  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.
