MIT AI Risk Repository
Browse AI risks
2,500 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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16.06.02.b · Additional evidence
Risk area 6: Environmental and Socioeconomic harms
Increasing inequality and negative effects on job quality
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17.01.01.a · Additional evidence
Discrimination, Exclusion and Toxicity
Social stereotypes and unfair discrmination
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17.01.04.a · Additional evidence
Discrimination, Exclusion and Toxicity
Lower performance for some languages and social groups
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17.02.01.a · Additional evidence
Compromising privacy by leaking private infiormation
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17.02.01.b · Additional evidence
Compromising privacy by leaking private infiormation
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17.02.02.a · Additional evidence
Compromising privacy by correctly inferring private information
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17.02.03.a · Additional evidence
Risks from leaking or correctly inferring sensitive information
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17.02.03.b · Additional evidence
Risks from leaking or correctly inferring sensitive information
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17.03.02.a · Additional evidence
Causing material harm by disseminating false or poor information
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17.03.02.b · Additional evidence
Causing material harm by disseminating false or poor information
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17.04.02.a · Additional evidence
Facilitating fraud, scames and more targeted manipulation
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17.04.02.b · Additional evidence
Facilitating fraud, scames and more targeted manipulation
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17.05.02.a · Additional evidence
Human-Computer Interaction Harms
Creating avenues for exploiting user trust, nudging or manipulation
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17.05.02.b · Additional evidence
Human-Computer Interaction Harms
Creating avenues for exploiting user trust, nudging or manipulation
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17.05.02.c · Additional evidence
Human-Computer Interaction Harms
Creating avenues for exploiting user trust, nudging or manipulation
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17.05.03.a · Additional evidence
Human-Computer Interaction Harms
Promoting harmful stereotypes by implying gender or ethnic identity
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17.06.01.a · Additional evidence
Automation, Access and Environmental Harms
Environmental harms from operation LMs
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17.06.01.b · Additional evidence
Automation, Access and Environmental Harms
Environmental harms from operation LMs
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17.06.02.a · Additional evidence
Automation, Access and Environmental Harms
Increasing inequality and negative effects on job quality
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17.06.02.b · Additional evidence
Automation, Access and Environmental Harms
Increasing inequality and negative effects on job quality
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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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20.01.01.a · Additional evidence
Governance of autonomous intelligence systems
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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
N/A
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21.02.00 · Risk Category
N/A
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Disclaimer: informational only, not legal advice. Verify every claim against the linked official sources and consult a qualified lawyer before acting.
Frequently asked questions
Where do these risk entries come from?
The MIT AI Risk Repository, which extracts risks from dozens of published frameworks, taxonomies and papers and codes each one by domain and subdomain, and by a causal taxonomy of entity, intent and timing. This is a browseable copy, attributed and openly licensed, not original research.
What do entity, intent and timing mean?
They are the causal coding. Entity is whether a human or the AI system is the cause; intent is whether the harm was intentional or not; timing is whether it arises before or after deployment. Together they let you separate misuse from malfunction.
Can I export the results?
Yes, any filtered set exports as CSV or JSON, and every export carries the upstream source, licence and citation with it, because attribution is a condition of the licence rather than a courtesy.
What is the MIT AI Risk Repository?
A living database of AI risks extracted from published frameworks, taxonomies and papers, classified by a causal taxonomy of entity, intent and timing and by a domain taxonomy of seven domains and 24 subdomains. It is published by the MIT AI Risk Initiative under CC BY 4.0.