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In this work, we propose {\it Fair-CDA}, a fine-grained data augmentationstrategy for imposing fairness constraints. We use a feature disentanglementmethod to extract the features highly related to the sensitive attributes. Thenwe show that group fairness can be achieved by regularizing the models ontransition paths of sensitive features between groups. By adjusting theperturbation strength in the direction of the paths, our proposed augmentationis controllable and auditable. To alleviate the accuracy degradation caused byfairness constraints, we further introduce a calibrated model to impute labelsfor the augmented data. Our proposed method does not assume any data generativemodel and ensures good generalization for both accuracy and fairness.Experimental results show that Fair-CDA consistently outperformsstate-of-the-art methods on widely-used benchmarks, e.g., Adult, CelebA andMovieLens. Especially, Fair-CDA obtains an 86.3\% relative improvement forfairness while maintaining the accuracy on the Adult dataset. Moreover, weevaluate Fair-CDA in an online recommendation system to demonstrate theeffectiveness of our method in terms of accuracy and fairness.

Fair-CDA: Continuous and Directional Augmentation for Group Fairness