Related papers: Fair Representations by Compression

Fair Representations by Compression

URL: http://arxiv.org/abs/2105.14044v1
Date: Fri, 28 May 2021 18:22:07 GMT
Title: Fair Representations by Compression
Authors: Xavier Gitiaux, Huzefa Rangwala
Abstract summary: We show that a parsimonious representation should filter out information related to sensitive attributes if they are provided directly to the decoder. Explicit control of the entropy of the representation bit stream allows the user to move smoothly and simultaneously along both rate-distortion and rate-fairness curves.
Score: 19.26754855778295
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Organizations that collect and sell data face increasing scrutiny for the discriminatory use of data. We propose a novel unsupervised approach to transform data into a compressed binary representation independent of sensitive attributes. We show that in an information bottleneck framework, a parsimonious representation should filter out information related to sensitive attributes if they are provided directly to the decoder. Empirical results show that the proposed method, \textbf{FBC}, achieves state-of-the-art accuracy-fairness trade-off. Explicit control of the entropy of the representation bit stream allows the user to move smoothly and simultaneously along both rate-distortion and rate-fairness curves. \end{abstract}

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