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Title: Achieving Causal Fairness through Generative Adversarial Networks

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 fairness-aware 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.
Authors:
; ; ; ;
Award ID(s):
1646654 1564250 1841119
Publication Date:
NSF-PAR ID:
10126320
Journal Name:
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Page Range or eLocation-ID:
1452 to 1458
Sponsoring Org:
National Science Foundation
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