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English(EN) The Statistical Fairness-Accuracy Frontier

新研究量化预测模型中的公平性-准确性权衡

作者 PulseAugur 编辑部 · [1 个来源] ·

该论文介绍了公平性-准确性（FA）帕累托前沿的概念，该概念说明了当单个预测模型服务于多个不同群体时，公平性与准确性之间的权衡。研究人员 Annie Ulichney 和同事们专注于从有限数据中近似该前沿，为有限样本情况提供了估计量和界限。他们的工作量化了样本限制如何影响不同群体，并提出了最优样本分配策略来应对这些权衡。 AI

影响 通过量化公平性-准确性权衡，为开发更公平的AI系统提供了理论工具。

排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了对统计公平性-准确性权衡的研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新研究量化预测模型中的公平性-准确性权衡

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该集群包含一篇在arXiv上发表的学术论文，详细介绍了对统计公平性-准确性权衡的研究。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alireza Fallah, Michael I. Jordan, Annie Ulichney ·

    统计公平-准确性前沿

    arXiv:2508.17622v4 Announce Type: replace-cross Abstract: We study fairness-accuracy tradeoffs when a single predictive model must serve multiple demographic groups. A useful tool for understanding this tradeoff is the fairness-accuracy (FA) Pareto frontier, which characterizes t…

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