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.

使用生成对抗网络（GAN）生成合成数据进行数据增强已成为许多应用程序的流行方法，但这种技术具有的内在缺陷应引起从业人员的重视，例如 GAN 增强数据可能会强化和甚至放大训练数据中的偏差，这应该成为普通从业人员的警示。