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Algorithmic Decision Making in Public Services: A CSCW-Perspective

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Association for Computing Machinery


Each day the public administration makes thousands of decisions with consequences for the welfare of its citizens. An increasing number of such decisions are supported or made by algorithmic decision making (ADM) systems, yet there is a widespread concern that these algorithms create a 'black box' of embedded bias, lack of human discretion, transparency or trust. For example, ADM is currently tested in public administration in job placement for prediction of a citizen's risk of long-term unemployment. This research project focus on bringing about research on citizens' 'trust' and 'transparency' from a practice-oriented perspective when algorithms are increasingly introduced in public services such as job placement. We propose a study of citizen-government relations to begin to uncover how computational systems and semi-automated decisions affect the relationship between citizens and caseworker, as they work through the collaborative processes around casework. In this context, our question is: What are citizens and caseworkers' different concepts of trust and transparency? How are casework processes affected as we are beginning to see a closer integration between legal guidelines and computational systems in casework? These questions are of huge importance to get a better understanding of how algorithms are changing the ways society makes decisions in core areas of public services in order to inform the responsible design of technologies in areas such as job placement.


Flügge, Asbjörn William Ammitzböll; Hildebrandt, Thomas; Möller, Naja Holten (2020): Algorithmic Decision Making in Public Services: A CSCW-Perspective. Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work. DOI: 10.1145/3323994.3369886. Association for Computing Machinery. pp. 111–114. Sanibel Island, Florida, USA