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.
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16.02.00 · Risk Category
"LM predictions that convey true information may give rise to information hazards, whereby the dissemination of private or sensitive information can cause harm [27]. Information hazards can cause harm at the point of use, even with no mistake of the technology user. For example, revealing trade secrets can damage a business, revealing a health diagnosis can cause emotional distress, and revealing private data can violate a person’s rights. Information hazards arise from the LM providing private data or sensitive information that is present in, or can be inferred from, training data. Observed r
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16.02.01 · Risk Sub-Category
Risk area 2: Information Hazards
Compromising privacy by leaking sensitive information
"A LM can “remember” and leak private data, if such information is present in training data, causing privacy violations [34]."
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16.02.02 · Risk Sub-Category
Risk area 2: Information Hazards
Compromising privacy or security by correctly inferring sensitive information
Anticipated risk: "Privacy violations may occur at inference time even without an individual’s data being present in the training corpus. Insofar as LMs can be used to improve the accuracy of inferences on protected traits such as the sexual orientation, gender, or religiousness of the person providing the input prompt, they may facilitate the creation of detailed profiles of individuals comprising true and sensitive information without the knowledge or consent of the individual."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.