Algorithms in the public sector. Why context matters

Georg Wenzelburger, Pascal D. König, Julia Felfeli, Anja Achtziger

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Algorithms increasingly govern people's lives, including through rapidly spreading applications in the public sector. This paper sheds light on acceptance of algorithms used by the public sector emphasizing that algorithms, as parts of socio‐technical systems, are always embedded in a specific social context. We show that citizens' acceptance of an algorithm is strongly shaped by how they evaluate aspects of this context, namely the personal importance of the specific problems an algorithm is supposed to help address and their trust in the organizations deploying the algorithm. The objective performance of presented algorithms affects acceptance much less in comparison. These findings are based on an original dataset from a survey covering two real‐world applications, predictive policing and skin cancer prediction, with a sample of 2661 respondents from a representative German online panel. The results have important implications for the conditions under which citizens will accept algorithms in the public sector.
Original languageEnglish
Pages (from-to)40-60
JournalPublic Administration
Volume102
Issue number1
DOIs
Publication statusPublished - Mar 2024

Funding

We would like to thank the anonymous reviewers for their valuable comments and suggestions. The manuscript also owes to the feedback by Adam Harkens, Tobias Krafft, Johannes Schmees, Wolfang Schulz, Karen Yeung, and Katharina Zweig. The authors disclose receipt of the following financial support for the research, authorship, and/or publication of this article: This research has been conducted within the project “Deciding about, by, and together with algorithmic decision-making systems,” funded by the Volkswagen Foundation (Grant reference 19-0087). Open Access funding enabled and organized by Projekt DEAL. We would like to thank the anonymous reviewers for their valuable comments and suggestions. The manuscript also owes to the feedback by Adam Harkens, Tobias Krafft, Johannes Schmees, Wolfang Schulz, Karen Yeung, and Katharina Zweig. The authors disclose receipt of the following financial support for the research, authorship, and/or publication of this article: This research has been conducted within the project “Deciding about, by, and together with algorithmic decision‐making systems,” funded by the Volkswagen Foundation (Grant reference 19‐0087). Open Access funding enabled and organized by Projekt DEAL.

FundersFunder number
Adam Harkens
Tobias Krafft
Volkswagen Foundation19‐0087
Volkswagen Foundation

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