Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization

TL;DR

This paper proposes a novel regularization method based on Bias Parity Score to improve fairness in neural network predictions without sacrificing accuracy, demonstrated on recidivism and income datasets.

Contribution

It introduces a family of BPS-based fairness regularization components that enhance model fairness during training, a novel approach in fairness-aware machine learning.

Findings

Reduces bias in models without accuracy loss

Effective on unbalanced datasets

Applicable to recidivism and income prediction tasks

Abstract

Increasing utilization of machine learning based decision support systems emphasizes the need for resulting predictions to be both accurate and fair to all stakeholders. In this work we present a novel approach to increase a Neural Network model's fairness during training. We introduce a family of fairness enhancing regularization components that we use in conjunction with the traditional binary-cross-entropy based accuracy loss. These loss functions are based on Bias Parity Score (BPS), a score that helps quantify bias in the models with a single number. In the current work we investigate the behavior and effect of these regularization components on bias. We deploy them in the context of a recidivism prediction task as well as on a census-based adult income dataset. The results demonstrate that with a good choice of fairness loss function we can reduce the trained model's bias without deteriorating accuracy even in unbalanced dataset.