Perspectives from Practice: Algorithmic Decision-Making in Public Employment Services

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt


Algorithms are increasingly being implemented into core welfare areas as Public Employment Services. These data-driven technologies are implemented with the ambition to support caseworkers' decision-making capabilities, by profiling unemployed individual's risk of long-term unemployment. The research outlined in this paper investigates how we can study opaque technologies as algorithms from the perspective of the users (caseworkers) and those categorized (unemployed individuals) by these systems. This is done by combining established methods within Computer-Supported Cooperative Work, including ethnographic fieldwork and Participatory Design methods. I present preliminary results focused on caseworker's perception of the value of AI in job placement, and find documentation plays a central role in collaboration in casework. With this research, I am to contribute to a deeper understanding of how the organization of work is impacted by data-driven technologies like AI and explore ways to include the voice of unemployed individuals in the development of digital public services.

TitelCSCW 2021 - Conference Companion Publication of the 2021 Computer Supported Cooperative Work and Social Computing
ForlagAssociation for Computing Machinery
ISBN (Elektronisk)9781450384797
StatusUdgivet - 2021
Begivenhed24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2021 - Virtual, Online, USA
Varighed: 23 okt. 202127 okt. 2021


Konference24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2021
ByVirtual, Online

Bibliografisk note

Funding Information:
A special thanks to my supervisors Naja Holten Møller, Thomas Hildebrandt, and Henrik Palmer Olsen. I would also like to thank colleagues Irina Shklovski, Finn Kensing, Hanne Marie Motzfeldt, Trine Rask Nielsen, and Cathrine Seidelin for valuable feedback on my research. This work has been supported by the Innovation Fund Denmark (EcoKnow: award number 7050-00034A) and the Independent Research Fund Denmark (PACTA: award number 8091-00025b).

Publisher Copyright:
© 2021 ACM.

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