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 rather how to structure this collaboration to achieve true complementarity. Human-AI complementarity refers to the conditions under which human-AI teams outperform either humans alone or AI systems alone. 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 and anchored in the foundational cognitive processes—reasoning, memory, and attention—to understand and engineer effective human-AI teams. We examine the sociotechnical factors that shape team 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. 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.