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Public sector chatbots: AI frictions and data infrastructures at the interface of the digital welfare state
Södertörn University, School of Culture and Education, Media and Communication Studies.ORCID iD: 0000-0002-5879-2130
Södertörn University, School of Culture and Education, Media and Communication Studies.ORCID iD: 0000-0002-9975-2354
2025 (English)In: New Media and Society, ISSN 1461-4448, E-ISSN 1461-7315, Vol. 27, no 4, p. 1962-1985Article in journal (Refereed) Published
Abstract [en]

Chatbots have become a mundane experience for Internet users. Public sector institutions have recently been introducing more advanced chatbots. In this article, we consider two cases of public sector chatbots, one in Estonia and one in Sweden, seeking to challenge the seemingly coherent understanding of artificial intelligence (AI) in the public sector. The aim is to both question the "thingness" of AI and show AI chatbots can be very different things. The material in this article is based on in-depth interviews and observations at public sector institutions that have relatively recently implemented chatbots. We employ the notion of AI frictions as a sensitizing concept to engage with the material and the diverging character of the public sector chatbots in the two countries. In the analysis, we identify AI frictions related to expectations of AI, organizational logics, as well as values connected with the digitalization of the public sector.

Place, publisher, year, edition, pages
Sage Publications, 2025. Vol. 27, no 4, p. 1962-1985
Keywords [en]
AI frictions, automated decision-making, data infrastructures, datafication, state-citizen relations, welfare
National Category
Media and Communication Studies
Identifiers
URN: urn:nbn:se:sh:diva-56958DOI: 10.1177/14614448251314394ISI: 001456738900027Scopus ID: 2-s2.0-105001643488OAI: oai:DiVA.org:sh-56958DiVA, id: diva2:1953048
Projects
AUTO-WELF
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare
Note

Project AUTO-WELF is supported by Austrian Science Fund: [I 6075-G], Austria; Independent Research Fund, Denmark; BMBF, Germany; National Science Centre, Poland (grant no.2021/03/Y/HS5/00263); FORTE, Sweden, under CHANSE ERA-NET Co-fund programme, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no 101004509.

Available from: 2025-04-17 Created: 2025-04-17 Last updated: 2025-10-07Bibliographically approved

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Kaun, AnneMänniste, Maris

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