  Machine Learning models have been deployed across almost every aspect of
society, often in situations that affect the social welfare of many
individuals. Although these models offer streamlined solutions to large
problems, they may contain biases and treat groups or individuals unfairly. To
our knowledge, this review is one of the first to focus specifically on gender
bias in applications of machine learning. We first introduce several examples
of machine learning gender bias in practice. We then detail the most widely
used formalizations of fairness in order to address how to make machine
learning models fairer. Specifically, we discuss the most influential bias
mitigation algorithms as applied to domains in which models have a high
propensity for gender discrimination. We group these algorithms into two
overarching approaches -- removing bias from the data directly and removing
bias from the model through training -- and we present representative examples
of each. As society increasingly relies on artificial intelligence to help in
decision-making, addressing gender biases present in these 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.
