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AI-Enhanced Collective Intelligence

Cui, Yasseri

2024Patterns42 citations

Review / survey / meta-analysisInterdisciplinaryTheoretical model

LLM / Generative AIMachine Learning (pre-LLM)AI (General)Human-AI collaborationCollective intelligence / teamsDecision-makingAugmentation vs. substitution

View Repository DOI: 10.1016/j.patter.2024.101074

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.

Summary

Cui and Yasseri conduct a narrative review guided by complexity theory and network science to conceptualize AI-enhanced collective intelligence as a multilayer system, analyzing over 900 real-world applications and synthesizing interdisciplinary literature on how AI augments human group performance

Main Finding

Human-AI hybrid systems can achieve superior collective intelligence compared to either humans or AI alone by leveraging complementary capabilities, but success depends on multiple factors including trust, communication, diversity, group structure, and appropriate role design for AI agents

Primary Datasets

Supermind Design Augmented Collective Intelligence Database (938 AI-CI applications)

Secondary Datasets

None (review paper synthesizing existing literature)

Key Methods: Narrative literature review guided by complexity theory and network science framework; analysis of Supermind Design database of 938 AI-CI applications
Sample Period: 2000-2024
Geographic Coverage: Global (review of international literature and case studies)
Sample Size: 938 AI-enhanced collective intelligence applications from Supermind Design database; narrative review of interdisciplinary literature
Level of Analysis: Individual, Firm, Task
Occupation Classification: None
Industry Classification: None
Replication Package: Yes

Notes

Patterns, vol. 5, no. 11 [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations. [Claude classification]: Interdisciplinary review integrating complexity science, network science, psychology, computer science, and organizational behavior. Proposes multilayer network framework (cognition, physical, information layers) for understanding human-AI collective intelligence. Analyzes 938 real-world AI-CI applications from Supermind Design database across 12 sectors. Not a traditional meta-analysis but comprehensive narrative review with case study illustrations.