FaiR-N: Fair and Robust Neural Networks for Structured Data

TL;DR

This paper introduces FaiR-N, a neural network training method that enhances fairness by equalizing recourse ability across groups and improves robustness without sacrificing accuracy.

Contribution

It proposes a novel loss function that considers distance to the decision boundary to promote fairness and robustness, addressing recourse disparity for the first time.

Findings

Training with the new loss improves fairness and robustness.

Models achieve similar accuracy to standard training methods.

Reducing recourse disparity also enhances error rate fairness.

Abstract

Fairness in machine learning is crucial when individuals are subject to automated decisions made by models in high-stake domains. Organizations that employ these models may also need to satisfy regulations that promote responsible and ethical A.I. While fairness metrics relying on comparing model error rates across subpopulations have been widely investigated for the detection and mitigation of bias, fairness in terms of the equalized ability to achieve recourse for different protected attribute groups has been relatively unexplored. We present a novel formulation for training neural networks that considers the distance of data points to the decision boundary such that the new objective: (1) reduces the average distance to the decision boundary between two groups for individuals subject to a negative outcome in each group, i.e. the network is more fair with respect to the ability to obtain recourse, and (2) increases the average distance of data points to the boundary to promote adversarial robustness. We demonstrate that training with this loss yields more fair and robust neural networks with similar accuracies to models trained without it. Moreover, we qualitatively motivate and empirically show that reducing recourse disparity across groups also improves fairness measures that rely on error rates. To the best of our knowledge, this is the first time that recourse capabilities across groups are considered to train fairer neural networks, and a relation between error rates based fairness and recourse based fairness is investigated.