AI-enhanced Collective Intelligence

• URL: http://arxiv.org/abs/2403.10433v4
• Date: Thu, 26 Sep 2024 09:33:29 GMT
• Title: AI-enhanced Collective Intelligence
• Authors: Hao Cui, Taha Yasseri,
• Abstract summary: Humans and AI possess complementary capabilities that can surpass the collective intelligence of either humans or AI in isolation. This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence. We explore how agents' diversity and interactions influence the system's collective intelligence and analyze real-world instances of AI-enhanced collective intelligence.
• Score: 2.5063318977668465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current societal challenges exceed the capacity of humans operating either alone or collectively. As AI evolves, its role within human collectives will vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, together, can surpass the collective intelligence of either humans or AI in isolation. However, the interactions in human-AI systems are inherently complex, involving intricate processes and interdependencies. This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence, comprising cognition, physical, and information layers. Within this multilayer network, humans and AI agents exhibit varying characteristics; humans differ in diversity from surface-level to deep-level attributes, while AI agents range in degrees of functionality and anthropomorphism. We explore how agents' diversity and interactions influence the system's collective intelligence and analyze real-world instances of AI-enhanced collective intelligence. We conclude by considering potential challenges and future developments in this field.

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