Trust plays a critical role in both effective teamwork and successful integration of AI into human-AI teams (HATs), which are becoming increasingly interconnected to tackle large-scale problems. As these HATs grow in complexity, so do the challenges of building, maintaining, calibrating, and aligning trust across HATs, as trust is dynamic, socially influenced, and capable of spreading and evolving over time. To understand the underexplored question of how trust and distrust can spread across HATs, we conducted a laboratory experiment with two interconnected HATs (N=40) distributed across the U.S., each comprising two humans and one AI collaborating over a remotely piloted aircraft system (RPAS) conducting simulated reconnaissance tasks. Our findings show that distrust toward the AI teammate spread more easily than trust, both within and across HATs. While intra-HAT (dis)trust in the AI developed through direct interaction, inter-HAT (dis)trust was more vulnerable to word-of-mouth distrust. Additionally, participants’ trust in the (dis)trust spreader, as well as their team-level trust, reflected in-group favoritism and out-group bias. This work presents the first empirical investigation into trust and distrust contagion across HATs, expanding the current understanding of trust dynamics in multi-agent and multi-team systems. It identifies potential mechanisms driving trust and distrust transfer between interconnected HATs and explores the individual, team, and multi-team level impacts. In doing so, we lay the groundwork for developing theories on trust contagion in multi-agent, multi-team systems, providing valuable insights for future research and design strategies to optimize human-AI collaboration in complex environments.

Human-Autonomy Teaming (HAT) is a multi-disciplinary domain with a diverse set of research needs and goals stemming from fields such as computer science, robotics, and human factors. This melting pot of fields generates a unique challenge in that there exist many disjoint research methods (measures and tasks) that cause issues with knowledge transfer and comparison between researchers. One way to address this issue is by providing researchers with a testbed containing a standardized suite of analysis tools and tasks that allow direct comparison between different approaches. Therefore, this study attempts to bring the HAT community together in a collaborative discussion to collect and organize their research needs for the future development of these testbeds. Specifically, through thematic analysis, our work reveals three emergent prongs that underpin testbed needs of HAT experts: task, AI, and technical requirements. Also, we organize our thematic analysis by priority to suggest possible paths for HAT testbed development to maximize its immediate and continued utility. Our research indicates that the HAT community places significant importance on both the pre-established, standardized functions available within the testbed and the freedom to tailor and develop their unique tasks or AI solutions.

The success of human-AI teams (HATs) requires humans to work with AI teammates in trustful ways over a certain time period. However, how trust evolves and changes dynamically in response to human-AI team interactions is generally understudied. This work explores the evolvement of trust in HATs over time by analyzing 45 participants' experiences of trust or distrust in an AI teammate prior to, during, and after collaborating with AI in a three-member HAT. Our findings highlight that humans' expectations of AI's ability, integrity, benevolence, and adaptability influence their initial trust in AI before collaboration. However, this initial trust can be maintained or revised through the development of situational trust during collaboration in response to the AI teammate's communication behaviors. Further, the trust developed through collaboration can impact individuals' subsequent expectations of AI's ability and their collaborations with AI. Our findings also reveal the similarities and differences in the temporal dimensions of trust for AI and human teammates. We contribute to CSCW community by offering one of the first empirical investigations into the dynamic and temporal dimension of trust evolvement in HATs. Our work yields insights into the pathways to expanding the methodological toolkit for investigating the development of trust in HATs, formulating theories of trust for the HAT context. These insights further inform the effective design of AI teammates and provide guidance on the timing, content, and methods for calibrating trust in future human-AI collaboration contexts.

This article considers human-autonomy teams (HATs) in which two human team members interact and collaborate with an autonomous teammate to achieve a common task while dealing with unexpected technological failures that were imposed either in automation or autonomy. A Wizard of Oz methodology is used to simulate the autonomous teammate. One of the critical aspects of HAT performance is the trust that develops over time as team members interact with each other in a dynamic task environment. For this reason, it is important to examine the dynamic nature of teammate trust through real-time measures of team interactions. This article examines team interaction and trust to understand better how they change under automation and autonomy failures. Thus, we address two research questions: 1) How does trust in HATs evolve over time?; and 2) How is the relationship between team interaction and trust impacted by the failures? We hypothesize that trust in HATs will decrease as autonomy failures increase. We also hypothesize that team interaction would be related to the development of trust and recovery from the failures. The results implicate three general trends: 1) team interaction dynamics are linked to the development of trust in HATs; 2) trust in the autonomous teammate is only associated with recovery from autonomy failures; 3) team interaction dynamics are related to both automation and autonomy failure recovery.