Human-AI coevolution

Understanding the mutual influence between Artificial Intelligence and Society

Our paper on Human-AI coevolution is now published on the Artificial Intelligence journal!

An international team of AI and complexity scientists has worked together to delineate a new field of research, at the crossroads of artificial intelligence and complexity science, to better understand how humans and algorithms influence each other onto online platforms.

By "human-AI coevolution" we mean?the joint, intertwined dynamic process occurring between AI-based recommenders and human behaviour, on top of various kinds of online platforms. A process based on an iterative feedback loop of mutual influence, widely discussed in our paper. A process that?leads to systemic outcomes and that in our opinion deserves a deeper study from a multi-disciplinary perspective, in order to be understood, modelled, and governed.

The article lays the groundwork for studying human-AI coevolution as a phenomenon with deep ethical and social implications. We discuss concrete examples of human-AI ecosystems: not only social media and digital marketplaces, but also other large online platforms such as geographic mapping and navigation services as well as chatbots based on generative AI.

Big thanks to all fantastic co-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

We love to hear your comments!

Cite as:

Pedreschi, Pappalardo et al. Human-AI coevolution. Artificial Intelligence, Volume 339 (2025) 104244, https://doi.org/10.1016/j.artint.2024.104244.