National Academies Press: OpenBook
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2021. Human-AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. doi: 10.17226/26355.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2021. Human-AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. doi: 10.17226/26355.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2021. Human-AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. doi: 10.17226/26355.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2021. Human-AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. doi: 10.17226/26355.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2021. Human-AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. doi: 10.17226/26355.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2021. Human-AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. doi: 10.17226/26355.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2021. Human-AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. doi: 10.17226/26355.
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Page viii Cite
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2021. Human-AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. doi: 10.17226/26355.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Human-AI Teaming: State-of-the-Art and Research Needs Committee on Human-System Integration Research Topics for the 711th Human Performance Wing of the Air Force Research Laboratory Board on Human-Systems Integration Division of Behavioral and Social Sciences and Education A Consensus Study Report of PREPUBLICATION COPY – Uncorrected Proofs

THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001 This activity was supported by contract number WBSRA-21-10-NAS between the National Academy of Sciences and the Wright Brothers Institute as a subcontract to the Air Force Research Laboratory. Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project. Also supporting the Committee’s work are the Board on Human-System Integration core sponsorship grants with the National Aeronautics and Space Administration, US Army Research Laboratory, the Human Factors and Ergonomics Society, and the Society for Human Resource Management. International Standard Book Number-13: 978-0-309-XXXXX-X International Standard Book Number-10: 0-309-XXXXX-X Digital Object Identifier: https://doi.org/10.17226/26355 Additional copies of this publication are available from the National Academies Press, 500 Fifth Street, NW, Keck 360, Washington, DC 20001; (800) 624-6242 or (202) 334-3313; http://www.nap.edu. Copyright 2021 by the National Academy of Sciences. All rights reserved. Printed in the United States of America Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2021. Human- AI Teaming: State of the Art and Research Needs. Washington, DC: The National Academies Press. https://doi.org/10.17226/26355. PREPUBLICATION COPY – Uncorrected Proofs

The National Academy of Sciences was established in 1863 by an Act of Congress, signed by President Lincoln, as a private, nongovernmental institution to advise the nation on issues related to science and technology. Members are elected by their peers for outstanding contributions to research. Dr. Marcia McNutt is president. The National Academy of Engineering was established in 1964 under the charter of the National Academy of Sciences to bring the practices of engineering to advising the nation. Members are elected by their peers for extraordinary contributions to engineering. Dr. John L. Anderson is president. The National Academy of Medicine (formerly the Institute of Medicine) was established in 1970 under the charter of the National Academy of Sciences to advise the nation on medical and health issues. Members are elected by their peers for distinguished contributions to medicine and health. Dr. Victor J. Dzau is president. The three Academies work together as the National Academies of Sciences, Engineering, and Medicine to provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The National Academies also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding in matters of science, engineering, and medicine. Learn more about the National Academies of Sciences, Engineering, and Medicine at www.nationalacademies.org. PREPUBLICATION COPY – Uncorrected Proofs

Consensus Study Reports published by the National Academies of Sciences, Engineering, and Medicine document the evidence-based consensus on the study’s statement of task by an authoring committee of experts. Reports typically include findings, conclusions, and recommendations based on information gathered by the committee and the committee’s deliberations. Each report has been subjected to a rigorous and independent peer-review process and it represents the position of the National Academies on the statement of task. Proceedings published by the National Academies of Sciences, Engineering, and Medicine chronicle the presentations and discussions at a workshop, symposium, or other event convened by the National Academies. The statements and opinions contained in proceedings are those of the participants and are not endorsed by other participants, the planning committee, or the National Academies. For information about other products and activities of the National Academies, please visit www.nationalacademies.org/about/whatwedo. PREPUBLICATION COPY – Uncorrected Proofs

COMMITTEE ON HUMAN-SYSTEM INTEGRATION RESEARCH TOPICS FOR THE 711TH HUMAN PERFORMANCE WING OF THE AIR FORCE RESEARCH LABORATORY MICA R. ENDSLEY, (Chair) SA Technologies BARRETT S. CALDWELL, Purdue University ERIN K. CHIOU, Arizona State University NANCY J. COOKE, Arizona State University MARY L. CUMMINGS, Duke University CLEOTILDE GONZALEZ, Carnegie Mellon University JOHN D. LEE, University of Wisconsin-Madison NATHAN J. MCNEESE, Clemson University CHRISTOPHER MILLER, Smart Information Flow Technologies EMILIE ROTH, Roth Cognitive Engineering WILLIAM B. ROUSE, NAE Georgetown University Staff DANIEL TALMAGE, Study Director v PREPUBLICATION COPY – Uncorrected Proofs

BOARD ON HUMAN-SYSTEMS INTEGRATION FREDERICK OSWALD, Department of Psychology, Rice University, Chair JAMES BAGIAN, NAE/NAM, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor DIANA BURLEY, Graduate School of Education and Human Development, George Washington University BARBARA DOSHER, NAS, School of Social Sciences, University of California, Irvine MICA ENDSLEY, SA Technologies, Mesa, Arizona EDMOND ISRAELSKI, AbbVie, North Chicago, Illinois NAJMEDIN MESHKATI, Viterbi School of Engineering, University of Southern California JOHN LOCKETT, United States Army Research Laboratory (Retired) EMILIE ROTH, Roth Cognitive Engineering, Stanford, California WILLIAM J. STRICKLAND, Human Resources Research Organization, Alexandria, Virginia MATTHEW WEINGER, Vanderbilt University Medical Center Staff MARY ELLEN O’CONNELL, Interim Director TOBY M. WARDEN, Director (Until 5/25/2021) vi PREPUBLICATION COPY – Uncorrected Proofs

Preface Artificial intelligence (AI) is being proposed as a force multiplier for the military. AI brings its own unique challenges, however, which must be balanced with effective human oversight, particularly in operations with high-consequence outcomes. AI therefore needs to work effectively as a part of a distributed team. This report addresses the state-of-the-art in human-AI teaming and establishes a framework for future research to meet the goal of effective use of AI for future defense operations. I wish to express my deep appreciation to the members of the committee for their diligent and dedicated contributions. The committee’s expertise and knowledge were indispensable throughout our deliberations and the writing of the report. Their efforts, which often required working nights and weekends, are particularly notable given the incredibly challenging year. I cannot thank them enough. On behalf of the entire committee, I also wish to thank the National Academies of Sciences, Engineering, and Medicine staff for their outstanding support and guidance. I am also deeply appreciative to Heather Kreidler for her writing and fact checking. The report benefited deeply from the editing skills of Susan Debad. Additionally, I want to express our sincere gratitude to everyone who contributed their time, expertise, and experiences to our committee, especially all the workshop presenters and attendees. The presentations, resources, and insights contributed immensely to our deliberations. Finally, I wish to thank the AFRL for their partnership and forthright participation throughout this process. I offer this report in the spirit of that partnership and believe that the research areas discussed in the report will be useful to the sponsor as they move forward. Mica Endsley, Chair Committee on Human-System Integration Research Topics for the 711th Human Performance Wing of the Air Force Research Laboratory vii PREPUBLICATION COPY – Uncorrected Proofs

Acknowledgment of Reviewers This Consensus Study Report was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published report as sound as possible and to ensure that it meets the institutional standards for objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process. We thank the following individuals for their review of this report: John Allspaw, Principal, Adaptive Capacity Labs, LLC, New York, NY Ellen J. Bass, Department of Health Systems and Sciences Research, College of Nursing and Health Professions, and Department of Systems and Information Engineering, College of Computing and Informatics, Drexel University Ann Bisantz, Industrial and Systems Engineering, University at Buffalo, The State University of New York Michael Coovert, Department of Psychology emeritus, University of South Florida Cindy Dominguez, Principal Cognitive Scientist, The Mitre Corporation Stephen M. Fiore, Cognitive Sciences, Department of Philosophy, University of Central Florida Jamie C. Gorman, Engineering Psychology, Georgia Institute of Technology Julie A. Shah, Interactive Robotics Group, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology James C. Spohrer, Board of Directors, International Society of Service Innovation Professionals (ISSIP) Although the reviewers listed above have provided many constructive comments and suggestions, they were not asked to endorse the conclusions or recommendations of this report nor did they see the final draft before its release. The review of this report was overseen by Robert F. Sproull, Computer Science, University of Massachusetts at Amhers and Julie J.C.H. Ryan, Chief Executive Officer, Wyndrose Technical Group. They were responsible for making certain that an independent examination of this report was carried out in accordance with the standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content of the report rests entirely with the authoring committee and the National Academies. ix PREPUBLICATION COPY – Uncorrected Proofs

Contents SUMMARY 1 1. INTRODUCTION 5 Study Background and Charge to the Committee, 5 Committee Approach, 6 Automation and AI, 7 Limits of AI, 8 Effect of AI on Human Performance, 8 Report Structure and Summary, 9 2. HUMAN-AI TEAMING METHODS AND MODELS 11 Teams, 11 Human-AI Teaming Models and Perspectives, 12 Should Humans Team with AI?, 14 Improved Models for Human-AI Teams, 16 Key Challenges and Research Gaps, 17 Research Needs, 17 Summary, 19 3. HUMAN-AI TEAMING PROCESSES AND EFFECTIVENESS 21 What Does It Mean for AI to Be a Teammate?, 21 Processes and Characteristics of Effective Human-AI Teams, 22 Team Heterogeneity, 22 Shared Cognition, 23 Communication and Coordination, 23 Social Intelligence, 24 Other Features of Effective Teams, 25 Key Challenges and Research Gaps, 25 Research Needs, 25 Summary, 26 4. SITUATION AWARENESS IN HUMAN-AI TEAMS 27 Situation Awareness in Multi-Domain Operations, 27 Key Challenges and Research Gaps, 28 Research Needs, 29 Shared SA in Human-AI Teams, 29 Key Challenges and Research Gaps, 31 Research Needs, 31 Summary, 32 5. AI TRANSPARENCY AND EXPLAINABILITY 33 Display Transparency, 36 Key Challenges and Research Gaps, 37 x PREPUBLICATION COPY – Uncorrected Proofs

Research Needs, 37 AI Explainability, 38 Key Challenges and Research Gaps, 39 Research Needs, 40 Summary, 42 6. HUMAN-AI TEAM INTERACTION 43 Level of Automation, 43 Key Challenges and Research Gaps, 46 Research Needs, 46 AI Dynamics and Temporality, 47 Key Challenges and Research Gaps, 47 Research Needs, 47 Granularity of Control, 48 Key Challenges and Research Gaps, 49 Research Needs, 49 Other Human-AI Team Interaction Issues, 49 Key Challenges and Research Gaps, 50 Research Needs, 50 Summary, 50 7. TRUSTING AI TEAMMATES 51 Trust Frameworks Past and Present, 51 Trusting AI in Complex Work Environments, 53 Key Challenges and Research Gaps, 54 Research Needs, 54 Summary, 57 8. IDENTIFICATION AND MITIGATION OF BIAS IN HUMAN-AI TEAMS 59 Human Biases, 59 AI Biases, 59 Human-AI Team Bias, 60 Key Challenges and Research Gaps, 61 Research Needs, 62 Summary, 63 9. TRAINING HUMAN-AI TEAMS 65 Human-Human Team Training to Inform Human-AI Team Training, 65 Strategies for Team Training, 65 The Use of Simulation, 66 Training Content: Taskwork and Teamwork, 66 Key Challenges and Research Gaps, 67 Research Needs, 68 Summary, 69 xi PREPUBLICATION COPY – Uncorrected Proofs

10. HSI PROCESSES AND MEASURES OF HUMAN-AI COLLABORATION AND PERFORMANCE 71 Taking an HSI Perspective in Human-AI Team Design and Implementation, 71 Key Challenges and Research Gaps, 72 Research Needs, 72 Requirements for Research in Human-AI Team Development, 73 Key Challenges and Research Gaps, 73 Research Needs, 73 Research Team Competencies, 74 Key Challenges and Research Gaps, 75 Research Needs, 75 HSI Considerations for Human-AI Teams, 75 Key Challenges and Research Gaps, 77 Research Needs, 77 Testing, Evaluation, Verification, and Validation of Human-AI Teams, 78 Key Challenges and Research Gaps, 79 Research Needs, 79 Human-AI Team Research Testbeds, 80 Key Challenges and Research Gaps, 81 Research Needs, 81 Human-AI Team Measures and Metrics, 81 Key Challenges and Research Gaps, 83 Research Needs, 83 Agile Software Development and HSI, 83 Key Challenges and Research Gaps, 85 Research Needs, 85 Summary, 85 11. CONCLUSIONS 87 12. REFERENCES 93 APPENDIXES A Committee Biographical Information A-1 B Committee Meeting Agenda B-1 C Definitions C-1 xii PREPUBLICATION COPY – Uncorrected Proofs

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Although artificial intelligence (AI) has many potential benefits, it has also been shown to suffer from a number of challenges for successful performance in complex real-world environments such as military operations, including brittleness, perceptual limitations, hidden biases, and lack of a model of causation important for understanding and predicting future events. These limitations mean that AI will remain inadequate for operating on its own in many complex and novel situations for the foreseeable future, and that AI will need to be carefully managed by humans to achieve their desired utility.

Human-AI Teaming: State-of-the-Art and Research Needs examines the factors that are relevant to the design and implementation of AI systems with respect to human operations. This report provides an overview of the state of research on human-AI teaming to determine gaps and future research priorities and explores critical human-systems integration issues for achieving optimal performance.

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