---
title: "Human-AI Coevolution (Abstract Reprint)"
authors:
  - "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"
doi: "10.24963/ijcai.2025/1231"
url: "https://ijcai.org/proceedings/2025/1231"
pdf: "https://ijcai.org/proceedings/2025/1231.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10957-10957"
track: "Journal Track"
keywords:
  - "IJCAI: Journal Track"
---

# 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

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10957-10957. DOI: [10.24963/ijcai.2025/1231](https://doi.org/10.24963/ijcai.2025/1231)

## 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.