测试GPT-5对意大利青年毕业生的招聘建议，发现模型对女性偏好情感类描述，存在隐性性别偏见。
Gender Bias in Generative AI-assisted Recruitment Processes
- 用24个均衡样本测试GPT-5的职业推荐倾向
- 女性被更多关联情感、共情词汇，男性则被关联战略、分析类词汇
- 揭示生成式AI在招聘中可能放大性别刻板印象，需加强透明与公平
近年来，生成式人工智能（GenAI）系统在人才选拔与候选人画像分析中扮演越来越重要的角色。然而，大型语言模型（LLMs）的使用可能再现甚至加剧劳动力市场中已有的性别刻板印象和偏见。本文旨在评估并测量这一现象，分析当前最先进的生成模型GPT-5如何根据性别和工作经验背景为35岁以下意大利毕业生推荐职业。研究对24个在性别、年龄、经验及专业领域上平衡的模拟候选人档案进行提示，结果显示，尽管职位名称和行业无显著差异，但女性候选人的描述中更频繁出现情感化、共情类形容词，而男性候选人则更多关联战略性和分析性词汇。该研究引发对生成式AI在敏感招聘场景中应用的伦理思考，强调未来数字劳动力市场中透明度与公平性的重要性。
原文摘要 · Abstract (English)
In recent years, generative artificial intelligence (GenAI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis of candidates' profiles. However, the employment of large language models (LLMs) risks reproducing, and in some cases amplifying, gender stereotypes and bias already present in the labour market. The objective of this paper is to evaluate and measure this phenomenon, analysing how a state-of-the-art generative model (GPT-5) suggests occupations based on gender and work experience background, focusing on under-35-year-old Italian graduates. The model has been prompted to suggest jobs to 24 simulated candidate profiles, which are balanced in terms of gender, age, experience and professional field. Although no significant differences emerged in job titles and industry, gendered linguistic patterns emerged in the adjectives attributed to female and male candidates, indicating a tendency of the model to associate women with emotional and empathetic traits, while men with strategic and analytical ones. The research raises an ethical question regarding the use of these models in sensitive processes, highlighting the need for transparency and fairness in future digital labour markets.
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