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Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization
Ensuring fairness is essential as machine learning increasingly informs consequential decisions. However, many fairness-aware methods focus on the outputs of individual predictors, without directly controlling sensitive information retained in the underlying representations. We propose Deep Fair Lea
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