用艺术史+创作实验+批判性提示，全面评估文生图模型偏见
A Framework for Critical Evaluation of Text-to-Image Models: Integrating Art Historical Analysis, Artistic Exploration, and Critical Prompt Engineering
- 融合艺术史分析、创作实验与批判性提示工程
- 揭示性别、种族、文化代表中的隐性偏见
- 适合关注AI伦理与艺术生成的跨学科研究者
本文提出一种跨学科框架，用于批判性评估文生图模型，弥补现有技术指标和偏见研究的不足。通过艺术史分析，系统考察视觉与符号元素，揭示潜在偏见与误表；通过艺术创作实验，探索模型隐藏潜能与局限，促使对算法假设的反思；通过批判性提示工程，主动挑战模型假设，暴露深层偏见。案例研究展示了该框架在识别性别、种族及文化代表性偏见方面的应用价值。这一综合方法不仅提升模型评估深度，还推动更公平、负责任、具文化意识的AI系统发展。
原文摘要 · Abstract (English)
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.
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