Artificial Intelligence Policy Tracker

MIT AI Risk Repository

Browse AI risks

6 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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6 entries

  1. 66.01.01 · Risk Sub-Category

    Autonomy

    Impersonation / identity theft

    "Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them or another party"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  2. 66.01.02 · Risk Sub-Category

    Autonomy

    IP / copyright / personality / rights loss

    "Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents. & Loss of or restrictions to the rights of an individual to control the commercial use of their identity, such as name, image, likeness, or other unequivocal identifiers"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  3. 66.02.03 · Risk Sub-Category

    Political and Economic

    Economic manipulation

    "Generative AI facilitating targeted manipulation of public opinion for economic purposes (e.g., inflating stock prices)"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  4. 66.04.05 · Risk Sub-Category

    Societal and Cultural

    Cheating / plagiarism

    "Use of generative AI in an academic setting to either cheat or plagiarize"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  5. 66.05.02 · Risk Sub-Category

    Reputational

    Defamation / libel / slander

    "Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group or organisation"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  6. 66.07.01 · Risk Sub-Category

    Psychological

    Sexualization

    "The non-consensual sexualisation of an individual or group using a technology or application"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

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.