Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying classification with respect to specific fairness metrics, modeled the corresponding fair classification problem as constrained optimization problems, and developed tailored algorithms to solve them. Despite this, there still remain important metrics for which there are no fair classifiers with theoretical guarantees; primarily because the resulting optimization problem is non-convex. The main contribution of this paper is a meta-algorithm for classification that can take as input a general class of fairness constraints with respect to multiple non disjoint and multi-valued sensitive attributes, and which comes with provable guarantees. In particular, our algorithm can handle non-convex "linear fractional" constraints (which includes fairness constraints such as predictive parity) for which no prior algorithm was known. Key to our results is an algorithm for a family of classification problems with convex constraints along with a reduction from classification problems with linear fractional constraints to this family. Empirically, we observe that our algorithm is fast, can achieve near-perfect fairness with respect to various fairness metrics, and the loss in accuracy due to the imposed fairness constraints is often small.


Published in:
Fat*'19: Proceedings Of The 2019 Conference On Fairness, Accountability, And Transparency, 319-328
Presented at:
ACM Conference on Fairness, Accountability, and Transparency (FAT), Atlanta, GA, Jan 29-31, 2019
Year:
Jan 01 2019
Publisher:
New York, ASSOC COMPUTING MACHINERY
ISBN:
978-1-4503-6125-5
Keywords:
Laboratories:




 Record created 2019-07-17, last modified 2019-08-30


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