Artificial Intelligence Policy Tracker

MIT AI Risk Repository

Browse AI risks

8 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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8 entries

  1. 30.02.00 · Risk Category

    Safety

    Avoiding unsafe and illegal outputs, and leaking private information

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  2. 30.02.01 · Risk Sub-Category

    Safety

    Violence

    LLMs are found to generate answers that contain violent content or generate content that responds to questions that solicit information about violent behaviors

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  3. 30.02.02 · Risk Sub-Category

    Safety

    Unlawful Conduct

    LLMs have been shown to be a convenient tool for soliciting advice on accessing, purchasing (illegally), and creating illegal substances, as well as for dangerous use of them

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  4. 30.02.03 · Risk Sub-Category

    Safety

    Harms to Minor

    LLMs can be leveraged to solicit answers that contain harmful content to children and youth

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  5. 30.02.04 · Risk Sub-Category

    Safety

    Adult Content

    LLMs have the capability to generate sex-explicit conversations, and erotic texts, and to recommend websites with sexual content

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  6. 30.06.00 · Risk Category

    Social Norm

    LLMs are expected to reflect social values by avoiding the use of offensive language toward specific groups of users, being sensitive to topics that can create instability, as well as being sympathetic when users are seeking emotional support

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  7. 30.06.01 · Risk Sub-Category

    Social Norm

    Toxicity

    language being rude, disrespectful, threatening, or identity-attacking toward certain groups of the user population (culture, race, and gender etc)

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  8. 30.06.03 · Risk Sub-Category

    Social Norm

    Cultural Insensitivity

    it is important to build high-quality locally collected datasets that reflect views from local users to align a model’s value system

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

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.