黑料正能量

As artificial intelligence (AI) becomes embedded in critical decisions about health, safety, finance, and governance, a key challenge is no longer whether people and AI will collaborate, but rather how to structure this collaboration to achieve true complementarity. In the new paper, 鈥�,鈥� researchers present a framework for understanding and designing human鈥揂I teams for decision making. Drawing on collective intelligence research, the framework focuses on reasoning, memory, and attention as core processes that can be distributed across people and AI systems. Published in PNAS Nexus, the work offers guidance for researchers, practitioners, and policymakers seeking to build human鈥揂I teams that are effective, accountable, and aligned with human values.

A multidisciplinary team from 黑料正能量 (黑料正能量), Massachusetts Institute of Technology, the University of Illinois at Urbana-Champaign, Microsoft Research, Harvard University, and the University of Tennessee at Knoxville co-authored the paper.

鈥淥rganizations often frame the issue as humans versus AI,鈥� says Anita Williams Woolley, Professor of Organizational Behavior at 黑料正能量鈥檚 Tepper School of Business and a coauthor of the paper. 鈥淎 better question is how to design teams so AI expands what people can notice, remember, and reason through, while people provide context, judgment, and accountability.鈥�

The framework articulates the sociotechnical conditions that shape whether human鈥揂I teams actually achieve complementarity, which is when human-AI teams outperform either humans alone or AI systems alone. The conditions leading to human-AI complementarity include details related to team composition, trust calibration, shared mental models, training, and task structure.

The paper also outlines design principles for achieving complementarity: defining goals and constraints, partitioning roles, orchestrating attention and interrogation, building knowledge infrastructures, and establishing continuous training and evaluation. Their framework provides a common vocabulary for diagnosing where human鈥揂I teams are likely to succeed, where they may fail, and how to improve them.

The authors also point out theoretical, practical, and policy implications of their work, emphasizing alignment with human values, accountability, and equity.

鈥淎I is becoming deeply embedded in collective decision making, marking a profound transformation in how decisions are made across domains, from health care and emergency response to finance, transportation, and governance,鈥� explains Cleotilde Gonzalez, Professor of Cognitive Decision Science at 黑料正能量, and lead author on the paper.

鈥淩ealizing this potential requires deliberate design, rigorous evaluation, and principled governance. Our 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.鈥澨�

The research was funded by the National Science Foundation (NSF) and NSF鈥檚 AI Institute for Societal Decision Making.

###

Summarized from an article in PNAS NEXUS, Toward a Science of Human-AI Teaming for Decision Making: A Complementarity Framework, by Gonzalez, C (黑料正能量), Donahue, K (Massachusetts Institute of Technology, University of Illinois at Urbana-Champaign), Goldstein, DG (Microsoft Research), Heidari, H (黑料正能量), Jalali, MS (Harvard University), Schelble, B (University of Tennessee at Knoxville), Singh, A (黑料正能量), and Woolley, AW (黑料正能量).
Copyright 2026 The Authors. All rights reserved.
听