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Ecosystemic AI: Local media systems and the challenge of artificial intelligence
Department of Media and Social Sciences, University of Stavanger, Norway .ORCID iD: 0000-0001-6737-8129
Department of Media and Communication, LM Munich, Germany.ORCID iD: 0000-0001-9055-4223
Responsible organisation
2026 (English)In: Digital Media Shadowing Democracy: Technology, Communication, and Power / [ed] A. Balčytienė, P. Bajomi-Lázár, & H. Sousa, Nordicom, University of Gothenburg , 2026, p. 219-238Chapter in book (Refereed)
Abstract [en]

In this chapter, we propose a theoretical perspective on the implications of artificial intelligence (AI) for local news media systems in Europe. As the technical protocols shaping web platformisation evolve into communication infrastructures in their own right, we ask: What dependencies does AI create for local news production? Drawing on previous literature, we contextualise AI adoption within local journalism as an ecosystem in which key civic and democratic infrastructures are subject to various forms of capture, specifically technological capture. Our primarily conceptual findings highlight the ecosystemic impact of AI on the political economy of local news structures in Europe. We conclude that the dominance of US-driven AI developments puts local European news media at risk of technological capture as they increasingly outsource production, management, dissemination, and audience relations to capitalise on AI, posing new challenges for regulators seeking to protect the autonomy of European news media.

Place, publisher, year, edition, pages
Nordicom, University of Gothenburg , 2026. p. 219-238
Keywords [en]
artificial intelligence, ecosystemic AI, journalism, local news media, technological capture
National Category
Media and Communication Studies
Research subject
Media
Identifiers
URN: urn:nbn:se:norden:org:diva-13759DOI: 10.48335/9789189864290-11OAI: oai:DiVA.org:norden-13759DiVA, id: diva2:2058066
Note

The authors thank the Volkswagen Foundation for their financial support regarding the research and authorship of this chapter (Grant 9B418). This research has also been supported by the Research Council of Norway (Grant 314257).

Available from: 2026-05-12 Created: 2026-05-06 Last updated: 2026-05-13

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