@inproceedings{
wang2024new,
title={New Job, New Gender? Measuring the Social Bias in Image Generation Models},
author={Wenxuan Wang and Haonan Bai and Jen-tse Huang and Yuxuan WAN and Youliang Yuan and Haoyi Qiu and Nanyun Peng and Michael Lyu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=P66QvdCPjE}
}
Image generation models can generate or edit images from a given text. Recent advancements in image generation technology, exemplified by DALL-E and Midjourney, have been groundbreaking. These advanced models, despite their impressive capabilities, are often trained on massive Internet datasets, making them susceptible to generating content that perpetuates social stereotypes and biases, which can lead to severe consequences. Prior research on assessing bias within image generation models suffers from several shortcomings, including limited accuracy, reliance on extensive human labor, and lack of comprehensive analysis. In this paper, we propose BiasPainter, a novel evaluation framework that can accurately, automatically and comprehensively trigger social bias in image generation models. BiasPainter uses a diverse range of seed images of individuals and prompts the image generation models to edit these images using gender, race, and age-neutral queries. These queries span 62 professions, 39 activities, 57 types of objects, and 70 personality traits. The framework then compares the edited images to the original seed images, focusing on the significant changes related to gender, race, and age. BiasPainter adopts a key insight that these characteristics should not be modified when subjected to neutral prompts. Built upon this design, BiasPainter can trigger the social bias and evaluate the fairness of image generation models. 
We use BiasPainter to evaluate six widely-used image generation models, such as stable diffusion and Midjourney. Experimental results show that BiasPainter can successfully trigger social bias in image generation models. According to our human evaluation, BiasPainter can achieve 90.8\% accuracy on automatic bias detection, which is significantly higher than the results reported in previous work.  All the code, data, and experimental results will be released to facilitate future research.
New Job, New Gender? Measuring the Social Bias in Image Generation Models
Wenxuan Wang Haonan Bai Jen-tse Huang Yuxuan WAN Youliang Yuan Haoyi Qiu Nanyun Peng Michael Lyu