Fairness GAN
This addresses fairness issues in multimedia datasets for decision-making applications, though it appears incremental as it builds on existing GAN methods.
The paper tackles the problem of generating fair datasets with respect to protected attributes in allocative decision making, introducing the Fairness GAN, which improves demographic parity and equality of opportunity while producing plausible images across multiple datasets.
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.