MIT AI Risk Repository

Browse AI risks

3 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.

3 entries

  1. 27.01.01 · Risk Sub-Category

    Typical safety scenarios

    Insult

    "Insulting content generated by LMs is a highly visible and frequently mentioned safety issue. Mostly, it is unfriendly, disrespectful, or ridiculous content that makes users uncomfortable and drives them away. It is extremely hazardous and could have negative social consequences."

    From Safety Assessment of Chinese Large Language Models (Sun2023)

  2. 27.01.03 · Risk Sub-Category

    Typical safety scenarios

    Crimes and Illegal Activities

    "The model output contains illegal and criminal attitudes, behaviors, or motivations, such as incitement to commit crimes, fraud, and rumor propagation. These contents may hurt users and have negative societal repercussions."

    From Safety Assessment of Chinese Large Language Models (Sun2023)

  3. 27.01.04 · Risk Sub-Category

    Typical safety scenarios

    Sensitive Topics

    "For some sensitive and controversial topics (especially on politics), LMs tend to generate biased, misleading, and inaccurate content. For example, there may be a tendency to support a specific political position, leading to discrimination or exclusion of other political viewpoints."

    From Safety Assessment of Chinese Large Language Models (Sun2023)

Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.