Human-AI coevolution
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.
Authors
- Alex Pentland (ORCID: https://orcid.org/0000-0002-8053-9983)
- Andrea Passarella (ORCID: https://orcid.org/0000-0002-1694-612X)
- Fosca Giannotti (ORCID: https://orcid.org/0000-0003-3099-3835)
- Albert-Ĺaszló Barabási (ORCID: https://orcid.org/0000-0002-4028-3522)
- Alistair Knott (ORCID: https://orcid.org/0000-0003-3036-4668)
- János Kertész (ORCID: https://orcid.org/0000-0003-4957-5406)
- Dino Pedreschi (ORCID: https://orcid.org/0000-0003-4801-3225)
- Alessandro Vespignani (ORCID: https://orcid.org/0000-0003-3419-4205)
- John Shawe‐Taylor (ORCID: https://orcid.org/0000-0002-2030-0073)
- Virginia Dignum (ORCID: https://orcid.org/0000-0001-7409-5813)
- Paul Lukowicz (ORCID: https://orcid.org/0000-0003-0320-6656)
- Luca Pappalardo (ORCID: https://orcid.org/0000-0002-1547-6007)
- Frank Dignum (ORCID: https://orcid.org/0000-0002-5103-8127)
- Ricardo Baeza‐Yates (ORCID: https://orcid.org/0000-0003-3208-9778)
- Tina Eliassi‐Rad (ORCID: https://orcid.org/0000-0002-1892-1188)
- Yannis Ioannidis (ORCID: https://orcid.org/0000-0002-1705-8247)
- Emanuele Ferragina
Institutions
- University of Pisa (IT)
- Northeastern University (US)
- Centre National de la Recherche Scientifique (FR)
- University of Kaiserslautern (DE)
- Scuola Normale Superiore (IT)
- Central European University (AT)
- National and Kapodistrian University of Athens (GR)
- Institut d'Etudes Politiques de Paris (FR)
- Victoria University of Wellington (NZ)
- Consorzio Pisa Ricerche (IT)
- Institute of Informatics and Telematics (IT)
- Instituto di Biofisica (ES)
- Silicon Valley University (US)
- National Research Council (IT)
- Athena Research and Innovation Center In Information Communication & Knowledge Technologies (GR)
- Centre de recherche sur les Inégalités Sociales (FR)
- University College London (GB)
- Massachusetts Institute of Technology (US)
- University of Otago (NZ)
- Umeå University (SE)
Publication Details
- Journal
- Artificial Intelligence
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1016/j.artint.2024.104244
- Citations
- 39
- Primary Topic
- Evolutionary Game Theory and Cooperation
- Type
- article
- Field-Weighted Citation Impact
- 44.15
Funders
- European Commission
- Agence Nationale de la Recherche
- Eesti Teadusagentuur
- Austrian Science Fund
- Bulgarian National Science Fund
- Université de Paris
- Horizon 2020 Framework Programme
- Engineering and Physical Sciences Research Council
- CHIST-ERA