  As machine learning-enabled Text-to-Image (TTI) systems are becoming
increasingly prevalent and seeing growing adoption as commercial services,
characterizing the social biases they exhibit is a necessary first step to
lowering their risk of discriminatory outcomes. This evaluation, however, is
made more difficult by the synthetic nature of these systems' outputs: common
definitions of diversity are grounded in social categories of people living in
the world, whereas the artificial depictions of fictive humans created by these
systems have no inherent gender or ethnicity. To address this need, we propose
a new method for exploring the social biases in TTI systems. Our approach
relies on characterizing the variation in generated images triggered by
enumerating gender and ethnicity markers in the prompts, and comparing it to
the variation engendered by spanning different professions. This allows us to
(1) identify specific bias trends, (2) provide targeted scores to directly
compare models in terms of diversity and representation, and (3) jointly model
interdependent social variables to support a multidimensional analysis. We
leverage this method to analyze images generated by 3 popular TTI systems
(Dall-E 2, Stable Diffusion v 1.4 and 2) and find that while all of their
outputs show correlations with US labor demographics, they also consistently
under-represent marginalized identities to different extents. We also release
the datasets and low-code interactive bias exploration platforms developed for
this work, as well as the necessary tools to similarly evaluate additional TTI
systems.
