FairGAN+: Achieving Fair Data Generation and Classification through Generative Adversarial Nets

Depeng Xu, Shuhan Yuan, Lu Zhang, Xintao Wu

Abstract: How to achieve fairness is important for next generation machine learning. Two tasks that are equally important in fair machine learning are how to obtain fair datasets and how to build fair classifiers. In this work, we propose a new generative adversarial network (GAN) model for fair machine learning, named FairGAN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> . FairGAN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> contains a generator to generate close-to-real samples, a classifier to predict class labels and three discriminators to assist adversarial learning. FairGAN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> simultaneously achieves fair data generation and classification by co-training the generative model and the classifier through joint adversarial games with the discriminators. Evaluations on real world data show the effectiveness of FairGAN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> on both fair data generation and fair classification.