Friction by Machine: How to Slow Down Reasoning with Computational Methods

Abstract

This paper provides a theoretical alternative to the prevailing perception of machine learning as synonymous with speed and efficiency. Inspired by ethnographic fieldwork and grounded in pragmatist philosophy, we introduce the concept of “data friction” as the situation when encounters between held beliefs and data patterns possess the potential to stimulate innovative thinking. Contrary to the conventional connotations of “speed” and “control,” we argue that computational methods can generate a productive dissonance, thereby fostering slower and more reflective practices within organizations. Drawing on a decade of experience in participatory data design and data sprints, we present a typology of data frictions and outline three ways in which algorithmic techniques within data science can be reimagined as “friction machines”. We illustrate these theoretical points through a dive into three case studies conducted with applied anthropologist in the movie industry, urban planning, and research.