2024/09/30 by Rajesh Ranjan, Ranjan, Rajesh, Shailja Gupta +3 · 1 voice · 2 citations
Computer Science · Social Sciences · #Artificial Intelligence in Law #Computers and Society (cs.CY) #FOS: Computer and information sciences #cs.CY
paper · pdf · doi:10.48550/arxiv.2409.19959
openalex publication_date 2024/09/30 · arxiv published 2024/09/30 · openalex created_date 2024/10/28 · arxiv updated 2025/02/15 · openalex updated_date 2026/07/28
Large Language Models (LLMs) are finding applications in all aspects of life, but their susceptibility to biases, particularly gender stereotyping, raises ethical concerns. This study introduces a novel methodology, a persona-based framework, and a unisex name methodology to investigate whether higher-intelligence LLMs reduce such biases. We analyzed 1400 personas generated by two prominent LLMs, revealing that systematic biases persist even in LLMs with higher intelligence and reasoning capabilities. o1 rated males higher in competency (8.1) compared to females (7.9) and non-binary (7.80). The analysis reveals persistent stereotyping across fields like engineering, data, and technology, where the presence of males dominates. Conversely, fields like design, art, and marketing show a stronger presence of females, reinforcing societal notions that associate creativity and communication with females. This paper suggests future directions to mitigate such gender bias, reinforcing the need for further research to reduce biases and create equitable AI models.