MIT AI Risk Repository
Browse AI risks
7 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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"The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"
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02.08.00 · Risk Category
"Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data."
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"Compared with the definition of toxicity, the definition of bias is more subjective and contextdependent. Based on previous work [97], [101], we describe the bias as disparities that could raise demographic differences among various groups, which may involve demographic word prevalence and stereotypical contents. Concretely, in massive corpora, the prevalence of different pronouns and identities could influence an LLM’s tendency about gender, nationality, race, religion, and culture [4]. For instance, the pronoun He is over-represented compared with the pronoun She in the training corpora, le
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02.01.00 · Risk Category
"The LLM-generated content sometimes contains biased, toxic, and private information"
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"Toxicity means the generated content contains rude, disrespectful, and even illegal information"
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"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]."
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02.11.00 · Risk Category
"Inputting a prompt contain an unsafe topic (e.g., notsuitable-for-work (NSFW) content) by a benign user. "
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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.