algorithmic fairness seeks to identify and correct sources of bias in machine
learning algorithms. Confoundingly, ensuring fairness often comes at the cost
of accuracy. We provide formal tools in this work for reconciling this
fundamental tension in algorithm fairness. Specifically, we