Systematic error in a model's outputs that disadvantages particular groups, typically arising from unrepresentative training data, biased proxy variables, or design choices. A widely cited example is a US care-management algorithm that used healthcare cost as a proxy for clinical need and consequently underestimated the needs of Black patients. Mitigation includes representative training data, evaluation across population subgroups, and continued monitoring after deployment.
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