  The use of synthetic data generated by Generative Adversarial Networks (GANs)
has become quite a popular method to do data augmentation for many
applications. While practitioners celebrate this as an economical way to get
more synthetic data that can be used to train downstream classifiers, it is not
clear that they recognize the inherent pitfalls of this technique. In this
paper, we aim to exhort practitioners against deriving any false sense of
security against data biases based on data augmentation. To drive this point
home, we show that starting with a dataset consisting of head-shots of
engineering researchers, GAN-based augmentation "imagines" synthetic engineers,
most of whom have masculine features and white skin color (inferred from a
human subject study conducted on Amazon Mechanical Turk). This demonstrates how
biases inherent in the training data are reinforced, and sometimes even
amplified, by GAN-based data augmentation; it should serve as a cautionary tale
for the lay practitioners.
