Paper short abstract:

By observing AI training courses ethnographically, I aim to understand how students develop their mastery of the specific inscriptions (Latour 1985) of AI. I will argue that diagrams (Mackenzie 2017) are at the core of the pedagogical practices of these courses.

Paper long abstract:

In his recent book, Florian Jaton demonstrates that ethnographies of AI labs are needed to understand what is at stake in the "progressive constitution of algorithms" (Jaton 2020).

In my PhD research, I radicalise his invitation by observing AI training courses ethnographically to understand how students develop their mastery of the specific inscriptions (Latour 1985) of AI. I will argue that diagrams are not only central to the development of algorithms (Mackenzie 2017): they are also at the core of the pedagogical practices of these training courses. Teachers use diagrams to enable their students to develop a geometric and intuitive understanding of how AI works, which largely dominates their practical work. Students often seem to carry out programming operations for the sole purpose of reworking the shape of the diagrams they are constantly producing, until they come closer to an expected shape, taken as a sign of the algorithm's efficiency. While students are required to justify their operations in mathematical terms, they often only rigorously grasp the math behind their work after having retrospectively investigated their own « diagrammatic » operations. Students thus assimilate while writing their reports, habits of veridiction that mask the reality of algorithmic design.

I will draw on an ethnographic study of two AI master’s degree trainings courses of a major French University, that I conducted during a whole semester, from September 2021 to January 2022. In this context I accumulated nearly 100 hours of observation, following four classes, and attending several student work sessions.