A Distributionally Robust Approach to Fair Classification

• URL: http://arxiv.org/abs/2007.09530v1
• Date: Sat, 18 Jul 2020 22:34:48 GMT
• Title: A Distributionally Robust Approach to Fair Classification
• Authors: Bahar Taskesen and Viet Anh Nguyen and Daniel Kuhn and Jose Blanchet
• Abstract summary: We propose a 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. We demonstrate that the resulting classifier improves fairness at a marginal loss of predictive accuracy on both synthetic and real datasets.
• Score: 17.759493152879013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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