Fairness Violations and Mitigation under Covariate Shift
Fairness Violations and Mitigation under Covariate Shift
We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set. Stability against changes in data distribution is an important mandate for responsible deployment of models. The domain adaptation literature addresses this concern, albeit with the notion of stability limited to that …