Human-AI coevolution

Pedreschi, Dino; Pappalardo, Luca; Ferragina, Emanuele; Baeza Yates, Ricardo; Barabási, Albert-László; Dignum, Frank; Dignum, Virginia; Eliassi-Rad, Tina; Giannotti, Fosca; Kertész, János; Knott, Alistair; Ioannidis, Yannis; Lukowicz, Paul; Passarella, Andrea; Pentland, Alex; Shawe-Taylor, John; Vespignani, Alessandro

Human-AI coevolution

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dc.contributor.author Pedreschi, Dino
dc.contributor.author Pappalardo, Luca
dc.contributor.author Ferragina, Emanuele
dc.contributor.author Baeza Yates, Ricardo
dc.contributor.author Barabási, Albert-László
dc.contributor.author Dignum, Frank
dc.contributor.author Dignum, Virginia
dc.contributor.author Eliassi-Rad, Tina
dc.contributor.author Giannotti, Fosca
dc.contributor.author Kertész, János
dc.contributor.author Knott, Alistair
dc.contributor.author Ioannidis, Yannis
dc.contributor.author Lukowicz, Paul
dc.contributor.author Passarella, Andrea
dc.contributor.author Pentland, Alex
dc.contributor.author Shawe-Taylor, John
dc.contributor.author Vespignani, Alessandro
dc.date.accessioned 2025-10-07T06:10:12Z
dc.date.available 2025-10-07T06:10:12Z
dc.date.issued 2025
dc.description.abstract 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.en
dc.description.sponsorship This work has been partially supported by PNRR - M4C2 - Investimento 1.3, Partenariato Esteso (grant No. PE00000013) - “FAIR - Future Artificial Intelligence Research” - Spoke 1 “Human-centered AI”, funded by the European Commission under the Next Generation EU programme; EU H2020 projects HumaneAI-net G.A. 952026 and SoBigData++ G.A. 871042; European Research Council ERC-2018-ADG 834756 “XAI: Science and technology for the eXplanation of AI decision making”; CHIST-ERA grant CHIST-ERA-19-XAI-010, by MUR (grant No. not available), FWF (grant No. I 5205), EPSRC (grant No. EP/V055712/1), NCN (grant No. 2020/02/Y/ST6/00064), ETAg (grant No. SLTAT21096), BNSF (grant No. KΠ-06-AOO2/5); PNRR (Piano Nazionale di Ripresa e Resilienza) in the context of the research program 20224CZ5X4_PE6_PRIN 2022 “URBAI – Urban Artificial Intelligence” (CUP B53D23012770006), funded by European Union – Next Generation EU; Emanuele Ferragina has been partially supported by a public grant overseen by the French National Research Agency (ANR) as part of the ‘Investissements d'Avenir’ program LIEPP (ANR-11-LABX-0091, ANR-11-IDEX-0005-02) and the Université de Paris IdEx (ANR-18-IDEX-0001). We thank Vincenzo Vivarini for the inspiration about the importance of the feedback loop in complex social systems. There is always a human agent beyond any tactical system. We also thank Daniele Fadda for the support on making the figures. Finally, we thank all members of KDD-Lab for insightful discussions about human-AI coevolution.en
dc.format.mimetype application/pdf
dc.identifier.citation Pedreschi D, Pappalardo L, Ferragina E, Baeza-Yates R, Barabási AL, Dignum F, et al. Human-AI coevolution. Artif Intell. 2025 Feb;339:104244. DOI: 10.1016/j.artint.2024.104244
dc.identifier.doi http://dx.doi.org/10.1016/j.artint.2024.104244
dc.identifier.issn 0004-3702
dc.identifier.uri http://hdl.handle.net/10230/71408
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Artificial Intelligence. 2025 Feb;339:104244
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/ 952026
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/871042
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/834756
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.keyword Artificial intelligenceen
dc.subject.keyword Complex systemsen
dc.subject.keyword Computational social scienceen
dc.subject.keyword Human-AI coevolutionen
dc.title Human-AI coevolutionen
dc.type info:eu-repo/semantics/article
dc.type.version info:eu-repo/semantics/publishedVersion

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