A Qualitative Analysis of Trust Dynamics in Human-Agent Teams (HATs)

Abstract

This study investigated how and why trust changes over time between differing levels of autonomy (LOA) for an intelligence, surveillance, and reconnaissance (ISR) task involving four unmanned aerial vehicles (UAV). Four LOAs were implemented in the study ranging from low to high (manual, advice, consent, veto). The analysis examined if trust increased, decreased, or stayed the same during transitions between two different LOAs. Through a thematic analysis, themes influencing the participant’s trust in agents were identified. Current findings suggest trust increases most when a participant moves between moderate LOAs. Specifically, moving from the condition in which the role of the agent is to provide advice, to the agent making decisions on their own with the consent of the human. Trust was also shown to increase when the human moved from the highest LOA (human merely vetoes) to the manual condition. The most prevalent themes influencing trust included perceived agent errors and false alarms.

From Selection to Design: Diverse Perspectives of Human Factors Issues in Unmanned Aerial Systems

“…They have also conducted research in the UAS domain related to human agent teams (HAT) such as examining how various levels of automation impact an operator’s trust, workload and performance (Rebensky et al, 2022). They are currently conducting research to examine the characteristics which influence an operator’s trust in a multi-agent HAT (Carmody et al, 2022; Nguyen, et al, 2022), including individual differences, system design and team characteristics. Included in this research is examination of how trust changes dynamically over time as a results of trust events (e.g., trust violation and repair events), how events associated with one agent impact an operator’s trust in other agents and the team as a whole, and how to effectively measure HAT trust (Ficke et al, 2022).…”

Complexity-Driven Trust Dynamics in Human–Robot Interactions: Insights from AI-Enhanced Collaborative Engagements

“…For instance, some research has focused on users' behaviors during unexpected events, delving deep into the dynamics of trust breakdown and restoration [40]. Others employ empirical methods to uncover varying patterns of dynamic trust as levels of robot autonomy change, and how these patterns shape human decision-making [41]. A different subset of research highlights the importance of nurturing and preserving dynamic trust when coordinating robots within the Internet of Things [42].…”