LLM在招聘评估中对印度求职者存在文化偏见，主要源于语言特征而非姓名。
Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Models
- 用英印两国求职者访谈数据对比，发现模型对印度文本评分更低。
- 印度文本得分低与句式复杂度和词汇多样性有关，非姓名差异导致。
- 提醒需关注语言与社会文化因素，避免算法招聘中的隐性偏见。
人工智能在招聘中的应用日益广泛，大型语言模型（LLMs）可能影响甚至决定招聘决策。然而，这引发了关于偏见、公平性和可信度的担忧，尤其是在跨文化背景下。尽管作用日益重要，但针对AI驱动招聘评估中文化差异的系统研究仍较少。本研究系统分析了LLMs在文化与身份维度上对求职面试的评价表现。基于两组面试转录文本——100份英国求职者与100份印度求职者——我们首先考察了跨文化差异对可雇佣性及相关特质评分的影响。结果显示，即使在匿名化处理后，印度求职者的文本仍获得显著更低的评分，且该差异与句式复杂度和词汇多样性等语言特征相关。随后，我们在印度数据集中通过替换姓名（性别、种姓、地区）进行受控实验，结果未显示统计显著效应，表明仅姓名本身在孤立情况下不影响模型评分。研究强调需同时考量语言与社会文化维度，并呼吁在AI辅助招聘中设计更具文化敏感性的评估机制与问责体系。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) is increasingly used in hiring, with large language models (LLMs) having the potential to influence or even make hiring decisions. However, this raises pressing concerns about bias, fairness, and trust, particularly across diverse cultural contexts. Despite their growing role, few studies have systematically examined the potential biases in AI-driven hiring evaluation across cultures. In this study, we conduct a systematic analysis of how LLMs assess job interviews across cultural and identity dimensions. Using two datasets of interview transcripts, 100 from UK and 100 from Indian job seekers, we first examine cross-cultural differences in LLM-generated scores for hirability and related traits. Indian transcripts receive consistently lower scores than UK transcripts, even when they were anonymized, with disparities linked to linguistic features such as sentence complexity and lexical diversity. We then perform controlled identity substitutions (varying names by gender, caste, and region) within the Indian dataset to test for name-based bias. These substitutions do not yield statistically significant effects, indicating that names alone, when isolated from other contextual signals, may not influence LLM evaluations. Our findings underscore the importance of evaluating both linguistic and social dimensions in LLM-driven evaluations and highlight the need for culturally sensitive design and accountability in AI-assisted hiring.
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