Background and activities

Interests

STS, machine learning, digital infrastructures, algorithmic governance, data-driven welfare state, datafication, technology and social power, ethnography.

 

PhD project

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

Displaying a selection of activities. See all publications in the database

Journal publications

Part of book/report

  • Reutter, Lisa Marie; Spilker, Hendrik Storstein. (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.

Report/dissertation

Others