Stable Diffusion Exposed: Gender Bias from Prompt to Image
Yankun Wu Yuta Nakashima Noa Garcia
Recent studies have highlighted biases in generative models, shedding light
on their predisposition towards gender-based stereotypes and imbalances. This
paper contributes to this growing body of research by introducing an evaluation
protocol designed to automatically analyze the impact of gender indicators on
Stable Diffusion images. Leveraging insights from prior work, we explore how
gender indicators not only affect gender presentation but also the
representation of objects and layouts within the generated images. Our findings
include the existence of differences in the depiction of objects, such as
instruments tailored for specific genders, and shifts in overall layouts. We
also reveal that neutral prompts tend to produce images more aligned with
masculine prompts than their feminine counterparts, providing valuable insights
into the nuanced gender biases inherent in Stable Diffusion.