MIT AI Risk Repository
Browse AI risks
336 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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"Property damage - Action(s) that lead directly or indirectly to the damage or destruction of tangible property eg. buildings, possessions, vehicles, robots."
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58.03.00 · Risk Category
"Psychological - Direct or indirect impairment of the emotional and psychological mental health of an individual, organisation, or society."
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"Anxiety/depression - Mental health decline due to addiction, negative social interactions such as humiliation and shaming and traumatic distressing events such as online violence or rape."
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"Trauma - Severe and lasting emotional shock and pain caused by an extremely upsetting experience."
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58.04.00 · Risk Category
"Reputational - Damage to the reputation of an individual, group or organisation."
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"Loss of confidence/trust - Misleading or unfair change(s) in how an individual, group, or organisation is viewed, leading to loss of ability to conduct relationships, raise capital, recruit people, etc."
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"Opportunity loss - Loss of ability to take advantage of a financial or other opportunity, such as education, employability/securing a job."
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"Benefits/entitlements loss - Denial or or loss of access to welfare benefits, pensions, housing, etc due to the malfunction, use or abuse of a technology system."
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"Dignity loss - Perceived loss of value experienced by or disrespect shown to an individual or group, resulting in self-sheltering, loss of connections and relationships, and public stigmatisation."
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"Breach of ethics/values/norms - An actual or perceived violation or deviation from the established societal values, norms or ethical standards or principles."
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"Critical infrastructure damage - Damage, disruption to or destruction of systems essential to the functioning and safety of a nation or state, including energy, transport, health, finance, and communication systems."
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"The large-scale erosion or violation of fundamental human rights and freedoms."
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62.02.00 · Risk Category
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"A risk may be triggered by a human, where the AI serves merely as a tool, or by the AI acting autonomously with no human intervention, or it may involve a combination of both, with the human delegating some parts of decision-making to the AI. For risks where AI is the entity, these risks are exacerbated by an increase in the AI’s level of autonomy. To manage risks involving AI as the trigger, appropriate levels of human oversight can be built-in."
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"In the context of Normal Accident Theory [150], normal accidents are those that “could no longer be ascribed to isolated equipment malfunction, operator error, or acts of God.” We refer to these as “system failures” (to be distinguished from “systemic risks”), while the opposite would be “isolated failures.” For isolated failures, harms are consistent with the underlying failure modes. For example, an AI capable of producing false or misleading content would constitute risks re- lated to misinformation and disinformation. Whereas for system failures, harms are not consistent with the underlyi
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"In the context of Normal Accident Theory [150], normal accidents are those that “could no longer be ascribed to isolated equipment malfunction, operator error, or acts of God.” We refer to these as “system failures” (to be distinguished from “systemic risks”), while the opposite would be “isolated failures.” For isolated failures, harms are consistent with the underlying failure modes. For example, an AI capable of producing false or misleading content would constitute risks re- lated to misinformation and disinformation. Whereas for system failures, harms are not consistent with the underlyi
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"As above, there are broadly two dimensions of technical failure modes: quality of data or input signal, and training performance. Due to a lack of transparency, it may be difficult to ascertain the type of technical failure that gives rise to a particular risk, and it is often a combination of several factors. Risks pertain- ing to AI failures are exacerbated by poor quality training data and imperfect training signals. Various measures can be implemented to improve the quality of the training data, and fine-tuning techniques can be used to disincentivize harmful model behavior."
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62.04.01 · Risk Sub-Category
Dimension - Technical Attributes (AI inadequacy - technical failure)
Supervised/unsupervised AI (AI data quality related - biased training data)
"As above, there are broadly two dimensions of technical failure modes: quality of data or input signal, and training performance. Due to a lack of transparency, it may be difficult to ascertain the type of technical failure that gives rise to a particular risk, and it is often a combination of several factors. Risks pertain- ing to AI failures are exacerbated by poor quality training data and imperfect training signals. Various measures can be implemented to improve the quality of the training data, and fine-tuning techniques can be used to disincentivize harmful model behavior."
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62.04.02 · Risk Sub-Category
Dimension - Technical Attributes (AI inadequacy - technical failure)
Supervised/unsupervised AI (AI training performance related - Robustness)
"As above, there are broadly two dimensions of technical failure modes: quality of data or input signal, and training performance. Due to a lack of transparency, it may be difficult to ascertain the type of technical failure that gives rise to a particular risk, and it is often a combination of several factors. Risks pertain- ing to AI failures are exacerbated by poor quality training data and imperfect training signals. Various measures can be implemented to improve the quality of the training data, and fine-tuning techniques can be used to disincentivize harmful model behavior."
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62.04.03 · Risk Sub-Category
Dimension - Technical Attributes (AI inadequacy - technical failure)
Supervised/unsupervised AI (AI training performance related - Accuracy)
"As above, there are broadly two dimensions of technical failure modes: quality of data or input signal, and training performance. Due to a lack of transparency, it may be difficult to ascertain the type of technical failure that gives rise to a particular risk, and it is often a combination of several factors. Risks pertain- ing to AI failures are exacerbated by poor quality training data and imperfect training signals. Various measures can be implemented to improve the quality of the training data, and fine-tuning techniques can be used to disincentivize harmful model behavior."
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62.04.04 · Risk Sub-Category
Dimension - Technical Attributes (AI inadequacy - technical failure)
Supervised/unsupervised AI (AI training performance related - Reliability)
"As above, there are broadly two dimensions of technical failure modes: quality of data or input signal, and training performance. Due to a lack of transparency, it may be difficult to ascertain the type of technical failure that gives rise to a particular risk, and it is often a combination of several factors. Risks pertain- ing to AI failures are exacerbated by poor quality training data and imperfect training signals. Various measures can be implemented to improve the quality of the training data, and fine-tuning techniques can be used to disincentivize harmful model behavior."
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62.04.05 · Risk Sub-Category
Dimension - Technical Attributes (AI inadequacy - technical failure)
Reinforcement learning AI (Training design related)
"As above, there are broadly two dimensions of technical failure modes: quality of data or input signal, and training performance. Due to a lack of transparency, it may be difficult to ascertain the type of technical failure that gives rise to a particular risk, and it is often a combination of several factors. Risks pertain- ing to AI failures are exacerbated by poor quality training data and imperfect training signals. Various measures can be implemented to improve the quality of the training data, and fine-tuning techniques can be used to disincentivize harmful model behavior."
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62.04.06 · Risk Sub-Category
Dimension - Technical Attributes (AI inadequacy - technical failure)
Reinforcement learning AI (Training performance related)
"As above, there are broadly two dimensions of technical failure modes: quality of data or input signal, and training performance. Due to a lack of transparency, it may be difficult to ascertain the type of technical failure that gives rise to a particular risk, and it is often a combination of several factors. Risks pertain- ing to AI failures are exacerbated by poor quality training data and imperfect training signals. Various measures can be implemented to improve the quality of the training data, and fine-tuning techniques can be used to disincentivize harmful model behavior."
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62.05.00 · Risk Category
"An example of AI capabilities is that an AI might be capable of developing novel bioweapons. Whereas an example of AI inadequacy is a self-driving car causing an accident due to not being able to recognize certain objects. The boundary between capabilities and inadequacy is sometimes blurred. For exam- ple, when an AI generates falsehoods, it could be framed as either a capability of developing fiction, or an inadequacy in generating truthful content."
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"Inherent capabilities are inherent to the AI, whether they are deliberately trained or have emerged unintentionally."
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"Extrinsic capabilities, on the other hand, are acquired through the use of external tools, such as LLM plugins."
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62.27.00 · Risk Category
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"An actual or perceived violation or deviation from the established societal values, norms or ethical standards or principles"
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"Loss of money, income or value due to the use, misuse, or underperformance of a genAI application"
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"Physical pain, injury, illness, or disease suffered by an individual or group due to the malfunction, use or misuse of a technology system."
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"Physical deterioration of an individual or animal over time in the form of disease, organ failure, prolonged hospital stay or death, etc"
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67.01.00 · Risk Category
"There is a wide range of potential societal harms arising from the use of AI.152 This has sparked a debate around the ethics of AI, with a wide proliferation of ethical frameworks and principles.153 We focus here on only a few societal harms, but this is not to downplay the importance of others."
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"Unpredictable and unforeseen outcomes from purposeful actions"
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72.04.00 · Risk Category
"Systemic risks emerge from widespread deployment of general-purpose AI beyond the risks directly posed by capabilities of individual models. These risks arise from structural mismatches between AI technology and existing social, economic, and institutional frameworks, creating vulnerabilities that transcend individual model-level interventions and require coordinated industry-wide and societal-level responses."
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73.05.00 · Risk Category
"The rapid evolution of LLMs brings significant socioeconomic opportunities and challenges, impacting the workforce, income inequality, education, and global economic development. Many of these challenges are systemic in nature, constituting what economists refer to as general equilibrium effects. These challenges do not arise directly from LLMs causing harm to users but rather from their indirect effects on the socioeconomic equilibrium."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.