  In this paper, we introduce the Fairness GAN, 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 allocative 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 they were
called for. 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.
