Regression under demographic parity constraints via unlabeled post-processing

• URL: http://arxiv.org/abs/2407.15453v1
• Date: Mon, 22 Jul 2024 08:11:58 GMT
• Title: Regression under demographic parity constraints via unlabeled post-processing
• Authors: Evgenii Chzhen, Mohamed Hebiri, Gayane Taturyan,
• Abstract summary: We present a general-purpose post-processing algorithm that generates predictions that meet the demographic parity. Unlike prior methods, our approach is fully theory-driven. We require precise control over the gradient norm of the convex function. Our algorithm is backed by finite-sample analysis and post-processing bounds, with experimental results validating our theoretical findings.
• Score: 5.762345156477737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of performing regression while ensuring demographic parity, even without access to sensitive attributes during inference. We present a general-purpose post-processing algorithm that, using accurate estimates of the regression function and a sensitive attribute predictor, generates predictions that meet the demographic parity constraint. Our method involves discretization and stochastic minimization of a smooth convex function. It is suitable for online post-processing and multi-class classification tasks only involving unlabeled data for the post-processing. Unlike prior methods, our approach is fully theory-driven. We require precise control over the gradient norm of the convex function, and thus, we rely on more advanced techniques than standard stochastic gradient descent. Our algorithm is backed by finite-sample analysis and post-processing bounds, with experimental results validating our theoretical findings.

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