MIT AI Risk Repository
Browse AI risks
15 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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"Laws and other restrictions can limit or prohibit the use of some data for specific AI use cases."
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"Laws and other regulations might limit the collection of certain types of data for specific AI use cases."
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"Laws and other restrictions can limit or prohibit transferring data."
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"Data contamination occurs when incorrect data is used for training. For example, data that is not aligned with model’s purpose or data that is already set aside for other development tasks such as testing and evaluation."
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"Unrepresentative data occurs when the training or fine-tuning data is not sufficiently representative of the underlying population or does not measure the phenomenon of interest."
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"Using undesirable output (for example, inaccurate, inappropriate, and user content) for retraining purposes can result in unexpected model behavior."
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"Improper collection and preparation of training or tuning data includes data label errors and by using data with conflicting information or misinformation."
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"Poor model accuracy occurs when a model’s performance is insufficient to the task it was designed for. Low accuracy might occur if the model is not correctly engineered, or there are changes to the model’s expected inputs."
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"When a model provides advice without having enough information, resulting in possible harm if the advice is followed."
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"Content might not be clearly disclosed as AI generated."
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"Without access to the training data, the types of explanations a model can provide are limited and more likely to be incorrect."
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"The content of the training data used for generating the model’s output is not accessible."
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"Explanations for model output decisions might be difficult, imprecise, or not possible to obtain."
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"Source attribution is the AI system's ability to describe from what training data it generated a portion or all its output. Since current techniques are based on approximations, these attributions might be incorrect."
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"Lack of model transparency is due to insufficient documentation of the model design, development, and evaluation process and the absence of insights into the inner workings of the model."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.