  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.
