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AI Risk

Biases are not accurately reflected in explanations

"Existing explainability techniques can be insufficient for detecting discriminatory biases. Manipulation methods can hide underlying biases from these tech- niques, generating misleading explanations [192, 112]. Such explanations ex- clude sensitive or prohibitive attributes, such as race or gender, and instead include desired attributes, even though they do not accurately represent the underlying model."

AI Risk1. Discrimination & Toxicity1.1 > Unfair discrimination and misrepresentation3 - Other

Record summary

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Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain1. Discrimination & ToxicityThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

Domain1. Discrimination & Toxicity
Subdomain1.1 > Unfair discrimination and misrepresentation
Entity3 - Other
Intent3 - Other
Timing3 - Other
CategoryModel Evaluations (Interpretability/Explainability)
SubcategoryBiases are not accurately reflected in explanations

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

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