MIT AI Risk Repository
Browse AI risks
24 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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"A systematic error, a tendency to learn consistently wrongly."
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"Impartial and just treatment without favouritism or discrimination."
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42.09.00 · Risk Category
"Vulnerable channel by which personal information may be accessed. The user may want their personal data to be kept private."
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42.02.00 · Risk Category
"The predictability of behaviour protocol in AI, particularly in some applications, can act an incentive to manipulate these systems."
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"Implications of the weaponization of AI for defence (the embeddedness of AI-based capabilities across the land, air, naval and space domains may affect combined arms operations)."
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"The political influence and competitive advantage obtained by having technology."
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"When it causes harm to others the losses caused by the harm will be sustained by the injured victims themselves and not by the manufacturers, operators or users of the system, as appropriate."
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42.17.00 · Risk Category
"A possible consequence of self-interest in AI generation of ethical guidelines."
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"Risk to the existence of humanity."
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"The assessment of how often a system performs the correct prediction."
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"Less moral responsibility humans will feel regarding their life-or-death decisions with the increase of machines autonomy."
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42.11.00 · Risk Category
"'Gaps' that arise across the development process where normal conditions for a complete specification of intended functionality and moral responsibility are not present."
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42.15.00 · Risk Category
"Reliability is defined as the probability that the system performs satisfactorily for a given period of time under stated conditions."
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42.01.00 · Risk Category
"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."
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"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."
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42.21.00 · Risk Category
"Any action or procedure performed by a model with the intention of clarifying or detailing its internal functions."
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42.07.00 · Risk Category
"Describe the operation of a system in an accurate way."
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42.13.00 · Risk Category
"Data quality is the measure of how well suited a data set is to serve its specific purpose."
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"Difference between the implicit intentions on the system's functionality and the explicit, concrete specification that is used to build the system."
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42.18.00 · Risk Category
"Describe the internals of a system in a way that is understandable to humans."
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42.19.00 · Risk Category
"The difference between a human actor being involved in the causation of an outcome and having the sort of robust control that establishes moral accountability for the outcome."
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"Ethical aspects of people's attitudes to AI, and on the other, problems associated with AI itself."
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"Set of actions and resources used to protect something or someone."
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42.24.00 · Risk Category
"The quality or state of being transparent."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.