MIT AI Risk Repository
Browse AI risks
242 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.
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62.08.06 · Risk Sub-Category
Direct Harm Domains (content safety harms)
Dangerous content (e.g., CBRN)
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47.04.00 · Risk Category
"Beyond the risks associated with AI technology and its applications, and the legal challenges arising from its development, it is crucial to consider other long- term issues posed by the deployment of increasingly advanced generative AI models. These risks to society, sometimes referred to as “systemic risks,”537 encompass several key areas: the potential for excessive market concentration, the impacts on employment, environmental consequences, and broader risks to humanity."
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58.07.00 · Risk Category
"Societal and Cultural - Harms affecting the functioning of societies, communities and economies caused directly or indirectly by the use or misuse technology systems."
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70.03.00 · Risk Category
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62.13.01 · Risk Sub-Category
Negative Externality Domains (Other harms from AI development and use)
Societal inequality (individuals and companies who develop the best AIs get disproportionately powerful)
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62.13.02 · Risk Sub-Category
Negative Externality Domains (Other harms from AI development and use)
Geopolitical harms (potential for conflict due to power imbalances)
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62.11.02 · Risk Sub-Category
Negative Externality Domains (Manufacturing of AI Hardware)
Human rights harms from exploitation of human labour
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05.11.00 · Risk Category
In response to the multitude of new risks associated with generative AI, papers advocate for legal regulation and governmental oversight. The focus of these discussions centers on the need for international coordination in AI governance, the establishment of binding safety standards for frontier models, and the development of mechanisms to sanction non-compliance. Furthermore, the literature emphasizes the necessity for regulators to gain detailed insights into the research and development processes within AI labs. Moreover, risk management strategies of these labs shall be evaluated. However,
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19.06.05 · Risk Sub-Category
Capturing future AI development and their threats with appropriate mechanism
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62.11.00 · Risk Category
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62.11.01 · Risk Sub-Category
Negative Externality Domains (Manufacturing of AI Hardware)
Environmental harms from exploitation of natural resources
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62.12.00 · Risk Category
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62.12.01 · Risk Sub-Category
Negative Externality Domains (Running AI Hardware)
Environmental harms from energy usage
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62.07.02 · Risk Sub-Category
Direct Harm Domains (system and operational)
Operational harms (financial markets)
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"This section catalogs the risk sources and risk management measures related to agentic AI systems. We categorize these into the following groups: goal- directedness, deception, situational awareness, self-proliferation, and persuasion"
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62.22.00 · Risk Category
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62.24.00 · Risk Category
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72.05.00 · Risk Category
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62.07.03 · Risk Sub-Category
Direct Harm Domains (system and operational)
Operational harms (critical infrastructure)
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62.07.04 · Risk Sub-Category
Direct Harm Domains (system and operational)
Operational harms (other physical systems e.g., transport)
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62.15.08 · Risk Sub-Category
Fine-tuning related (Excessive or overly restrictive safety-tuning)
"Excessive safety training or safety tuning can impair the performance of AI systems, leading to overly cautious behavior. As a result, these systems may refuse to answer entirely safe prompts which are partially similar to harmful ones [27]."
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05.13.00 · Risk Category
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.
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05.14.00 · Risk Category
Closely related to other clusters like AI safety, fairness, or harmful content, papers stress the importance of evaluating generative AI systems both in a narrow technical way as well as in a broader sociotechnical impact assessment focusing on pre-release audits as well as post-deployment monitoring. Ideally, these evaluations should be conducted by independent third parties. In terms of technical LLM or text-to-image model audits, papers furthermore criticize a lack of safety benchmarking for languages other than English.
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05.19.00 · Risk Category
While the scoping review identified distinct topic clusters within the literature, it also revealed certain issues that either do not fit into these categories, are discussed infrequently, or in a nonspecific manner. For instance, some papers touch upon concepts like trustworthiness, accountability, or responsibility, but often remain vague about what they entail in detail. Similarly, a few papers vaguely attribute socio-political instability or polarization to generative AI without delving into specifics. Apart from that, another minor topic area concerns responsible approaches of talking abo
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09.01.00 · Risk Category
Domain-specific AI - Effects on humans and other living beings: Existential Risks
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09.02.00 · Risk Category
Domain-specific AI - Effects on humans and other living beings: Non-existential risks
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09.05.00 · Risk Category
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09.06.00 · Risk Category
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10.08.00 · Risk Category
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13.01.00 · Risk Category
"What can be evaluated in a technical system and its components'...The following categories are high-level, non-exhaustive, and present a synthesis of the findings across different modalities"
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13.02.00 · Risk Category
"what can be evaluated among people and society"
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19.01.00 · Risk Category
"Fig 3 shows that technological, data, and analytical AI risks are characterised by the loss of control over AI systems, whereby in particular the autonomous decision and its consequences are classified as risk factors since they are not subject to human influence (Boyd & Wilson, 2017; Scherer, 2016; Wirtz et al., 2019). Programming errors in algorithms due to the lack of expert knowledge or to the increasing complexity and black-box character of AI systems may also lead to undesired AI results (Boyd & Wilson, 2017; Danaher et al., 2017). In addition, a lack of data, poor data quality, and bia
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"Social acceptance and trust in AI is highly interconnected with the other challenges mentioned. Acceptance and trust result from the extent to which an individual’s subjective expectation corresponds to the real effect of AI on the individual’s life. In the case of transparent and explainable AI, acceptance may be high but if an individual encounters harmful AI behavior like discrimination, acceptance for AI will eventually decline (COMEST, 2017).
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21.01.00 · Risk Category
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21.02.00 · Risk Category
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22.03.00 · Risk Category
"An essential factor in preventing accidents and maintaining low levels of risk lies in the organizations responsible for these technologies."
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22.03.02 · Risk Sub-Category
Organizational Risks (Accidental)
Organizational Factors can Reduce the Chances of Catastrophe
"Some organizations successfully avoid catastrophes while operating complex and hazardous systems such as nuclear reactors, aircraft carriers, and air traffic control systems [92, 93]. These organizations recognize that focusing solely on the hazards of the technology involved is insufficient; consideration must also be given to organizational factors that can contribute to accidents, including human factors, organizational procedures, and structure. These are especially important in the case of AI, where the underlying technology is not highly reliable and remains poorly understood"
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26.01.00 · Risk Category
"Ability to provide responsible disclosure to those affected by AI systems to understand the outcome"
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.