Instruction: Discuss your methodology for evaluating and ensuring that ML models are fair and unbiased across different demographic groups.
Context: This question is designed to assess the candidate's commitment to and strategies for promoting fairness in ML models.
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I start by defining which groups and which fairness harms matter in the use case, because fairness is not one generic metric. Then I evaluate performance, calibration, and error disparities across those groups using data that reflects real deployment...