dorsal/arxiv
View SchemaFair Regression under Demographic Parity: A Unified Framework
| Authors | Yongzhen Feng, Weiwei Wang, Raymond K. W. Wong, Xianyang Zhang |
|---|---|
| Categories | |
| ArXiv ID | 2601.10623vv1 |
| URL | https://arxiv.org/abs/2601.10623 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
We propose a unified framework for fair regression tasks formulated as risk minimization problems subject to a demographic parity constraint. Unlike many existing approaches that are limited to specific loss functions or rely on challenging non-convex optimization, our framework is applicable to a broad spectrum of regression tasks. Examples include linear regression with squared loss, binary classification with cross-entropy loss, quantile regression with pinball loss, and robust regression with Huber loss. We derive a novel characterization of the fair risk minimizer, which yields a computationally efficient estimation procedure for general loss functions. Theoretically, we establish the asymptotic consistency of the proposed estimator and derive its convergence rates under mild assumptions. We illustrate the method's versatility through detailed discussions of several common loss functions. Numerical results demonstrate that our approach effectively minimizes risk while satisfying fairness constraints across various regression settings.
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"date_created": "2026-02-17T05:53:26.455000Z",
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"abstract": "We propose a unified framework for fair regression tasks formulated as risk minimization problems subject to a demographic parity constraint. Unlike many existing approaches that are limited to specific loss functions or rely on challenging non-convex optimization, our framework is applicable to a broad spectrum of regression tasks. Examples include linear regression with squared loss, binary classification with cross-entropy loss, quantile regression with pinball loss, and robust regression with Huber loss. We derive a novel characterization of the fair risk minimizer, which yields a computationally efficient estimation procedure for general loss functions. Theoretically, we establish the asymptotic consistency of the proposed estimator and derive its convergence rates under mild assumptions. We illustrate the method\u0027s versatility through detailed discussions of several common loss functions. Numerical results demonstrate that our approach effectively minimizes risk while satisfying fairness constraints across various regression settings.",
"arxiv_id": "2601.10623",
"authors": [
"Yongzhen Feng",
"Weiwei Wang",
"Raymond K. W. Wong",
"Xianyang Zhang"
],
"categories": [
"stat.ME",
"stat.ML"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Fair Regression under Demographic Parity: A Unified Framework",
"url": "https://arxiv.org/abs/2601.10623",
"version": "v1"
},
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"source": {
"execution_id": "3b4e5a69-ac0a-4eed-9e74-0fe38aa5f824",
"id": "arXiv Dataset",
"type": "Model",
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