AI image models are rapidly evolving, disrupting aesthetic production in many industries. However, understanding of their underlying archives, their logic of image reproduction, and their persistent biases remains limited. What kind of methods and approaches could open up these black boxes? In this paper, we provide three methodological approaches for investigating AI image models and apply them to Stable Diffusion as a case study. Unmaking the ecosystem analyzes the values, structures, and incentives surrounding the model's production. Unmaking the data analyzes the images and text the model draws upon, with their attendant particularities and biases. Unmaking the output analyzes the model's generative results, revealing its logics through prompting, reflection, and iteration. Each mode of inquiry highlights particular ways in which the image model captures, "understands," and recreates the world. This accessible framework supports the work of critically investigating generative AI image models and paves the way for more socially and politically attuned analyses of their impacts in the world.

翻译：AI图像模型正在快速发展，颠覆了众多行业的审美生产活动。然而，人们对其底层档案库、图像再现逻辑以及持续存在的偏见仍然知之甚少。何种方法或路径能够打开这些黑箱？本文提出了三种研究AI图像模型的方法论路径，并以Stable Diffusion作为案例进行应用。拆解生态系统分析围绕模型生产所涉及的价值、结构与激励机制。拆解数据则分析模型所依据的图像与文本，及其所附带的特殊性与偏见。拆解输出分析模型的生成结果，通过提示、反思与迭代揭示其内在逻辑。每种探究方式都突显了图像模型捕捉、“理解”并再造世界的特定方式。这一易用的框架有助于开展对生成式AI图像模型的批判性研究，并为其在世界范围内更具社会与政治敏感性的影响分析铺平道路。