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Title: Fair Representation Learning: An Alternative to Mutual Information
Learning fair representations is an essential task to reduce bias in data-oriented decision making. It protects minority subgroups by requiring the learned representations to be independent of sensitive attributes. To achieve independence, the vast majority of the existing work primarily relaxes it to the minimization of the mutual information between sensitive attributes and learned representations. However, direct computation of mutual information is computationally intractable, and various upper bounds currently used either are still intractable or contradict the utility of the learned representations. In this paper, we introduce distance covariance as a new dependence measure into fair representation learning. By observing that sensitive attributes (e.g., gender, race, and age group) are typically categorical, the distance covariance can be converted to a tractable penalty term without contradicting the utility desideratum. Based on the tractable penalty, we propose FairDisCo, a variational method to learn fair representations. Experiments demonstrate that FairDisCo outperforms existing competitors for fair representation learning.  more » « less
Award ID(s):
2134079 1939725 1947135
NSF-PAR ID:
10380838
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
KDD
Page Range / eLocation ID:
1088 to 1097
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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