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<p>The growing adoption of Artificial Intelligence (AI) in recruitment is often flaunted as a means to enhance efficiency and reduce human biases, despite lack of evidence. This study investigates whether AI, specifically ChatGPT, mitigates or perpetuates existing gender bias in hiring decisions We propose a dual-stage methodological framework combining natural language processing techniques with prompt engineering. We apply the framework to the analysis of a large-scale corpus of job descriptions across various industries and the assessment of how ChatGPT screens resume in response to these descriptions. Our findings indicate that the presence and type of gender bias in job descriptions is echoed in ChatGPT-fueled resume screening. More specifically, job postings in traditionally male-dominated fields emphasize leadership, technical expertise, and agentic traits, while those in female-dominated fields prioritize interpersonal skills and service orientation. ChatGPT exhibits bias in resume screening, favouring male-coded attributes, with candidate selection influenced by names and attributes in resumes perceived as gendered. These findings underscore the risk of AI reinforcing societal biases rather than eliminating them and suggest recommendations to mitigate them.</p>

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ChatGPT is a gender bias echo-chamber in HR recruitment: an NLP analysis and framework to uncover the language roots of bias

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月28日

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  • Siva Sankari Sivakaminathan,
  • Elena Musi

摘要

The growing adoption of Artificial Intelligence (AI) in recruitment is often flaunted as a means to enhance efficiency and reduce human biases, despite lack of evidence. This study investigates whether AI, specifically ChatGPT, mitigates or perpetuates existing gender bias in hiring decisions We propose a dual-stage methodological framework combining natural language processing techniques with prompt engineering. We apply the framework to the analysis of a large-scale corpus of job descriptions across various industries and the assessment of how ChatGPT screens resume in response to these descriptions. Our findings indicate that the presence and type of gender bias in job descriptions is echoed in ChatGPT-fueled resume screening. More specifically, job postings in traditionally male-dominated fields emphasize leadership, technical expertise, and agentic traits, while those in female-dominated fields prioritize interpersonal skills and service orientation. ChatGPT exhibits bias in resume screening, favouring male-coded attributes, with candidate selection influenced by names and attributes in resumes perceived as gendered. These findings underscore the risk of AI reinforcing societal biases rather than eliminating them and suggest recommendations to mitigate them.