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.

翻译：如今，将用户书面文本描述转化为图像的机器学习模型已广泛在线可用，并被数百万用户每天用于生成数百万张图像。我们研究了这些模型放大危险且复杂刻板印象的可能性。我们发现，大量普通提示会引发刻板印象，包括仅提及特征、特质、职业或物体的提示。例如，我们发现，针对基本特征或社会角色的提示会导致强化“白人至上”观念的图像；针对职业的提示会加剧种族和性别差异；针对物体的提示则会固化美国中心主义规范。无论提示是否明确提及身份和人口统计语言，刻板印象均存在。此外，尽管采取了缓解策略，刻板印象依然持续存在：无论是用户通过请求包含特定反刻板印象图像来试图对抗刻板印象，还是机构试图添加系统“防护栏”，都未能阻止刻板印象的延续。我们的分析证实了关于当前模型影响的担忧，提供了令人震惊的范例，并将这些发现与社会科学和人文学科中关于伤害的深刻见解联系起来。这项研究有助于揭示语言-视觉模型中独特的复杂偏见，并展示了文本到图像生成模型的大规模部署如何导致刻板印象的大规模传播及其带来的伤害。