arXiv cs.CY· Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz, Sherri Rose·· 13 小时前AI 评分31
面向慢性肾病多群体的惩罚性公平回归方法
Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
AI 导读
研究者提出一种面向多群体的通用惩罚性公平回归框架，以真阳性率差异作为惩罚项，可归约为代价敏感分类问题高效实现，并引入新的评分函数自动选择惩罚权重。模拟实验中该方法取得优于现有对比方法的公平性-准确率前沿；在慢性肾病全国多站点基层医疗研究中，针对终末期肾病构建的公平分类器在多个种族和族裔群体上显著提升公平性，整体拟合无明显损失。
正文 · 原文
Abstract:Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes with true positive rate disparity penalties. It can be efficiently implemented through reduction to a cost-sensitive classification problem. We additionally introduce novel score functions for automatically selecting penalty weights. Our penalized fair regression methods are empirically studied in simulations, where they achieve a fairness-accuracy frontier beyond that of existing comparison methods. Finally, we apply these methods to a national multi-site primary care study of chronic kidney disease to develop a fair classifier for end-stage renal disease. There we find substantial improvements in fairness for multiple race and ethnicity groups who experience societal bias in the health care system without any appreciable loss in overall fit.
| Subjects: | Methodology (stat.ME); Computers and Society (cs.CY); Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML) |
| Cite as: | arXiv:2512.17340 [stat.ME] |
| (or arXiv:2512.17340v3 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2512.17340 arXiv-issued DOI via DataCite |
Submission history
From: Carter H Nakamoto [view email]
[v1]
Fri, 19 Dec 2025 08:33:48 UTC (629 KB)
[v2]
Tue, 13 Jan 2026 21:26:52 UTC (1,289 KB)
[v3]
Wed, 21 Oct 2026 20:17:11 UTC (29,989 KB)
来源：arXiv cs.CY · arxiv.org