Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
This addresses the problem of discriminatory outcomes in commercial AI applications for users and developers, though it is incremental by extending bias analysis from generation to editing/classification.
The paper investigated social biases in downstream applications of text-to-image diffusion models, specifically image editing and classification, using synthetic images to probe Stable Diffusion. It uncovered significant intersectional biases, cautioning against uninformed adoption of these models for commercial tasks.
Text-to-image diffusion models have been adopted into key commercial workflows, such as art generation and image editing. Characterising the implicit social biases they exhibit, such as gender and racial stereotypes, is a necessary first step in avoiding discriminatory outcomes. While existing studies on social bias focus on image generation, the biases exhibited in alternate applications of diffusion-based foundation models remain under-explored. We propose methods that use synthetic images to probe two applications of diffusion models, image editing and classification, for social bias. Using our methodology, we uncover meaningful and significant inter-sectional social biases in \textit{Stable Diffusion}, a state-of-the-art open-source text-to-image model. Our findings caution against the uninformed adoption of text-to-image foundation models for downstream tasks and services.