Gender stereotypes in artificial intelligence within the accounting profession using large language models

This study investigates how artificial intelligence (AI) perpetuates gender stereotypes in the accounting profession. Using large language models (LLMs), the study examines how these models assign gender labels to accounting job titles. Findings reveal differing tendencies among LLMs, with one favoring male labels, another female labels, and a third showing a balanced approach. Statistical analyses indicate significant disparities in labeling patterns, and job titles classified as male are associated with higher salary ranges, suggesting gender-related bias in economic outcomes. The study underscores the transfer of biases from the physical to the digital realm through LLMs and proposes raising public awareness to mitigate these biases.

Kelvin Leong, Anna Sung