Before revision:  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   .
After revision:  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 .