Computer scientists are increasingly concerned about the many ways that machine learning can reproduce and reinforce forms of bias. When ML systems are incorporated into core social institutions, like healthcare, criminal justice and education, issues of bias and discrimination can be extremely serious. But what can be done about it? Part of the trouble with bias in machine learning in high-stakes decision making is that it can be the result of one or many factors: the training data, the model, the system goals, and whether the system works less well for some populations, among several others. Given the difficulty of understanding how a machine learning system produced a particular result, bias is often discovered after a system has been producing unfair results in the wild. But there is another problem as well: the definition of bias changes significantly depending on your discipline, and there are exciting approaches from other fields that have not yet been included by computer science. This talk will look at the recent literature on bias in machine learning, consider how we can incorporate approaches from the social sciences, and offer new strategies to address bias.