Toward a science of human–AI teaming for decision-making: A complementarity framework

Abstract As artificial intelligence (AI) becomes embedded in critical decisions involving health, safety, finance, and governance, the key challenge is no longer whether humans and AI will collaborate, but how to structure this collaboration to achieve true complementarity, conditions under which Human-AI teams outperform either humans or AI-only teams. This paper advances the science of Human–AI teaming for decision-making by integrating insights from cognitive science, AI, human factors, organizational behavior, and ethics. We propose a framework grounded in collective intelligence, anchored in the foundational processes of reasoning, memory, and attention, for understanding and engineering effective Human–AI teams. We examine how Human–AI teams can achieve complementarity, and identify the sociotechnical factors that shape their effectiveness, including team composition, trust calibration, shared mental models, training, and task structure. We then outline design principles for achieving complementarity: defining goals and constraints, partitioning roles, orchestrating attention and interrogation, building knowledge infrastructures, and establishing continuous training and evaluation. We conclude with theoretical, practical, and policy implications, emphasizing alignment with human values, accountability, and equity. Taken together, these insights offer a roadmap for building Human–AI teams that are not only high-performing and adaptive but also transparent, trustworthy, and fundamentally human-centered.