  Training classifiers under fairness constraints such as group fairness,
regularizes the disparities of predictions between the groups. Nevertheless,
even though the constraints are satisfied during training, they might not
generalize at evaluation time. To improve the generalizability of fair
classifiers, we propose fair mixup, a new data augmentation strategy for
imposing the fairness constraint. In particular, we show that fairness can be
achieved by regularizing the models on paths of interpolated samples between
the groups. We use mixup, a powerful data augmentation strategy to generate
these interpolates. We analyze fair mixup and empirically show that it ensures
a better generalization for both accuracy and fairness measurement in tabular,
vision, and language benchmarks.
