Abstract
The bigger picture
The challenges facing society today are becoming increasingly complex, surpassing what human efforts alone can manage, whether individually or collectively. As artificial intelligence (AI) evolves and improves, some might believe that replacing human intelligence with AI could solve our societal challenges. This notion has been criticized. Instead, we believe that AI can enhance human collective intelligence rather than replace it. Humans bring intuition, creativity, and diverse experiences, while AI offers vast computational power and rapid data processing. Combining these strengths can create a level of collective intelligence greater than the sum of its parts. However, understanding how humans and AI can effectively collaborate to achieve this collective intelligence is a complex and intellectually stimulating task. This is a new territory for us and requires multidisciplinary research to be harnessed effectively.
Summary
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.
The challenges facing society today are becoming increasingly complex, surpassing what human efforts alone can manage, whether individually or collectively. As artificial intelligence (AI) evolves and improves, some might believe that replacing human intelligence with AI could solve our societal challenges. This notion has been criticized. Instead, we believe that AI can enhance human collective intelligence rather than replace it. Humans bring intuition, creativity, and diverse experiences, while AI offers vast computational power and rapid data processing. Combining these strengths can create a level of collective intelligence greater than the sum of its parts. However, understanding how humans and AI can effectively collaborate to achieve this collective intelligence is a complex and intellectually stimulating task. This is a new territory for us and requires multidisciplinary research to be harnessed effectively.
Summary
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.
| Original language | English |
|---|---|
| Article number | 101074 |
| Journal | Patterns |
| Volume | 5 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2024 |
| Externally published | Yes |