Abstract
Achieving fairness in learning models is currently
an imperative task in machine learning. Meanwhile, recent research showed that fairness should
be studied from the causal perspective, and proposed a number of fairness criteria based on Pearl’s
causal modeling framework. In this paper, we investigate the problem of building causal fairnessaware generative adversarial networks (CFGAN),
which can learn a close distribution from a given
dataset, while also ensuring various causal fairness
criteria based on a given causal graph. CFGAN
adopts two generators, whose structures are purposefully designed to reflect the structures of causal
graph and interventional graph. Therefore, the two
generators can respectively simulate the underlying
causal model that generates the real data, as well
as the causal model after the intervention. On the
other hand, two discriminators are used for producing a close-to-real distribution, as well as for
achieving various fairness criteria based on causal
quantities simulated by generators. Experiments on
a real-world dataset show that CFGAN can generate high quality fair data