A Distributionally Robust Approach to Fair Classification
This work addresses fairness in classification for applications involving sensitive attributes like gender or ethnicity, presenting an incremental improvement with a tractable convex optimization approach.
The authors tackled the problem of unfair classification by proposing a distributionally robust logistic regression model with an unfairness penalty, which improves fairness with only a marginal loss in predictive accuracy on synthetic and real datasets.
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.