Before revision:  We first introduce several examples of machine learning gender bias in practice   .
After revision:  We first 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 .