A Distributionally Robust Approach to Fair Classification
arXiv:2007.09530
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.
References in corpus (15)
- Equality of Opportunity in Supervised Learning
- On Fairness and Calibration
- Robust Wasserstein Profile Inference and Applications to Machine Learning
- Distributionally Robust Logistic Regression
- Regularization via Mass Transportation
- Censoring Representations with an Adversary
- Does mitigating ML's impact disparity require treatment disparity?
- Penalizing Unfairness in Binary Classification
- Training individually fair ML models with Sensitive Subspace Robustness
- Fairness for Robust Log Loss Classification
- Robust Optimization for Fairness with Noisy Protected Groups
- Stable and Fair Classification
- Incorporating Unlabeled Data into Distributionally Robust Learning
- A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression
- On Linear Optimization over Wasserstein Balls
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- Fair Mixup: Fairness via Interpolation
- Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation
- Sinkhorn Distributionally Robust Optimization
- Wasserstein Robust Classification with Fairness Constraints
- Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms
- Robust Fairness-aware Learning Under Sample Selection Bias
- Frank-Wolfe Methods in Probability Space