The Epistemic and Performative Dynamics of Machine Learning Praxis

Abstract

This article is about machine learning as it relates to classic concerns in anthropology and the social sciences, regarding meaning, value, and culture, as well as agency, power and performativity. It focuses on the role of machine learning, with its peculiar manner of modeling phenomena, in mediating: (i) the sensibilities and assumptions agents have (qua interpretive grounds and algorithmic models) insofar as these mediate their actions, inferences, and affects; and (ii) the actions, inferences, and affects of agents (qua computational processes and interpretive practices) insofar as these drive their sensibilities and assumptions. More generally, it offers a model of the process of modeling per se, so far as this process unfolds in contexts of machine learning and beyond. In this respect, the metamodel offered is meant to capture some of the key dynamics of the tense and mutually transformative relations linking objects (of analysis), data (drawn from those objects), models (of such objects, as informed by such data), and actions (grounded in such models, and often transformative of such objects). It foregrounds the wily, epistemic, performative, and often violent dynamics of such processes when the objects being modeled are themselves agents capable of modeling.