English(EN) Practical Estimation of the Bayes Optimal Fairness-Accuracy Tradeoff with Soft Labels

新方法估计人工智能模型的最优公平-准确性权衡

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了一种新方法，用于估计满足公平性约束的分类模型的理论上可达到的最佳错误率。该方法在最近的 arXiv 论文中有所详述，侧重于准确性和公平性之间的固有权衡，尤其是在涉及不同人口群体的情况下。该方法仅使用软标签（正类别的概率）和群体信息，无需输入特征或辅助训练，并提供了对最优公平-准确性权衡曲线的稳健估计。 AI

影响通过量化公平-准确性权衡的极限，为开发更公平的人工智能分类系统提供了理论框架。

排序理由在 arXiv 上发表的学术论文，详细介绍了估计分类模型中公平-准确性权衡的新方法。 [lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Mohit Sharma, Okan Ko\c{c}, Amit Deshpande, Takashi Ishida, Gang Niu, Rajiv Ratn Shah, Masashi Sugiyama · 2026-11-05 05:00

具有软标签的贝叶斯最优公平-准确性权衡的实际估计

arXiv:2611.06027v1 Announce Type: cross Abstract: There is a fundamental limit to the best prediction error of any model for a given data distribution. In classification, the Bayes error quantifies this limit and serves as both a benchmark for trained models and a criterion to de…

本文如何被排名

报道来源 [1]

具有软标签的贝叶斯最优公平-准确性权衡的实际估计

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