  We present a systematic approach for achieving fairness in a binary
classification setting. While we focus on two well-known quantitative
definitions of fairness, our approach encompasses many other previously studied
definitions as special cases. The key idea is to reduce fair classification to
a sequence of cost-sensitive classification problems, whose solutions yield a
randomized classifier with the lowest (empirical) error subject to the desired
constraints. We introduce two reductions that work for any representation of
the cost-sensitive classifier and compare favorably to prior baselines on a
variety of data sets, while overcoming several of their disadvantages.
