  We present a new approach for mitigating unfairness in learned classifiers.
In particular, we focus on binary classification tasks over individuals from
two populations, where, as our criterion for fairness, we wish to achieve
similar false positive rates in both populations, and similar false negative
rates in both populations. As a proof of concept, we implement our approach and
empirically evaluate its ability to achieve both fairness and accuracy, using
datasets from the fields of criminal risk assessment, credit, lending, and
college admissions.
