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

Authors

DOI:

https://doi.org/10.32473/flairs.36.133311

Keywords:

Fairness in ML, Bias Parity Score, Fairness Regularization, Deep Learning

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 datasets.

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Published

08-05-2023

How to Cite

Jain, B., Huber, M., & Elmasri, R. (2023). Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization. The International FLAIRS Conference Proceedings, 36(1). https://doi.org/10.32473/flairs.36.133311

Issue

Section

Special Track: Security, Privacy, Trust and Ethics in AI