Adapt and overcome: Perceptions of adaptive autonomous agents for human-AI teaming

Hauptman, Allyson I.; Schelble, Beau G.; McNeese, Nathan J.; Madathil, Kapil Chalil

doi:10.1016/j.chb.2022.107451

Cited by 33 publications

(15 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, research has shown that humans hold high expectations for ATs powered by AI, believing they should demonstrate human-like behaviors and capabilities (Flathmann et al, 2021;Zhang et al, 2021). This demand requires ATs to possess the ability to communicate and coordinate with teammate with shared understanding, contribute to shared goals and outcomes, and demonstrate interpersonal and situational awareness (Hauptman et al, 2022;Schelble, Flathmann, McNeese, O'Neill, et al, 2022;Zhang et al, 2021), each of which contribute to the development and sustainment of trust.…”

Section: Human-ai Teamingmentioning

confidence: 99%

Introduction to the Minitrack on Collaboration in Online Communities: Information Processing and Decision Making

Jensen,

Meservy,

Fadel

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

This study improves the understanding of trust in human-AI teams by investigating the relationship of AI teammate ethicality on individual outcomes of trust (i.e., monitoring, confidence, fear) in AI teammates and human teammates over time. Specifically, a synthetic task environment was built to support a three-person team with two human teammates and one AI teammate (simulated by a confederate). The AI teammate performed either an ethical or unethical action in three missions, and measures of trust in the human and AI teammate were taken after each mission. Results from the study revealed that unethical actions by the AT had a significant effect on nearly all of the outcomes of trust measured and that levels of trust were dynamic over time for both the AI and human teammate, with the AI teammate recovering trust in Mission 1 levels by Mission 3. AI ethicality was mostly unrelated to participants' trust in their fellow human teammates but did decrease perceptions of fear, paranoia, and skepticism in them, and trust in the human and AI teammate was not significantly related to individual performance outcomes, which both diverge from previous trust research in human-AI teams utilizing competency-based trust violations.

show abstract

Section: Human-ai Teamingmentioning

confidence: 99%

Introduction to the Minitrack on Collaboration in Online Communities: Information Processing and Decision Making

Jensen,

Meservy,

Fadel

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Schelble et al [9] explore dimensions of ethics in human-AI collaboration and try to explore the effect of trust-repair strategies on trust and team performance. Hauptman et al [2] propose design suggestions for the progress of adaptive autonomous teammates. Despite an increasing focus on design strategies, little research has fully investigated the importance of human feeling in human-AI collaboration.…”

Section: Human-ai Collaborationmentioning

confidence: 99%

“…Second, this study lays the groundwork for future research into relationships in human-AI collaboration. Previous studies have emphasized the design strategies of human-AI teamwork [2,12], while this study endeavors to understand the relationships generated during human-AI interaction. Few studies have attempted to comprehensively assess the factors that may influence the adoption and performance of human-AI collaboration.…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

“…The trend that training cost becomes lower while training time becomes faster facilitates the adoption of AI technologies in the commercial area. Against this background, exponential growth in the AI area facilitates the interaction between humans and AI agents [2]. Human-AI collaboration in which AI agents work as interdependent teammates to cooperate with humans toward common goals has become an irreversible trend [3].…”

Section: Introductionmentioning

confidence: 99%

“…Human-AI collaboration is a major area of interest within the field of management because it will enable decision-makers to perform meaningful actions to advance AI technologies [1,2]. There have been studies investigating human-AI collaboration in different contexts, such as data science [4] and piloting [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Trust in Human–AI Collaboration in the Context of Multiplayer Online Games

Hou

Cai

2023

Systems

View full text Add to dashboard Cite

Human–AI collaboration has attracted interest from both scholars and practitioners. However, the relationships in human–AI teamwork have not been fully investigated. This study aims to research the influencing factors of trust in AI teammates and the intention to cooperate with AI teammates. We conducted an empirical study by developing a research model of human–AI collaboration. The model presents the influencing mechanisms of interactive characteristics (i.e., perceived anthropomorphism, perceived rapport, and perceived enjoyment), environmental characteristics (i.e., peer influence and facilitating conditions), and personal characteristics (i.e., self-efficacy) on trust in teammates and cooperative intention. A total of 423 valid surveys were collected to test the research model and hypothesized relationships. The results show that perceived rapport, perceived enjoyment, peer influence, facilitating conditions, and self-efficacy positively affect trust in AI teammates. Moreover, self-efficacy and trust positively relate to the intention to cooperate with AI teammates. This study contributes to the teamwork and human–AI collaboration literature by investigating different antecedents of the trust relationship and cooperative intention.

show abstract

Understanding the influence of AI autonomy on AI explainability levels in human-AI teams using a mixed methods approach

Hauptman,

Schelble,

Duan

et al. 2024

Cogn Tech Work

View full text Add to dashboard Cite

An obstacle to effective teaming between humans and AI is the agent’s "black box" design. AI explanations have proven benefits, but few studies have explored the effects that explanations can have in a teaming environment with AI agents operating at heightened levels of autonomy. We conducted two complementary studies, an experiment and participatory design sessions, investigating the effect that varying levels of AI explainability and AI autonomy have on the participants’ perceived trust and competence of an AI teammate to address this research gap. The results of the experiment were counter-intuitive, where the participants actually perceived the lower explainability agent as both more trustworthy and more competent. The participatory design sessions further revealed how a team’s need to know influences when and what teammates need explained from AI teammates. Based on these findings, several design recommendations were developed for the HCI community to guide how AI teammates should share decision information with their human counterparts considering the careful balance between trust and competence in human-AI teams.

show abstract

Adapt and overcome: Perceptions of adaptive autonomous agents for human-AI teaming

Cited by 33 publications

References 34 publications

Introduction to the Minitrack on Collaboration in Online Communities: Information Processing and Decision Making

Introduction to the Minitrack on Collaboration in Online Communities: Information Processing and Decision Making

Exploring Trust in Human–AI Collaboration in the Context of Multiplayer Online Games

Understanding the influence of AI autonomy on AI explainability levels in human-AI teams using a mixed methods approach

Contact Info

Product

Resources

About