Background and activities
STS, machine learning, digital infrastructures, algorithmic governance, data-driven welfare state, datafication, technology and social power, ethnography.
I am part of the DICE research group. I am researching the practice of doing machine learning within the Norwegian public sector.
Platforms, big data, clouds and machine learning signal promises of a profound digital transformation, that will impact all aspects of the welfare state and the services it provides to citizens and society. I am following several machine learning projects in the publiv sector in attempt to obtain a better understanding of the supposed data-driven future the Norwegian welfare state is heading towards. Which values are programmed into the socio-technological assemblage of smart digital infrastructures? Moreover, are we soon governed by techno-rational cyborg-bureaucrats? Is the democratization of machine learning even possible? While industry insiders regard machine learning as a truly innovative way to personalize the welfare state and empower citizens, researchers stress the techno-rationality, surveillance and unpredictable power the automation of bureaucracy fosters. Through ethnographic encounters with the inner workings of machine learning production in the Norwegian public sector, I intend to open the data-driven black box.
Currently I am also working on a general mapping of the Norwegian public sector's work on AI/data science together with Difi and UiO.
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
Part of book/report
- (2019) The Quest for Workable Data - Building Machine Learning Algorithms from Public Sector Archives. The Democratization of Artificial Intelligence - Net Politics in the Era of Learning Algorithms.