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2025 (English)In: Artificial Intelligence, ISSN 0004-3702, E-ISSN 1872-7921, Vol. 339, article id 104244Article, review/survey (Refereed) Published
Abstract [en]
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.
Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Artificial intelligence, Complex systems, Computational social science, Human-AI coevolution
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-232122 (URN)10.1016/j.artint.2024.104244 (DOI)001359648100001 ()2-s2.0-85209118417 (Scopus ID)
Funder
European CommissionEU, Horizon 2020, 952026EU, Horizon 2020, 871042EU, European Research Council, ERC-2018-ADG 834756
2024-11-272024-11-272025-04-24Bibliographically approved