Wasserstein Fair Classification
This addresses fairness in classification for domains with sensitive attributes, presenting an incremental improvement with theoretical robustness.
The paper tackles fair classification by enforcing independence between classifier outputs and sensitive information through Wasserstein-1 distance minimization, achieving robust performance across thresholds and showing empirical gains against fairness baselines on benchmark datasets.
We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approach has desirable theoretical properties and is robust to specific choices of the threshold used to obtain class predictions from model outputs. We introduce different methods that enable hiding sensitive information at test time or have a simple and fast implementation. We show empirical performance against different fairness baselines on several benchmark fairness datasets.