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The race to develop image generation models is intensifying, with a rapidincrease in the number of text-to-image models available. This is coupled withgrowing public awareness of these technologies. Though other generative AImodels--notably, large language models--have received recent critical attentionfor the social and other non-technical issues they raise, there has beenrelatively little comparable examination of image generation models. This paperreports on a novel, comprehensive categorization of the social issuesassociated with image generation models. At the intersection of machinelearning and the social sciences, we report the results of a survey of theliterature, identifying seven issue clusters arising from image generationmodels: data issues, intellectual property, bias, privacy, and the impacts onthe informational, cultural, and natural environments. We situate these socialissues in the model life cycle, to aid in considering where potential issuesarise, and mitigation may be needed. We then compare these issue clusters withwhat has been reported for large language models. Ultimately, we argue that therisks posed by image generation models are comparable in severity to the risksposed by large language models, and that the social impact of image generationmodels must be urgently considered.

Situating the social issues of image generation models in the model life cycle: a sociotechnical approach