  Machine learning models that convert user-written text descriptions into
images are now widely available online and used by millions of users to
generate millions of images a day. We investigate the potential for these
models to amplify dangerous and complex stereotypes. We find a broad range of
ordinary prompts produce stereotypes, including prompts simply mentioning
traits, descriptors, occupations, or objects. For example, we find cases of
prompting for basic traits or social roles resulting in images reinforcing
whiteness as ideal, prompting for occupations resulting in amplification of
racial and gender disparities, and prompting for objects resulting in
reification of American norms. Stereotypes are present regardless of whether
prompts explicitly mention identity and demographic language or avoid such
language. Moreover, stereotypes persist despite mitigation strategies; neither
user attempts to counter stereotypes by requesting images with specific
counter-stereotypes nor institutional attempts to add system ``guardrails''
have prevented the perpetuation of stereotypes. Our analysis justifies concerns
regarding the impacts of today's models, presenting striking exemplars, and
connecting these findings with deep insights into harms drawn from social
scientific and humanist disciplines. This work contributes to the effort to
shed light on the uniquely complex biases in language-vision models and
demonstrates the ways that the mass deployment of text-to-image generation
models results in mass dissemination of stereotypes and resulting harms.
