurement of capabilities)","description":"\"The capabilities of general-purpose AI systems can be difficult to measure, compared to the capabilities of more limited and fixed-purpose AI systems. This is in part due to a broader distribution of potential risks, a lack of well-defined metrics to evaluate these risks, and risks from unpredictable (or emergent) AI model properties.\"","entity":"Other","intent":"Other","timing":"Other","domain":7,"subdomain":"7.4"},{"ev_id":"62.18.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Model Evaluations (Interpretability/Explainability) ","risk_subcategory":null,"description":null,"entity":"Human","intent":"Other","timing":"Pre-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"62.18.05","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations (Interpretability/Explainability) ","risk_subcategory":"Model outputs inconsistent with chain-of-thought reasoning","description":"\"Chain-of-thought reasoning is sometimes employed to get a better understanding of the model’s output, where it encourages transparent reasoning in text form. However, in some cases, this reasoning is not consistent with the final answer given by the AI model, and as such does not give sufficient transparency [113].\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"62.19.10","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Attacks on GPAIs/GPAI Failure Modes ","risk_subcategory":"Lack of understanding of in-context learning in language models","description":"\"In-context learning allows the model to learn a new task or improve its perfor- mance by providing examples in the prompt, without changing its weights [101]. Even though this technique is highly effective, its working mechanism is not well understood. Since many potential misuses are directly related to prompting, it becomes difficult to guarantee safety when the exact mechanism of in-context learning is not fully investigated [13].\"","entity":"Other","intent":"Other","timing":"Other","domain":7,"subdomain":"7.4"},{"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":"65.17.01","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"},{"ev_id":"70.04.03","quick_ref":"Perlo2025","paper_title":"Embodied AI: Emerging Risks and Opportunities for Policy Action","level":"Risk Sub-Category","risk_category":"Social Risks ","risk_subcategory":"Lack of transparency, explainability, and trust","description":"\"Understanding how AI reaches conclusions or why AI systems perform specific actions motivates an entire branch of interpretability research [111], but physical embodiment raises the stakes for understanding these systems. For example, transparency of planned actions and explainability of decision-making is crucial when an AV suddenly changes lanes. A lack of transparency and explainability could lead to a lack of trust, which could become a critical and socially destabilizing issue with the widespread deployment of EAI [112–114].\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":7,"subdomain":"7.4"}]}