Fostering Collective Intelligence in Human–AI Collaboration: Laying the Groundwork for COHUMAIN

• Published in: Topics in cognitive science Vol. 17; no. 2; pp. 189 - 216
• Main Authors: Gupta, Pranav, Nguyen, Thuy Ngoc, Gonzalez, Cleotilde, Woolley, Anita Williams
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.04.2025; John Wiley and Sons Inc
• Subjects: Artificial Intelligence; Artificial social intelligence; Cognitive architectures; Collaboration; Collective intelligence; Cooperative Behavior; Humans; Human–AI collaboration; Instance‐based learning; Intelligence; Interdisciplinary Research; Original; Sociocognitive architectures; Collective intelligence; Cognitive architectures; Instance‐based learning; Artificial social intelligence; Human–AI collaboration; Sociocognitive architectures
• ISSN: 1756-8757; 1756-8765; 1756-8765
• DOI: 10.1111/tops.12679

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain—Collective Human‐Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human–AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance‐based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine intelligence. How do we know that a sociotechnical system, as a whole, is exhibiting collective intelligence? We outline a research agenda for COHUMAIN by proposing sociocognitive architectures as a vehicle for designing and developing the dynamics of sociotechnical systems and illustrate this holistic approach by discussing the transactive systems model of collective intelligence and instance‐based learning.