评估大语言模型在职位-简历匹配中的偏见：性别、种族与教育背景
Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education
AI总结：
本研究评估大语言模型在职位-简历匹配中的公平性，发现近期模型虽减少性别和种族偏见，但教育背景的隐性偏见仍显著，需持续评估和开发缓解策略。
摘要：
大语言模型（LLMs）通过将职位描述与候选人的简历进行匹配，有望实现招聘自动化，简化招聘流程并降低运营成本。然而，这些模型固有的偏见可能导致不公平的招聘做法，加剧社会偏见并削弱工作场所的多样性。本研究考察了在英语语境和美国背景下，大语言模型在职位-简历匹配任务中的性能与公平性。它评估了性别、种族和教育背景等因素如何影响模型决策，为人力资源应用中大语言模型的公平性和可靠性提供了关键见解。我们的研究结果表明，尽管近期模型已减少与显性属性（如性别和种族）相关的偏见，但关于教育背景的隐性偏见仍然显著。这些结果强调了持续评估和开发先进偏见缓解策略的必要性，以确保在行业环境中使用大语言模型时实现公平的招聘实践。
英文摘要：
Large Language Models (LLMs) offer the potential to automate hiring by matching job descriptions with candidate resumes, streamlining recruitment processes, and reducing operational costs. However, biases inherent in these models may lead to unfair hiring practices, reinforcing societal prejudices and undermining workplace diversity. This study examines the performance and fairness of LLMs in job-resume matching tasks within the English language and U.S. context. It evaluates how factors such as gender, race, and educational background influence model decisions, providing critical insights into the fairness and reliability of LLMs in HR applications. Our findings indicate that while recent models have reduced biases related to explicit attributes like gender and race, implicit biases concerning educational background remain significant. These results highlight the need for ongoing evaluation and the development of advanced bias mitigation strategies to ensure equitable hiring practices when using LLMs in industry settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
