Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule

TL;DR

This paper proposes a novel reweighting scheme in empirical risk minimization to improve fairness by ensuring the sufficiency rule, using a bilevel formulation and discretized weights for faster training, validated through empirical experiments.

Contribution

It introduces a new reweighting approach based on sample weights to enhance fairness and generalization, differing from traditional size-based methods.

Findings

Improved fairness metrics across multiple datasets

Maintained or enhanced prediction accuracy

Faster training due to weight discretization

Abstract

We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhance fairness. This scheme aims to uphold the sufficiency rule in fairness by ensuring that optimal predictors maintain consistency across diverse sub-groups. We employ a bilevel formulation to address this challenge, wherein we explore sample reweighting strategies. Unlike conventional methods that hinge on model size, our formulation bases generalization complexity on the space of sample weights. We discretize the weights to improve training speed. Empirical validation of our method showcases its effectiveness and robustness, revealing a consistent improvement in the balance between prediction performance and fairness metrics across various experiments.