End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning

TL;DR

This paper introduces an end-to-end bias mitigation framework for deep learning models that combines multiple bias correction techniques to improve fairness, demonstrated through experiments on neural networks.

Contribution

It proposes a novel integrated bias mitigation approach that leverages pre-, in-, and post-processing methods in deep learning models.

Findings

The framework outperforms individual techniques on fairness metrics.

Combines strengths of various bias mitigation methods.

Provides practical tools and open source packages for fairness assessment.

Abstract

Machine Learning models have been deployed across many different aspects of society, often in situations that affect social welfare. Although these models offer streamlined solutions to large problems, they may contain biases and treat groups or individuals unfairly based on protected attributes such as gender. In this paper, we introduce several examples of machine learning gender bias in practice followed by formalizations of fairness. We provide a survey of fairness research by detailing influential pre-processing, in-processing, and post-processing bias mitigation algorithms. We then propose an end-to-end bias mitigation framework, which employs a fusion of pre-, in-, and post-processing methods to leverage the strengths of each individual technique. We test this method, along with the standard techniques we review, on a deep neural network to analyze bias mitigation in a deep learning setting. We find that our end-to-end bias mitigation framework outperforms the baselines with respect to several fairness metrics, suggesting its promise as a method for improving fairness. As society increasingly relies on artificial intelligence to help in decision-making, addressing gender biases present in deep learning models is imperative. To provide readers with the tools to assess the fairness of machine learning models and mitigate the biases present in them, we discuss multiple open source packages for fairness in AI.