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Empirically Understanding the Potential Impacts and Process of Social Influence in Human-AI Teams

Authors:

Christopher Flathmann,

Nathan J. Mcneese,

Allyson Hauptman,

Rui ZhangAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 8, Issue CSCW1

Article No.: 49, Pages 1 - 32

https://doi.org/10.1145/3637326

Published: 26 April 2024 Publication History

Abstract

In the coming years, Artificial Intelligence (AI) will be applied as a teammate that works alongside and collaborates with humans. Prior research in teaming and CSCW has shown that teammates have the ability to change the thoughts and behaviors of each other through simple interactions in a process known as social influence. However, to date, research has yet to identify the social influence that AI teammates could have in these human-AI teams, which has led to a limited understanding of how AI teammates will change the behaviors of their human teammates. To remedy this gap, we conduct a mixed-methods study (N=33) with young individuals to explore how humans could behaviorally adapt and perceive their behavioral adaptation due to interaction with an AI teammate. Qualitative results report that perceived three unique stages they had to experience for the social influence of their AI teammate to lead to adaptation (i.e., perceiving a sense of control, identifying a technological or performative justification, and gaining first-hand experience). Quantitative results validate and illustrate the results of this perceived process, as results show that participants adapted their behaviors to complement the behaviors of different types of AI teammates. This study contributes to the CSCW/HCI field by developing an initial understanding of AI teammates' social influence in human-AI teams, which will be a pivotal design and research consideration in future efforts.

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Acosta-Enriquez BGuzmán Valle MArbulú Ballesteros MArbulú Castillo JArbulu Perez Vargas CTorres ISilva León PSaavedra Tirado K(2025)What is the influence of psychosocial factors on artificial intelligence appropriation in college students?BMC Psychology10.1186/s40359-024-02328-x13:1Online publication date: 4-Jan-2025
https://doi.org/10.1186/s40359-024-02328-x
Duan WZhou SScalia MFreeman GGorman JTolston MMcNeese NFunke G(2025)Understanding the processes of trust and distrust contagion in Human–AI Teams: A qualitative approachComputers in Human Behavior10.1016/j.chb.2025.108560165(108560)Online publication date: Apr-2025
https://doi.org/10.1016/j.chb.2025.108560
Flathmann CSchelble BGaleano A(2024)Empirical Impacts of Independent and Collaborative Training on Task Performance and Improvement in Human-AI TeamsProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/1071181324127442568:1(1447-1453)Online publication date: 13-Aug-2024
https://doi.org/10.1177/10711813241274425
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Index Terms

Empirically Understanding the Potential Impacts and Process of Social Influence in Human-AI Teams
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing devices
  2. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 8, Issue CSCW1

CSCW

April 2024

6294 pages

EISSN:2573-0142

DOI:10.1145/3661497

Editor:
Jeff Nichols
Apple Inc., United States

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Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 26 April 2024

Published in PACMHCI Volume 8, Issue CSCW1

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Cited By

Acosta-Enriquez BGuzmán Valle MArbulú Ballesteros MArbulú Castillo JArbulu Perez Vargas CTorres ISilva León PSaavedra Tirado K(2025)What is the influence of psychosocial factors on artificial intelligence appropriation in college students?BMC Psychology10.1186/s40359-024-02328-x13:1Online publication date: 4-Jan-2025
https://doi.org/10.1186/s40359-024-02328-x
Duan WZhou SScalia MFreeman GGorman JTolston MMcNeese NFunke G(2025)Understanding the processes of trust and distrust contagion in Human–AI Teams: A qualitative approachComputers in Human Behavior10.1016/j.chb.2025.108560165(108560)Online publication date: Apr-2025
https://doi.org/10.1016/j.chb.2025.108560
Flathmann CSchelble BGaleano A(2024)Empirical Impacts of Independent and Collaborative Training on Task Performance and Improvement in Human-AI TeamsProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/1071181324127442568:1(1447-1453)Online publication date: 13-Aug-2024
https://doi.org/10.1177/10711813241274425
Duan WWeng NScalia MZhang RTuttle JYin XZhou SFreeman GGorman JFunke GTolston MMcNeese N(2024)Getting Along With Autonomous Teammates: Understanding the Socio-Emotional and Teaming Aspects of Trust in Human-Autonomy TeamsProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/1071181324127212368:1(1531-1536)Online publication date: 21-Oct-2024
https://doi.org/10.1177/10711813241272123
Mullins TNecaise AFiore SAmon M(2024)Navigating Trust: The Interplay of Trust in Automation and Team Communication in an Extended Simulated Military MissionProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/1071181324126299168:1(305-309)Online publication date: 29-Aug-2024
https://doi.org/10.1177/10711813241262991
Mallick RFlathmann CDuan WSchelble BMcNeese N(2024)What you say vs what you do: Utilizing positive emotional expressions to relay AI teammate intent within human–AI teamsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103355192(103355)Online publication date: Dec-2024
https://doi.org/10.1016/j.ijhcs.2024.103355

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