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Paper short abstract

This paper proposes mechanology and transduction as methods for studying how ML operates, rather than only what it does. Drawing on ethnography in an AI lab, it shows how computational processes enact regimes of knowledge, calling for new methodological engagements with algorithmic opacity.

Paper long abstract

This paper reflects on methodological impasses encountered during ethnographic research in an AI laboratory, and engaging with ‘technology’ more broadly. Conventional approaches that privilege either engineers’ interpretations of their systems, or the social effects of algorithmic outputs, often leave the technical operations opaque to anthropological inquiry. We argue that these limits call for methods that engage not only with what machines do, or what practitioners say they do, but with how they operate, reorganising modes of perception, action, and knowledge.

Drawing on debates starting from the philosophy of Simondon, this paper develops two methodological orientations: mechanology (Rieder, 2020) and transduction (Mackenzie, 2002; Helmreich, 2007). First, we propose a mechanological reading of encounters with ML systems, as a way of translating machinic operations into anthropological problems, tracing how computational processes crystallise regimes of generativity and sociality. Second, we outline what a transductive anthropology might look like: one concerned with how signals traverse technical media and, in doing so, do not merely pass through, but transform the very terms they relate, reshaping concepts and practices by constantly redrawing the limits of thinking and being, in ways our interlocutors often refused to acknowledge.

These approaches foreground algorithmic techniques as epistemic actors whose modes of functioning demand new forms of methodological engagement. Rather than treating opacity as a barrier to critique, this paper argues for studying algorithmic operations as sites where epistemological assumptions and social imaginaries are materially enacted, opening pathways for critique that do not rely on rendering these systems fully visible.