Algorithmic decision-making in public e-services and its influence on citizens' legitimacy perceptions

Bruder, Jonas ; Helmig, Bernd ; Sievert, Martin

Weitere URL:	https://www.irspm.org/conferences/conference-2024
Dokumenttyp:	Präsentation auf Konferenz
Erscheinungsjahr:	2024
Veranstaltungstitel:	IRSPM Conference 2024
Veranstaltungsort:	Tampere, Finland
Veranstaltungsdatum:	16.-18.04.2024
Verwandte URLs:	https://www.irspm.org/conferences/confer... https://irspm.org/conference-2024/confer...
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Betriebswirtschaftslehre > ABWL, Public & Non Profit Management (Helmig 2008-)
Fachgebiet:	350 Öffentliche Verwaltung
Freie Schlagwörter (Englisch):	algorithmic decision-making , public e-services , legitimacy , e-government , citizen-state interactions
Abstract:	The emerging application of algorithmic decision-making systems in public administration bears a wide array of challenges regarding fairness, opaqueness, and societal values. Especially the reduction or erasure of human decision-making in public e-services raises important questions, as citizens are used to being served by human public officials by default. Current research on AI and algorithmic decision-making shows that the use of algorithms also affects citizens’ perceptions of legitimacy. We applied a multilevel online discrete-choice experiment to test if algorithmic decision-making influences citizens' preferences for public e-services of different quality and their respective legitimacy perceptions of the responsible public organization. The results from a sample of 1214 German citizens indicate that, contrary to our expectations, there are no systematic differences in perceptions and preferences between citizens’ who were served by an algorithm to those who were granted a decision by a human public official. The results have implications for research on algorithmic decision-making in the public sector, public e-services, and legitimacy-as-perception. They demand further investigation into when and how citizens care and worry about the application of algorithmic decision systems in the public sector.