  Text-to-Image (T2I) generation is enabling new applications that support
creators, designers, and general end users of productivity software by
generating illustrative content with high photorealism starting from a given
descriptive text as a prompt. Such models are however trained on massive
amounts of web data, which surfaces the peril of potential harmful biases that
may leak in the generation process itself. In this paper, we take a
multi-dimensional approach to studying and quantifying common social biases as
reflected in the generated images, by focusing on how occupations, personality
traits, and everyday situations are depicted across representations of
(perceived) gender, age, race, and geographical location. Through an extensive
set of both automated and human evaluation experiments we present findings for
two popular T2I models: DALLE-v2 and Stable Diffusion. Our results reveal that
there exist severe occupational biases of neutral prompts majorly excluding
groups of people from results for both models. Such biases can get mitigated by
increasing the amount of specification in the prompt itself, although the
prompting mitigation will not address discrepancies in image quality or other
usages of the model or its representations in other scenarios. Further, we
observe personality traits being associated with only a limited set of people
at the intersection of race, gender, and age. Finally, an analysis of
geographical location representations on everyday situations (e.g., park, food,
weddings) shows that for most situations, images generated through default
location-neutral prompts are closer and more similar to images generated for
locations of United States and Germany.
