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A Reductions Approach to Fair Classification
Alekh Agarwal · Alina Beygelzimer · Miroslav Dudik · John Langford · Hanna Wallach

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #89

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.

Author Information

Alekh Agarwal (Microsoft Research)
Alina Beygelzimer (Yahoo Research)
Miro Dudik (Microsoft Research)
John Langford (MSR)
Hanna Wallach (Microsoft Research)

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