MIT AI Risk Repository
Browse AI risks
9 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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"Without accurate documentation on how a model's data was collected, curated, and used to train a model, it might be harder to satisfactorily explain the behavior of the model with respect to the data."
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"Data provenance refers to tracing history of data, which includes its ownership, origin, and transformations. Without standardized and established methods for verifying where the data came from, there are no guarantees that the data is the same as the original source and has the correct usage terms."
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"Determining who is responsible for an AI model is challenging without good documentation and governance processes."
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"Insufficient documentation of the system that uses the model and the model’s purpose within the system in which it is used."
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"Testing is unrepresentative when the test inputs are mismatched with the inputs that are expected during deployment."
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"Since foundation models can be used for many purposes, a model’s intended use is important for defining the relevant risks of that model. As the use changes, the relevant risks might correspondingly change."
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"Lack of data transparency is due to insufficient documentation of training or tuning dataset details. "
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"A metric selected to measure or track a risk is incorrectly selected, incompletely measuring the risk, or measuring the wrong risk for the given context."
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"AI model risks are socio-technical, so their testing needs input from a broad set of disciplines and diverse testing practices."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.