We outline some common methodological issues in the field of critical AI studies, including a tendency to overestimate the explanatory power of individual samples (the benchmark casuistry), a dependency on theoretical frameworks derived from earlier conceptualizations of computation (the black box casuistry), and a preoccupation with a cause-and-effect model of algorithmic harm (the stack casuistry). In the face of these issues, we call for, and point towards, a future set of methodologies that might take into account existing strengths in the humanistic close analysis of cultural objects.
@article{arxiv.2411.18833,
title = {The Method of Critical AI Studies, A Propaedeutic},
author = {Fabian Offert and Ranjodh Singh Dhaliwal},
journal= {arXiv preprint arXiv:2411.18833},
year = {2025}
}