Human-AI Coevolution (Abstract Reprint)

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

doi:10.24963/ijcai.2025/1231

Human-AI Coevolution (Abstract Reprint)

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

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Journal Track. Pages 10957-10957.

https://doi.org/10.24963/ijcai.2025/1231

Keywords

IJCAI: Journal Track

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.

Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-László Barabási, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex `Sandy' Pentland, John Shawe-Taylor, and Alessandro Vespignani. 2025. Human-AI Coevolution (Abstract Reprint). In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10957–10957. https://doi.org/10.24963/ijcai.2025/1231

@inproceedings{ijcai2025p1231,
  title     = {Human-AI Coevolution (Abstract Reprint)},
  author    = {Pedreschi, Dino and Pappalardo, Luca and Ferragina, Emanuele and Baeza-Yates, Ricardo and Barabási, Albert-László and Dignum, Frank and Dignum, Virginia and Eliassi-Rad, Tina and Giannotti, Fosca and Kertész, János and Knott, Alistair and Ioannidis, Yannis and Lukowicz, Paul and Passarella, Andrea and Pentland, Alex `Sandy' and Shawe-Taylor, John and Vespignani, Alessandro},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {10957--10957},
  year      = {2025},
  month     = {8},
  note      = {Journal Track},
  doi       = {10.24963/ijcai.2025/1231},
  url       = {https://doi.org/10.24963/ijcai.2025/1231},
}

TY  - CPAPER
TI  - Human-AI Coevolution (Abstract Reprint)
AU  - Pedreschi, Dino
AU  - Pappalardo, Luca
AU  - Ferragina, Emanuele
AU  - Baeza-Yates, Ricardo
AU  - Barabási, Albert-László
AU  - Dignum, Frank
AU  - Dignum, Virginia
AU  - Eliassi-Rad, Tina
AU  - Giannotti, Fosca
AU  - Kertész, János
AU  - Knott, Alistair
AU  - Ioannidis, Yannis
AU  - Lukowicz, Paul
AU  - Passarella, Andrea
AU  - Pentland, Alex `Sandy'
AU  - Shawe-Taylor, John
AU  - Vespignani, Alessandro
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 10957
EP  - 10957
DO  - 10.24963/ijcai.2025/1231
UR  - https://doi.org/10.24963/ijcai.2025/1231
ER  -