名字影响简历评价语言，导致AI招聘出现隐蔽偏见
Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring
- 通过合成简历测试不同姓名对评价语调的影响
- 事实内容稳定，但评价用词在极端分布中随姓名变化
- 开源模型更易产生这种隐蔽偏见，难被传统公平性审计发现
已有研究揭示了大模型在招聘和薪资推荐中的姓名偏见。本文关注大模型生成候选人摘要用于后续评估的场景。在一项大规模受控实验中，我们分析了4个模型在近百万份简历摘要上的表现，使用合成简历和真实职位信息，系统性地改变姓名的种族与性别特征。通过将摘要分解为事实内容与评价语调，发现事实部分基本稳定，而评价语言存在细微但显著的姓名相关差异，尤其集中在分布的极端处，且开源模型更为明显。招聘模拟显示，这种评价偏差会将方向性伤害转化为对称性不稳定，可能避开传统公平性审计，揭示出大模型间自动化偏见的新路径。
原文摘要 · Abstract (English)
Research has documented LLMs' name-based bias in hiring and salary recommendations. In this paper, we instead consider a setting where LLMs generate candidate summaries for downstream assessment. In a large-scale controlled study, we analyze nearly one million resume summaries produced by 4 models under systematic race-gender name perturbations, using synthetic resumes and real-world job postings. By decomposing each summary into resume-grounded factual content and evaluative framing, we find that factual content remains largely stable, while evaluative language exhibits subtle name-conditioned variation concentrated in the extremes of the distribution, especially in open-source models. Our hiring simulation demonstrates how evaluative summary transforms directional harm into symmetric instability that might evade conventional fairness audit, highlighting a potential pathway for LLM-to-LLM automation bias.
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