aiqbench analysis
This paper gives a statistical foundation for a common practice in fairness-aware machine learning: optimizing a convex surrogate loss instead of the original fairness-constrained objective. It defines fairness-aware Fisher consistency under demographic parity and equal opportunity, ties it to derivative conditions on the surrogate losses and the fairness regularizer, and derives a calibration inequality bounding excess fairness-aware risk by excess surrogate risk. Readers building fair classifiers should note that consistency depends on how the loss and the regularization term interact, not on the loss alone, so a surrogate that looks sound in isolation can still fail under a given fairness penalty.
Surrogate Losses for Fairness-Aware Classification: Fisher Consistency and Calibration
arXiv:2611.01613v1 Announce Type: cross Abstract: Fairness-aware classification seeks to balance predictive accuracy and fairness across demographic groups. Although surrogate losses facilitate the optimization of fairness-aware objectives, it remains unclear when minimizing these losses yields optimal decisions under the original fairness-aware risk. This paper investigates the statistical validity of surrogate-based fairness-aware classification under demographic parity and equal opportunity, considering both attribute-blind and attribute-aware settings. We introduce fairness-aware Fisher consistency and characterize it through conditions on the derivatives of the surrogate losses. The characterization shows that consistency depends not only on the individual loss functions but also on their interaction within the fairness-regularized objective. We further establish a calibration inequality that relates the excess fairness-aware risk to the excess surrogate risk, providing a quantitative connection between surrogate optimization and the original classification objective. Numerical experiments illustrate how deviations from the consistency conditions affect fairness-aware excess risk and how the regularization parameter influences the well-posedness of surrogate optimization. An application to consumer credit default prediction demonstrates that appropriately calibrated fairness regularization can substantially reduce demographic disparities while maintaining predictive performance.
Abstract:Fairness-aware classification seeks to balance predictive accuracy and fairness across demographic groups. Although surrogate losses facilitate the optimization of fairness-aware objectives, it remains unclear when minimizing these losses yields optimal decisions under the original fairness-aware risk. This paper investigates the statistical validity of surrogate-based fairness-aware classification under demographic parity and equal opportunity, considering both attribute-blind and attribute-aware settings. We introduce fairness-aware Fisher consistency and characterize it through conditions on the derivatives of the surrogate losses. The characterization shows that consistency depends not only on the individual loss functions but also on their interaction within the fairness-regularized objective. We further establish a calibration inequality that relates the excess fairness-aware risk to the excess surrogate risk, providing a quantitative connection between surrogate optimization and the original classification objective. Numerical experiments illustrate how deviations from the consistency conditions affect fairness-aware excess risk and how the regularization parameter influences the well-posedness of surrogate optimization. An application to consumer credit default prediction demonstrates that appropriately calibrated fairness regularization can substantially reduce demographic disparities while maintaining predictive performance.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2611.01613 [stat.ML]
(or arXiv:2611.01613v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2611.01613 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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