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This paper introduces Deep Fair Learning (DFL), a novel framework that enforces fairness directly within the learned representations of machine learning models rather than just at the output level. By jointly optimizing for distance covariance regularization and predictive loss, the method effectively reduces downstream disparities while maintaining high task accuracy across tabular, text, and image domains. Theoretical analysis confirms convergence properties and establishes bounds linking representation-level independence to final prediction fairness, offering a robust approach for consequential decision-making systems.
Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization
arXiv:2504.06470v2 Announce Type: replace-cross Abstract: 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 Learning (DFL), which combines distance covariance regularization with predictive loss to jointly learn representations and downstream predictors, promoting fairness at both levels while preserving task-relevant information. Its marginal and class-conditional formulations target independence and separation, respectively. Under suitable regularity conditions, we establish non-asymptotic joint excess-risk rates and convergence of the learned representation up to natural invariances. We further derive fairness-inheritance bounds linking representation-level dependence to downstream disparities over suitable predictor classes, extending fairness guarantees beyond the jointly trained predictor. Experiments on tabular, text, and image benchmarks show that DFL achieves lower fairness gaps than competing methods in many evaluated settings while maintaining competitive predictive accuracy, with fairness gains largely preserved after downstream retraining.
Abstract: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 Learning (DFL), which combines distance covariance regularization with predictive loss to jointly learn representations and downstream predictors, promoting fairness at both levels while preserving task-relevant information. Its marginal and class-conditional formulations target independence and separation, respectively. Under suitable regularity conditions, we establish non-asymptotic joint excess-risk rates and convergence of the learned representation up to natural invariances. We further derive fairness-inheritance bounds linking representation-level dependence to downstream disparities over suitable predictor classes, extending fairness guarantees beyond the jointly trained predictor. Experiments on tabular, text, and image benchmarks show that DFL achieves lower fairness gaps than competing methods in many evaluated settings while maintaining competitive predictive accuracy, with fairness gains largely preserved after downstream retraining.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2504.06470 [stat.ML]
(or arXiv:2504.06470v2 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2504.06470 Focus to learn more arXiv-issued DOI via DataCite
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