Robust Fairness under Covariate Shift
This addresses fairness issues for protected groups in machine learning systems when data distributions shift, representing an incremental improvement over existing methods that assume identical distributions.
The paper tackles the problem of ensuring fairness in classification algorithms under covariate shift, where training and testing data distributions differ, by proposing a robust predictor that meets target fairness requirements while matching source data properties. The approach is demonstrated to provide benefits on benchmark prediction tasks, though no concrete numbers are provided.
Making predictions that are fair with regard to protected group membership (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. We propose an approach that obtains the predictor that is robust to the worst-case in terms of target performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks.