FairRR: Pre-Processing for Group Fairness through Randomized Response

• URL: http://arxiv.org/abs/2403.07780v1
• Date: Tue, 12 Mar 2024 16:08:47 GMT
• Title: FairRR: Pre-Processing for Group Fairness through Randomized Response
• Authors: Xianli Zeng, Joshua Ward, Guang Cheng
• Abstract summary: We show that measures of group fairness can be directly controlled for with optimal model utility. We propose a pre-processing algorithm called FairRR that yields excellent downstream model utility and fairness.
• Score: 11.748613469340071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing usage of machine learning models in consequential decision-making processes has spurred research into the fairness of these systems. While significant work has been done to study group fairness in the in-processing and post-processing setting, there has been little that theoretically connects these results to the pre-processing domain. This paper proposes that achieving group fairness in downstream models can be formulated as finding the optimal design matrix in which to modify a response variable in a Randomized Response framework. We show that measures of group fairness can be directly controlled for with optimal model utility, proposing a pre-processing algorithm called FairRR that yields excellent downstream model utility and fairness.

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