  Fairness-aware learning is increasingly important in data mining.
Discrimination prevention aims to prevent discrimination in the training data
before it is used to conduct predictive analysis. In this paper, we focus on
fair data generation that ensures the generated data is discrimination free.
Inspired by generative adversarial networks (GAN), we present fairness-aware
generative adversarial networks, called FairGAN, which are able to learn a
generator producing fair data and also preserving good data utility. Compared
with the naive fair data generation models, FairGAN further ensures the
classifiers which are trained on generated data can achieve fair classification
on real data. Experiments on a real dataset show the effectiveness of FairGAN.
