  As recent literature has demonstrated how classifiers often carry unintended
biases toward some subgroups, deploying machine learned models to users demands
careful consideration of the social consequences. How should we address this
problem in a real-world system? How should we balance core performance and
fairness metrics? In this paper, we introduce a MinDiff framework for
regularizing classifiers toward different fairness metrics and analyze a
technique with kernel-based statistical dependency tests. We run a thorough
study on an academic dataset to compare the Pareto frontier achieved by
different regularization approaches, and apply our kernel-based method to two
large-scale industrial systems demonstrating real-world improvements.
