A Large Scale Analysis of Gender Biases in Text-to-Image Generative Models
Journal:
arXiv
Published Date:
Mar 30, 2025
Abstract
With the increasing use of image generation technology, understanding its
social biases, including gender bias, is essential. This paper presents the
first large-scale study on gender bias in text-to-image (T2I) models, focusing
on everyday situations. While previous research has examined biases in
occupations, we extend this analysis to gender associations in daily
activities, objects, and contexts. We create a dataset of 3,217 gender-neutral
prompts and generate 200 images per prompt from five leading T2I models. We
automatically detect the perceived gender of people in the generated images and
filter out images with no person or multiple people of different genders,
leaving 2,293,295 images. To enable a broad analysis of gender bias in T2I
models, we group prompts into semantically similar concepts and calculate the
proportion of male- and female-gendered images for each prompt. Our analysis
shows that T2I models reinforce traditional gender roles, reflect common gender
stereotypes in household roles, and underrepresent women in financial related
activities. Women are predominantly portrayed in care- and human-centered
scenarios, and men in technical or physical labor scenarios.
