MIT AI Risk Repository

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4 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.

4 entries

  1. 02.01.00 · Risk Category

    Harmful Content

    "The LLM-generated content sometimes contains biased, toxic, and private information"

    From Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  2. 02.01.02 · Risk Sub-Category

    Harmful Content

    Toxicity

    "Toxicity means the generated content contains rude, disrespectful, and even illegal information"

    From Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  3. 02.08.01 · Risk Sub-Category

    Toxicity and Bias Tendencies

    Toxic Training Data

    "Following previous studies [96], [97], toxic data in LLMs is defined as rude, disrespectful, or unreasonable language that is opposite to a polite, positive, and healthy language environment, including hate speech, offensive utterance, profanities, and threats [91]."

    From Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  4. "Inputting a prompt contain an unsafe topic (e.g., notsuitable-for-work (NSFW) content) by a benign user. "

    From Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

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