Abstract

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 classificati to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subje 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, whil overcoming several of their disadvantages.