The race to develop image generation models is intensifying, with a rapid increase in the number of text-to-image models available. This is coupled with growing public awareness of these technologies. Though other generative AI models--notably, large language models--have received recent critical attention for the social and other non-technical issues they raise, there has been relatively little comparable examination of image generation models. This paper reports on a novel, comprehensive categorization of the social issues associated with image generation models. At the intersection of machine learning and the social sciences, we report the results of a survey of the literature, identifying seven issue clusters arising from image generation models: data issues, intellectual property, bias, privacy, and the impacts on the informational, cultural, and natural environments. We situate these social issues in the model life cycle, to aid in considering where potential issues arise, and mitigation may be needed. We then compare these issue clusters with what has been reported for large language models. Ultimately, we argue that the risks posed by image generation models are comparable in severity to the risks posed by large language models, and that the social impact of image generation models must be urgently considered.

翻译：图像生成模型的开发竞赛日益激烈，文本到图像模型的数量迅速增加，公众对这些技术的认知也在不断提升。尽管其他生成式AI模型（尤其是大型语言模型）因其引发的社会及其他非技术问题而受到近期批评性关注，但对图像生成模型的相关审视却相对不足。本文报告了对图像生成模型相关社会问题的一种新颖且全面的分类。在机器学习与社会科学的交叉点上，我们通过文献调研结果，识别出图像生成模型引发的七个问题集群：数据问题、知识产权、偏见、隐私，以及信息、文化和自然环境的影响。我们将这些社会问题置于模型生命周期中，以助于思考潜在问题可能出现的位置及需要缓解的环节。随后，我们将这些问题集群与大型语言模型已报告的问题进行比较。最终，我们论证图像生成模型带来的风险严重程度可与大型语言模型相媲美，亟需紧急考虑其社会影响。