Examining Pathological Bias in a Generative Adversarial Network Discriminator: A Case Study on a StyleGAN3 Model

Abstract

Generative adversarial networks generate photorealistic faces that are often indistinguishable by humans from real faces. We find that the discriminator in the pre-trained StyleGAN3 model, a popular GAN network, systematically stratifies scores by both image- and face-level qualities and that this disproportionately affects images across gender, race, and other categories. We examine the discriminator's bias for color and luminance across axes perceived race and gender; we then examine axes common in research on stereotyping in social psychology.