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Human-AI coevolution
Title / Series / Name
Publication Volume
Publication Issue
Pages
Authors
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 Sandy
Shawe-Taylor, John
Vespignani, Alessandro
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 Sandy
Shawe-Taylor, John
Vespignani, Alessandro
Editors
Keywords
Artificial intelligence
Complex systems
Computational social science
Human-AI coevolution
Language and Linguistics
Linguistics and Language
Artificial Intelligence
Complex systems
Computational social science
Human-AI coevolution
Language and Linguistics
Linguistics and Language
Artificial Intelligence
URI
https://hdl.handle.net/20.500.14018/26971
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2025-02
Language
ISBN
Identifiers
10.1016/j.artint.2024.104244