This paper proposes a novel interdisciplinary framework for the critical evaluation of text-to-image models, addressing the limitations of current technical metrics and bias studies. By integrating art historical analysis, artistic exploration, and critical prompt engineering, the framework offers a more nuanced understanding of these models' capabilities and societal implications. Art historical analysis provides a structured approach to examine visual and symbolic elements, revealing potential biases and misrepresentations. Artistic exploration, through creative experimentation, uncovers hidden potentials and limitations, prompting critical reflection on the algorithms' assumptions. Critical prompt engineering actively challenges the model's assumptions, exposing embedded biases. Case studies demonstrate the framework's practical application, showcasing how it can reveal biases related to gender, race, and cultural representation. This comprehensive approach not only enhances the evaluation of text-to-image models but also contributes to the development of more equitable, responsible, and culturally aware AI systems.

翻译：本文提出了一种新颖的跨学科框架，用于对文本到图像模型进行批判性评估，以应对当前技术指标和偏见研究的局限性。该框架通过整合艺术史分析、艺术探索和批判性提示工程，为理解这些模型的能力及其社会影响提供了更为细致的视角。艺术史分析提供了一种结构化方法来审视视觉与象征元素，揭示潜在的偏见与误表征。艺术探索则通过创造性实验，发掘模型的隐藏潜力与局限，促使人们对算法假设进行批判性反思。批判性提示工程则主动挑战模型的预设，暴露其内嵌的偏见。案例研究展示了该框架的实际应用，具体说明了其如何揭示与性别、种族及文化表征相关的偏见。这一综合性方法不仅提升了对文本到图像模型的评估水平，也有助于开发更加公平、负责任且具备文化意识的人工智能系统。