Human-AI Coevolution

Dino Pedreschi; Luca Pappalardo; Emanuele Ferragina; Ricardo Baeza-Yates; Albert-Laszlo Barabasi; Frank Dignum; Virginia Dignum; Tina Eliassi-Rad; Fosca Giannotti; Janos Kertesz; Alistair Knott; Yannis Ioannidis; Paul Lukowicz; Andrea Passarella; Alex Sandy Pentland; John Shawe-Taylor; Alessandro Vespignani

arXiv · 2306.13723

Human-AI Coevolution

Dino Pedreschi·Luca Pappalardo·Emanuele Ferragina·Ricardo Baeza-Yates·Albert-Laszlo Barabasi·Frank Dignum·Virginia Dignum·Tina Eliassi-Rad·Fosca Giannotti·Janos Kertesz·Alistair Knott·Yannis Ioannidis·Paul Lukowicz·Andrea Passarella·Alex Sandy Pentland·John Shawe-Taylor·Alessandro Vespignani

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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 on 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'' social outcomes. This paper introduces Coevolution AI 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., technical, epistemological, legal and socio-political.

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Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, Janos Kertesz, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani. 2023-06-23. Human-AI Coevolution. https://arxiv.org/abs/2306.13723

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