The Method of Critical AI Studies, A Propaedeutic

TL;DR

Some common methodological issues in the field of critical AI studies, including a tendency to overestimate the explanatory power of individual samples, are outlined, and a future set of methodologies that might take into account existing strengths in the humanistic close analysis of cultural objects are pointed towards.

Abstract

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.