2024
Journal article  Open Access

Human-AI coevolution

Pedreschi D., Pappalardo L., Ferragina E., Baeza-Yates R., Barabási A-L., Dignum F., Dignum V., Eliassi-Rad T., Giannotti F., Kertész J., Knott A., Ioannidis Y., Lukowicz P., Passarella A., Pentland A. S., Shawe-Taylor J., Vespignani A.

Human-AI coevolution  Artificial Intelligence  Computational social science  Complex systems  Computational Social Science  FOS: Computer and information sciences  Computer Sciences  Artificial Intelligence (cs.AI)  [SHS.SOCIO]Humanities and Social Sciences/Sociology  Societal Risks  Collective intelligence  Datavetenskap (datalogi)  Artificial intelligence  Computer Science - Artificial Intelligence 

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.

Source: ARTIFICIAL INTELLIGENCE, vol. 339


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BibTeX entry
@article{oai:iris.cnr.it:20.500.14243/514878,
	title = {Human-AI coevolution},
	author = {Pedreschi D. and Pappalardo L. and Ferragina E. and Baeza-Yates R. and Barabási A-L. and Dignum F. and Dignum V. and Eliassi-Rad T. and Giannotti F. and Kertész J. and Knott A. and Ioannidis Y. and Lukowicz P. and Passarella A. and Pentland A.  S. and Shawe-Taylor J. and Vespignani A.},
	doi = {10.1016/j.artint.2024.104244 and 10.48550/arxiv.2306.13723},
	year = {2024}
}

HumanE-AI-Net
HumanE AI Network

SAI: Social Explainable Artificial Intelligence
XAI
Science and technology for the explanation of AI decision making

SoBigData-PlusPlus
SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics

Social Explainable Artificial Intelligence (SAI)


OpenAIRE