  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.
