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Demographic data pertain to people’s identities and behaviors. Analyses of demographic data are used to describe patterns and predict behaviors, to inform interface design, and even institutional decision-making processes. Demographic data thus need to be complete and correct to ensure they can be analyzed in ways that reflect reality. This study consists of interviews with 40 people in STEM and addresses how causes of relational (dis)trust in demographic data collection contribute to pervasive problems of missing and incorrect responses and disobliging responses (e.g., non-disclosure, false responses, attrition, and hesitancy to use services). The findings then guide a preliminary set of recommendations for cultivating trustworthiness based on recent developments in trust theory and designing for responsive and trustworthy systems. Specifically, we explore how demographic questionnaire design (e.g., item construction and instructions) can communicate necessary reassurances and transparency for users. The ongoing research provides interview-based recommendations for improving the quality and completeness of demographic data collection. This research adds to other recommendations on improving demographic questionnaires. 

This paper discusses three points inspired by Skraaning and Jamieson’s perspective on automation failure: (a) the limitations of the automation failure concept with expanding system boundaries; (b) parallels between the failure to grasp automation failure and the failure to grasp trust in automation; (c) benefits of taking a pluralistic approach to definitions in sociotechnical systems science. While a taxonomy of automation-involved failures may not directly improve our understanding of how to prevent those failures, it could be instrumental for identifying hazards during test and evaluation of operational systems. 

Virtual humans are on-screen characters that are often embedded in learning technologies to deliver educational content. Little research has investigated how virtual humans can be used to correct common misconceptions. In this study, we explored how different types of narrative structures, refutation text and expository text, influence perceptions of trust, credibility, and learning outcomes. In addition, we conducted exploratory analyses examining how different measures of trust and credibility are related to each other and how these measures may mediate learning outcomes. Results showed that the type of narrative used did not influence any measure. However, the trust and credibility measures, while related to one another, were measurably distinct. In addition, only perceptions of message trust were significantly related to learning. Perceptions of message trust did not mediate learning outcomes, but were significantly predictive of learning at nearly the same effect as prior knowledge. 

Understanding how people trust autonomous systems is crucial to achieving better performance and safety in human-autonomy teaming. Trust in automation is a rich and complex process that has given rise to numerous measures and approaches aimed at comprehending and examining it. Although researchers have been developing models for understanding the dynamics of trust in automation for several decades, these models are primarily conceptual and often involve components that are difficult to measure. Mathematical models have emerged as powerful tools for gaining insightful knowledge about the dynamic processes of trust in automation. This paper provides an overview of various mathematical modeling approaches, their limitations, feasibility, and generalizability for trust dynamics in human-automation interaction contexts. Furthermore, this study proposes a novel and dynamic approach to model trust in automation, emphasizing the importance of incorporating different timescales into measurable components. Due to the complex nature of trust in automation, it is also suggested to combine machine learning and dynamic modeling approaches, as well as incorporating physiological data. 

Trust plays a critical role in the success of human-robot teams (HRTs). While typically studied as a perceptual attitude, trust also encompasses individual dispositions and interactive behaviors like compliance. Anthropomorphism, the attribution of human-like qualities to robots, is a related phenomenon that designers often leverage to positively influence trust. However, the relationship of anthropomorphism to perceptual, dispositional, and behavioral trust is not fully understood. This study explores how anthropomorphism moderates these relationships in a virtual urban search and rescue HRT scenario. Our findings indicate that the moderating effects of anthropomorphism depend on how a robot’s recommendations and its confidence in them are communicated through text and graphical information. These results highlight the complexity of the relationships between anthropomorphism, trust, and the social conveyance of information in designing for safe and effective human-robot teaming. 

Virtual testbeds are fundamental to the success of research on cognitive work in safety-critical domains. A testbed that can meet researchers' objectives and create a sense of reality for participants positively impacts the research process; they have the potential to allow researchers to address questions not achievable in physical environments. This paper discusses the development of a synthetic task environment (STE) for Urban Search and Rescue (USAR) to advance the boundaries of Human-Robot Teams (HRTs) using Roblox. Virtual testbeds can simulate USAR task environments and HRT interactions. After assessing alternative STE platforms, we discovered Roblox not only met our research capabilities but also would prove invaluable for research teams without substantial coding experience. This paper outlines the design process of creating an STE to meet our research team's objectives.