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

Generative adversarial networks (GANs) have seen widespread adoption in machine learning, especially in computer vision applications. These "generative" models are capable of producing artificial images in many instances indistinguishable from the real thing. The most common use of these networks is that of artificial face generation. These so-called "deepfakes" have been used in a number of research and commercial applications. With their proliferation, however, have come predictable problems of bias in their generation. All such models are trained on large datasets. Several pre-trained models for StyleGANs 2 and 3 are trained on the Flickr (FFHQ) dataset.