On GANs perpetuating biases for face verification

DeepLearningsystemsneedlargedatafortraining.Datasets for training face verification systems are difficult to obtain and prone to privacy issues. Synthetic data generated by generative models such as GANs can be a good alternative. However, we show that data generated from GANs are prone to bias and fairness issues. Specifically GANs trained on FFHQ dataset show bias towards generating white faces in the age group of 20-29. We also demonstrate that synthetic faces cause disparate impact, specifically for race attribute, when used for fine tuning face verification systems. This is measured using $DoB_{fv}$ metric, which is defined as standard deviation of GAR@FAR for face verification.