COHUMAIN: Building the Socio-Cognitive Architecture of Collective Human-Machine Intelligence

In recent years, we have experienced rapid development of advanced technology, machine learning, and artificial intelligence (AI), intended to interact with and augment the abilities of humans in practically every area of life. With the rapid growth of new capabilities, such as those enabled by generative AI (e.g., ChatGPT), AI is increasingly at the center of human communication and collaboration, resulting in a growing recognition of the need to understand how humans and AI can integrate their inputs in collaborative teams. However, there are many unanswered questions regarding how human-AI collective intelligence will emerge and what the barriers might be. Truly integrated collaboration between humans and intelligent agents may result in a different way of working that looks nothing like what we know now, and it is important to keep the essential goal of human societal well-being and prosperity a priority. In this special issue, we begin to scope out the underpinnings of a socio-cognitive architecture for Collective HUman-MAchine INtelligence (COHUMAIN), which is the study of the capability of an integrated human and machine (i.e., intelligent technology) system to achieve goals in a wide range of environments. This topic consists of nine papers including a description of the conceptual foundation for a socio-cognitive architecture for COHUMAIN, empirical tests of some aspects of this architecture, research on proposed representations of intelligent agents that can jointly interact with humans, empirical tests of human-human and human-machine interactions, and philosophical and ethical issues to consider as we develop these systems.