Conditional Learning of Fair Representations

Zhao, H., Coston, A., Adel, T. and Gordon, G. (2019) Conditional Learning of Fair Representations. In: ICLR 2020 Eighth International Conference on Learning Representations, Virtual Conference, Formerly Addis Ababa, Ethiopia, 26-30 April 2020,

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Abstract

We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. We show how these two components contribute to ensuring accuracy parity and equalized false-positive and false-negative rates across groups without impacting demographic parity. Furthermore, we also demonstrate both in theory and on two real-world experiments that the proposed algorithm leads to a better utility-fairness trade-off on balanced datasets compared with existing algorithms on learning fair representations for classification.

Item Type:Conference Proceedings
Additional Information:HZ and GG would like to acknowledge support from the DARPA XAI project, contract #FA87501720152 and an Nvidia GPU grant. HZ would also like to thank Richard Zemel, Toniann Pitassi, David Madras and Elliot Creager for helpful discussions during HZ’s visit to the Vector Institute.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Hesham, Dr Tameem Adel
Authors: Zhao, H., Coston, A., Adel, T., and Gordon, G.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2020 The Authors
Publisher Policy:Reproduced with the permission of the publisher
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