TY - GEN
T1 - Utilising Synthetic Data from LLM for Gender Bias Detection and Mitigation in Recruitment Systems
AU - Lee, Donghyeok
AU - Jaiswal, Rajesh R.
AU - Byrne, Adrian
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/12/2
Y1 - 2024/12/2
N2 - 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.
AB - 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.
KW - AI Recruitment Systems
KW - Bias Detection and Mitigation
KW - Human-Centred AI
KW - LLM
KW - Synthetic Data
UR - https://www.scopus.com/pages/publications/85216543246
U2 - 10.1145/3701268.3701285
DO - 10.1145/3701268.3701285
M3 - Conference contribution
AN - SCOPUS:85216543246
T3 - ACM International Conference Proceeding Series
SP - 57
BT - HCAI-ep 2024 - Proceedings of the 2024 Conference on Human Centered Artificial Intelligence - Education and Practice
PB - Association for Computing Machinery (ACM)
T2 - 2nd Conference on Human Centered Artificial Intelligence - Education and Practice, HCAI-ep 2024
Y2 - 1 December 2024 through 2 December 2024
ER -