Post-processing fairness with minimal changes

• URL: http://arxiv.org/abs/2408.15096v2
• Date: Thu, 29 Aug 2024 15:59:13 GMT
• Title: Post-processing fairness with minimal changes
• Authors: Federico Di Gennaro, Thibault Laugel, Vincent Grari, Xavier Renard, Marcin Detyniecki,
• Abstract summary: We introduce a novel post-processing algorithm that is both model-agnostic and does not require the sensitive attribute at test time. Our algorithm is explicitly designed to enforce minimal changes between biased and debiased predictions.
• Score: 5.927938174149359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a novel post-processing algorithm that is both model-agnostic and does not require the sensitive attribute at test time. In addition, our algorithm is explicitly designed to enforce minimal changes between biased and debiased predictions; a property that, while highly desirable, is rarely prioritized as an explicit objective in fairness literature. Our approach leverages a multiplicative factor applied to the logit value of probability scores produced by a black-box classifier. We demonstrate the efficacy of our method through empirical evaluations, comparing its performance against other four debiasing algorithms on two widely used datasets in fairness research.

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