Fairness and Bias in Generative Models

Recent advances in generative adversarial networks have shown that it is possible to generate high-resolution and hyperrealistic images. However, the images produced by GANs are only as fair and representative as the datasets on which they are trained. We will develop approaches to investigate and debias GAN models.