Semester 2, academic year 2024/2025
By Dongdong Zhu
A major issue with generative AI is the presence of biases, which often carry a negative connotation, suggesting an unfair or unjustified preference for certain ideas or groups (May, 2021). Multiple studies have shown that females are underrepresented in various occupations in AI-generated outputs. However, existing research on gender bias in AI career representations often focuses on a single dimension, such as the proportion of females (Currie et al., 2024) or how their expressions and gestures are depicted (Sun et al., 2024). There is a lack of a comprehensive approach to measuring gender bias, as well as limited investigation into how generative AI compares to real-world cultural biases.
This study aims to explore gender bias in AI-generated image representations of politicians as a specific profession, and to compare these findings with the extensive body of research on gender bias in human-generated representations. It first examines the quantitative differences in the proportion of female politicians across AI-generated, human-generated, and real-world representations. Then, building on the Agency/Communion Model, it explores whether generative AI reproduces or alters human bias, specifically whether female politicians are more likely to be depicted in communal-oriented political domains, while male politicians are more likely to be represented in agentic-oriented domains using automated content analysis.