MIT AI Risk Repository

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2 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.

2 entries

  1. 16.03.01 · Risk Sub-Category

    Risk area 3: Misinformation Harms

    Disseminating false or misleading information

    "Where a LM prediction causes a false belief in a user, this may threaten personal autonomy and even pose downstream AI safety risks [99]."

    From Taxonomy of Risks posed by Language Models (Weidinger2022)

  2. 16.03.02 · Risk Sub-Category

    Risk area 3: Misinformation Harms

    Causing material harm by disseminating false or poor information e.g. in medicine or law

    "Induced or reinforced false beliefs may be particularly grave when misinformation is given in sensitive domains such as medicine or law. For example, misin- formation on medical dosages may lead a user to cause harm to themselves [21, 130]. False legal advice, e.g. on permitted owner- ship of drugs or weapons, may lead a user to unwillingly commit a crime. Harm can also result from misinformation in seemingly non-sensitive domains, such as weather forecasting. Where a LM prediction endorses unethical views or behaviours, it may motivate the user to perform harmful actions that they may otherw

    From Taxonomy of Risks posed by Language Models (Weidinger2022)

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