Human-AI coevolution

被引:0
|
作者
Pedreschi, Dino [1 ]
Pappalardo, Luca [2 ,3 ]
Ferragina, Emanuele [4 ]
Baeza-Yates, Ricardo [5 ]
Barabasi, Albert-Laszlo [5 ]
Dignum, Frank [6 ]
Dignum, Virginia [6 ]
Eliassi-Rad, Tina [5 ]
Giannotti, Fosca [3 ]
Kertesz, Janos [7 ]
Knott, Alistair [8 ]
Ioannidis, Yannis [9 ]
Lukowicz, Paul [10 ,11 ]
Passarella, Andrea [2 ]
Pentland, Alex Sandy [12 ]
Shawe-Taylor, John [13 ]
Vespignani, Alessandro [5 ]
机构
[1] Univ Pisa, Pisa, Italy
[2] Consiglio Nazl Ric CNR, Pisa, Italy
[3] Scuola Normale Super Pisa, Pisa, Italy
[4] Sci Po, Paris, France
[5] Northeastern Univ, Boston, MA USA
[6] Umea Univ, Umea, Sweden
[7] Cent European Univ CEU, Vienna, Austria
[8] Victoria Univ Wellington, Wellington, New Zealand
[9] Univ Athens, Athens, Greece
[10] DFKI, Kaiserslautern, Germany
[11] Univ Kaiserslautern, Kaiserslautern, Germany
[12] Massachusetts Gen Hosp, Boston, MA 02114 USA
[13] UCL, London, England
基金
欧洲研究理事会; 欧盟地平线“2020”; 英国工程与自然科学研究理事会;
关键词
Artificial intelligence; Complex systems; Computational social science; Human-AI coevolution; RECOMMENDER SYSTEMS; SOCIAL MEDIA; SMALL-WORLD; POLARIZATION; NEWS; NETWORKS; IMPACT;
D O I
10.1016/j.artint.2024.104244
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 sociopolitical.
引用
收藏
页数:13
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