Human-AI Coevolution (Abstract Reprint)

Human-AI Coevolution (Abstract Reprint)

Dino Pedreschi1 , Luca Pappalardo2,3 , Emanuele Ferragina4 , Ricardo A. Baeza-Yates5 , Albert-L´aszl´o Barab´asi6 Frank P.M. Dignum7 , Virginia Dignum7 , Tina Eliassi-Rad6 ,

Fosca Giannotti3 , J´anos Kert´esz8 , Alistair Knott9 , Yannis E. Ioannidis10 , Paul Lukowicz 11 , Andrea Passarella12 , Alex Sandy Pentland13 , John S. Shawe-Taylor 14 and Alessandro Vespignani6

1 University of Pisa, Italy

2 Consiglio Nazionale delle Ricerche (CNR), Pisa, Italy

3 Scuola Normale Superiore, Pisa, Italy 4 Sciences Po, Paris, France

5 Universidad de Chile, Santiago, Chile

6 Northeastern University, Boston, MA, USA

7 Ume˚a Universitet, Umea, Sweden

8 Central European University, Vienna, Austria

9 Victoria University of Wellington, Wellington, New Zealand

10 National and Kapodistrian University of Athens, Athens, Greece

11 Rheinland-Pf¨alzische Technische Universit¨at Kaiserslautern-Landau, Kaiserslautern, Germany

12 Consiglio Nazionale delle Ricerche, Rome, Italy

13 Massachusetts Institute of Technology, Cambridge, MA, USA

14 University College London, London, UK

dino.pedreschi@unipi.it, luca.pappalardo@isti.cnr.it, emanuele.ferragina@sciencespo.fr

Abstract Reprint. This is an abstract reprint of a journal article by [Pedreschi et al., 2025].

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 humanAI 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.