Published in Ethnographic Praxis in Industry Conference Proceedings. Wiley journal, 2023
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 posses the potential to stimulate innovative thinking.
Recommended citation: Koed Madsen, Munk & Søltoft, (2023). Friction by machine: How to Slow Down Reasoning with Computational Methods. Ethnographic Praxis in Industry Conference Proceedings. Wiley journal (vol. 2023. no 1)
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The project focuses on stimulating SMEs data-imagination and associated competencies.The project will create knowledge about how small and medium-sized enterprises can understand, utilize and learn from their data
We live in a Digital Anthropocene era in which notions such as intelligence, emotions, and agency—historically considered traits that distinguish humans from other animals—are increasingly traced and understood through algorithmic logics and big digital datasets.
The project develops new anthropological analysis tools that combine AI with ethnographic methods and create new knowledge about the demand for cultural products.
The most common application of machine learning in small and medium-sized enterprises (SMEs) is the automation of routine tasks based on quantitative data, an area where Danish SMEs excel as European leaders. Concurrently, these enterprises are producing and archiving increasingly large volumes of unconventional, qualitative material related to activities that are neither routine nor quantifiable. It can be difficult for companies to envision how working with this type of data can be profitable. The project involves trying to expanding the companies’ notions of what data is and how it can be processed. Through various case studies, the project demonstrates how companies can broaden their data imagination by engaging in experiments with data and machine learning.
The most common application of machine learning in small and medium-sized enterprises (SMEs) is the automation of routine tasks based on quantitative data, an area where Danish SMEs excel as European leaders. Concurrently, these enterprises are producing and archiving increasingly large volumes of unconventional, qualitative material related to activities that are neither routine nor quantifiable. It can be difficult for companies to envision how working with this type of data can be profitable. The project involves trying to expanding the companies’ notions of what data is and how it can be processed. Through various case studies, the project demonstrates how companies can broaden their data imagination by engaging in experiments with data and machine learning.
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