Fairness Through Independence via Cramér-von Mises Regularization

Albert Gimó Contreras, Mariia Vladimirova, Federico Pavone, Reda CHHAIBI

Keywords: fairness, bias, tabular data, optimization, dependence

TL;DR: We propose a new in-training CvM dependence regularizer that steers the fairness–utility trade-off and efficiently reduces demographic-parity gaps at scale.

Abstract: Controlling fairness in machine learning model outputs is challenging due to complex, unstable and computationally expensive techniques for bias estimation on finite data samples. We propose a simple in-processing method to control group fairness during training by penalizing statistical dependence between model outputs $\hat{Y}$ and a sensitive attribute $S$. Our approach instantiates the Cramér--von Mises (CvM) dependence coefficient $\xi(S,\hat{Y})$ as a bounded, differentiable regularizer that integrates seamlessly with stochastic optimization. The resulting objective $L+\lambda \xi(S,\hat{Y})$ positions models along a fairness–utility Pareto frontier through a single multiplier $\lambda$. Our experiments demonstrate the effectiveness of this method for controlling the fairness-utility trade-off in both fairness-aware small and large tabular datasets. In order to control the compromise between fairness metrics and performance metrics, we propose a task-agnostic hyperparameter tuning pipeline and showcase its effectiveness in a large tabular dataset. In practice, we have observed that controlling for CvM leads to lower demographic-parity (DP) scores, providing a tractable and computationally efficient methodology, bridging the gap between policy requirements on DP and scalable training procedure for ML models.

Submission Number: 208