  The fairness of machine learning-based decisions has become an increasingly
important focus in the design of supervised machine learning methods. Most
fairness approaches optimize a specified trade-off between performance
measure(s) (e.g., accuracy, log loss, or AUC) and fairness metric(s) (e.g.,
demographic parity, equalized odds). This begs the question: are the right
performance-fairness trade-offs being specified? We instead re-cast fair
machine learning as an imitation learning task by introducing superhuman
fairness, which seeks to simultaneously outperform human decisions on multiple
predictive performance and fairness measures. We demonstrate the benefits of
this approach given suboptimal decisions.
