Taskesen, B., Nguyen, V. A., Kuhn, D., & Blanchet, J. (2020). A Distributionally Robust Approach to Fair Classification. ArXiv. /abs/2007.09530

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Abstract

We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity. This model is equivalent to a tractable convex optimization problem if a Wasserstein ball centered at the empirical distribution on the training data is used to model distributional uncertainty and if a new convex unfairness measure is used to incentivize equalized opportunities. We demonstrate that the resulting classifier improves fairness at a marginal loss of predictive accuracy on both synthetic and real datasets. We also derive linear programming-based confidence bounds on the level of unfairness of any pre-trained classifier by leveraging techniques from optimal uncertainty quantification over Wasserstein balls.

Authors
Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, Jose Blanchet
Publication date
2020/7/18
Journal
arXiv preprint arXiv:2007.09530