Utilising Synthetic Data from LLM for Gender Bias Detection and Mitigation in Recruitment Systems

In the current landscape, diversity and inclusion are highly emphasised, this research proposed a methodology to identify and mitigate gender bias in AI recruitment systems.The methodology included identifying biases based on the U.S. 80% Rule, generating synthetic data.The synthetic data was validated for its quality with 3 metrics.By leveraging GPT, this research aimed to create high-quality, diverse synthetic data to retrain AI systems.The ultimate goal of this research is to go beyond the currently proposed gender bias mitigation methodology and explore various bias issues, proposing innovative solutions to address them.Through this approach, the aim is to develop more comprehensive and contextually appropriate strategies for mitigating different types of biases that arise in AI recruitment systems.