Fairness GAN: Generating datasets with fairness properties using a generative adversarial network

TLDR

The authors develop a Fairness GAN that generates datasets resembling a given multimedia set while improving fairness with respect to protected attributes. They design an auxiliary‑classifier GAN that can incorporate unlabeled data, applying it to CelebA, Quick, Draw!, and soccer‑player datasets to produce fair images. Experiments show the method enhances demographic parity and equality of opportunity while maintaining image plausibility.

Abstract

We introduce the Fairness GAN (generative adversarial network), an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in decision making. We propose a novel auxiliary classifier GAN that strives for demographic parity or equality of opportunity and show empirical results on several datasets, including the CelebFaces Attributes (CelebA) dataset, the Quick, Draw! dataset, and a dataset of soccer player images and the offenses for which they were called. The proposed formulation is well suited to absorbing unlabeled data; we leverage this to augment the soccer dataset with the much larger CelebA dataset. The methodology tends to improve demographic parity and equality of opportunity while generating plausible images.