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

• URL: http://arxiv.org/abs/2111.03638v1
• Date: Fri, 5 Nov 2021 17:42:33 GMT
• Title: Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization
• Authors: Bhanu Jain, Manfred Huber, Ramez Elmasri
• Abstract summary: We introduce a family of fairness enhancing regularization components that we use in conjunction with the traditional binary-cross-entropy based accuracy loss. We deploy them in the context of a recidivism prediction task as well as on a census-based adult income dataset.
• Score: 0.8594140167290099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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