Friction by Machine

How to Slow Down Reasoning with Computational Methods

Datos Bibliográficos

ID5948401
AutoresAnders Koed Madsen (0000-0003-2842-4908, Institute of Technology of Cambodia), Anders Kristian Munk (0000-0002-5542-3065, Institute of Technology of Cambodia), Johan Irving Søltoft (0000-0002-3076-0575, Institute of Technology of Cambodia)
Año2023
Volumen2023
Número1
Páginas65-88
Fecha de publicación2023-11-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEthnographic Praxis in Industry Conference Proceedings (CONFERENCE)
Identificadores de la revistaISSN: 1559-890X • E-ISSN: 1559-8918
EditorialWiley (PUBLISHER • GB)
DOI10.1111/epic.12153
OpenAlexW4406323162
IdiomaEN
Citas recibidas10
Referencias citadas19

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

Cognitive science · Computer Science · Modeling, Simulation, and Optimization · Multi-Agent Systems and Negotiation · Psychology · Robot Manipulation and Learning · Artificial Intelligence