Advancing Collective Intelligence in Human–AI Collaboration: Foundations for the COHUMAIN Framework

Abstract

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capabilities in many ways, how can we ensure that the sociotechnical system as a whole—comprising 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. Integrating these diverse perspectives and methods is crucial at this juncture. To truly advance our understanding of this important and rapidly evolving area, we need frameworks to facilitate research that bridges 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. To illustrate the approach we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, which articulates the critical processes underlying the emergence and functioning of collective intelligence in human–AI collaborations.