arXiv cs.AI· Ji\v{r}\'i N\v{e}me\v{c}ek, Mark Kozdoba, Illia Kryvoviaz, Tom\'a\v{s} Pevn\'y, Jakub Mare\v{c}ek·· 10 天前AI 评分22
基于混合整数优化的交叉群体公平性
Intersectional Fairness via Mixed-Integer Optimization
AI 导读
研究提出一个统一框架，利用混合整数优化（MIO）训练交叉群体公平且内在可解释的分类器。作者证明了 MSD 与 SPSF 两种交叉公平性度量在检测最不公平子群体上的等价性，并实证表明该 MIO 算法在发现偏差方面表现更优。所训练的分类器可将交叉偏差限制在可接受阈值以下，面向受监管行业等场景。
正文 · 原文
Abstract:The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and transparent. While regulatory frameworks, including the EU's AI Act, mandate bias mitigation, they are deliberately vague about the definition of bias. In line with existing research, we argue that true fairness requires addressing bias at the intersections of protected groups. We propose a unified framework that leverages Mixed-Integer Optimization (MIO) to train intersectionally fair and intrinsically interpretable classifiers. We prove the equivalence of two measures of intersectional fairness (MSD and SPSF) in detecting the most unfair subgroup and empirically demonstrate that our MIO-based algorithm improves performance in finding bias. We train high-performing, interpretable classifiers that bound intersectional bias below an acceptable threshold, offering a robust solution for regulated industries and beyond.
| Comments: | 17 pages, 10 figures, 1 table |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC); Machine Learning (stat.ML) |
| Cite as: | arXiv:2601.19595 [cs.LG] |
| (or arXiv:2601.19595v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2601.19595 arXiv-issued DOI via DataCite |
|
| Journal reference: | NeurIPS 2026 |
Submission history
From: Jiří Němeček [view email]
[v1]
Tue, 27 Jan 2026 13:29:25 UTC (596 KB)
[v2]
Mon, 5 Oct 2026 21:58:17 UTC (607 KB)
来源：arXiv cs.AI · arxiv.org