Gender Biases in LLMs: Higher intelligence in LLM does not necessarily solve gender bias and stereotyping

Abstract

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.