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

Bruder, Jonas ; Sievert, Martin ; Helmig, Bernd

Dokumenttyp:	Präsentation auf Konferenz
Erscheinungsjahr:	2024
Veranstaltungstitel:	The Annual Conference of the European Group for Public Administration 2024 (EGPA 2024)
Veranstaltungsort:	Athens, Greece
Veranstaltungsdatum:	03.-06.09.2024
Verwandte URLs:	https://iias-iisa.org/egpa-2024-conferen... https://www.conftool.org/egpa-conference... https://www.conftool.org/egpa-conference...
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 many 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 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 served by an algorithm and those granted a decision by a human bureaucrat. The results have implications for research on algorithmic decision-making in the public sector and public e-services. They demand further investigation into when and how citizens even care about the application of algorithmic decision systems in the public sector.