This area of research intends to identify actionable findings on how to effectively lead teams where some members are people, and others are intelligent machines. The point of entry for AI in many organizations will be teams, whose members are highly specialized and coordinate complex work. Leading human-AI teams requires an understanding of how the introduction of AI into teams will affect team dynamics. Our guiding question is: how can we successfully lead and incorporate the collaboration of an “AI teammate” in an otherwise all human team?
ARL The Signatures of Success in Human-Agent Teams
Rapid technological advances offer new ways to augment human collaboration to make military engagements safer and more effective. Realizing this vision will require foundational advances to understand the affective, cognitive, and behavioral underpinnings of effective human-agent teams. Simply put, how do individuals and agents interact effectively in teams? To answer this question, we must understand both human-agent interaction, and also the nature of interpersonal interactions when agents join teams. Our project discovers the signatures of success in human-agent teams using advances in event network Analysis.
TEAMS: Teamwork-Enhancing Adaptive Machine Synergies
Project TEAMS (Teamwork-Enhancing Adaptive Machine Synergies) leverages advances in Artificial Intelligence to create human-machine partnerships that scale up teamwork by enabling decentralized networks of teams to collaborate and adapt effectively. Teams have become a cornerstone of modern organizations. Complex organizations from Ford to Apple to the US military use teams as a distinct advantage. Working in teams empowers people and organizations by providing greater access to ideas and information where and when needed. A key challenge to unlocking the full potential of teams remains. While individual teams can build the degree of trust and shared understanding needed to perform, organizations require the decentralized and coordinated action of multiple teams to achieve their missions.
- Schecter, A.M., Hohenstein, J., Larson, L.E., Harris, A., Hou, T., Lee, W., Lauharatanahirun, N., DeChurch, L., Contractor, N.S., Jung, M. (2023). Vero: An accessible method for studying Human-AI teamwork. Computers in Human Behavior, 141.
- Harris-Watson, A., Larson, L., DeChurch, L.A., & Contractor, N.S. (2023). Social perception in human-AI teams: Warmth and competence predict receptivity to AI teammates. Computers in Human Behavior, 145, 107765.
- Hohenstein, J., Larson, L.E., Hou., Y.T., Harris, M.A., Schecter, A., DeChurch, L.A., Contractor, N., Jung, M.F. (2022, January) Vero: A method for remotely studying Human-AI collaboration. In Proceedings of the 55th Hawaii International Conference on System Sciences, 254-263.
Doctoral Dissertations Supported:
- Harris, A.M. (2023) “Alexa, how can I trust you again?” Trust repair in Human-AI teaming. Northwestern University, Evanston, IL.
- Larson, L. (2021) Leading teams in the digital age: Team technology adaptation in human-agent teams. Northwestern University, Evanston, IL. Dissertation was awarded an honorable mention for the Psychology of Technology Dissertation Award, and finalist for the Richard Hackman Dissertation Award.
Research supported by:
- U.S. Army Research Laboratory – ARL (2019-2025)
- Microsoft Corporation (2023-2024)