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This article presents a generative artificial intelligence application, developed from a large language model specifically designed to identify and mitigate gender bias. As workplaces strive for greater inclusivity, addressing gender biases in professional settings has become a critical priority. This helps organizations proactively detect discriminatory language, ensuring compliance with ethical guidelines and gender policies. By leveraging advanced techniques such as multi-shot training, Chain-Of-Thought, and prompt engineering for effective operation. Research results demonstrate the system's high accuracy and robustness, ensuring optimal performance for users. By producing coherent, concise, and appropriate responses for analyses, this AI facilitates the tasks of professionals in human resources and regulatory compliance, streamlining their workflow and enhancing compliance efficiency.

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Use of Large Language Models to Detect Gender Bias in Workplace

  • 会议论文

  • 发表日期：

    2025年

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  • Benazir Abigail de la Rosa Munoz,
  • Natalia Usme,
  • Alexandra Gonzalez,
  • Ulises Valdez,
  • Sergio Ferro

摘要

This article presents a generative artificial intelligence application, developed from a large language model specifically designed to identify and mitigate gender bias. As workplaces strive for greater inclusivity, addressing gender biases in professional settings has become a critical priority. This helps organizations proactively detect discriminatory language, ensuring compliance with ethical guidelines and gender policies. By leveraging advanced techniques such as multi-shot training, Chain-Of-Thought, and prompt engineering for effective operation. Research results demonstrate the system's high accuracy and robustness, ensuring optimal performance for users. By producing coherent, concise, and appropriate responses for analyses, this AI facilitates the tasks of professionals in human resources and regulatory compliance, streamlining their workflow and enhancing compliance efficiency.