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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. 

Abstract We present a wisdom of crowds study where participants are asked to order a small set of images based on the number of dots they contain and then to guess the respective number of dots in each image. We test two input elicitation interfaces—one elicits the two modalities of estimates jointly and the other independently. We show that the latter interface yields higher quality estimates, even though the multimodal estimates tend to be more self-contradictory. The inputs are aggregated via optimization and voting-rule based methods to estimate the true ordering of a larger universal set of images. We demonstrate that the quality of collective estimates from the simpler yet more computationally-efficient voting methods is comparable to that achieved by the more complex optimization model. Lastly, we find that using multiple modalities of estimates from one group yields better collective estimates compared to mixing numerical estimates from one group with the ordinal estimates from a different group. 

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. 

Construction inspection and monitoring are key activities in construction projects. Automation of inspection tasks can address existing limitations and inefficiencies of the manual process to enable systematic and consistent construction inspection. However, there is a lack of an in-depth understanding of the process of construction inspection and monitoring and the tasks and sequences involved to provide the basis for task delegation in a human-technology partnership. The purpose of this research is to study the conventional process of inspection and monitoring of construction work currently implemented in construction projects and to develop an alternative process using a quadruped robot as an inspector assistant to overcome the limitations of the conventional process. This paper explores the use of quadruped robots for construction inspection and monitoring with an emphasis on a human-robot teaming approach. Technical development and testing of the robotic technology are not in the scope of this study. The results indicate how inspector assistant quadruped robots can enable a human-technology partnership in future construction inspection and monitoring tasks. The research was conducted through on-site experiments and observations of inspectors during construction inspection and monitoring followed by a semi-structured interview to develop a process map of the conventional construction inspection and monitoring process. The study also includes on-site robot training and experiments with the inspectors to develop an alternative process map to depict future construction inspection and monitoring work with the use of an inspector assistant quadruped robot. Both the conventional and alternative process maps were validated through interview surveys with industry experts against four criteria including, completeness, accuracy, generalizability, and comprehensibility. The findings suggest that the developed process maps reflect existing and future construction inspection and monitoring work. 

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. 

Urban Search and Rescue (USAR) missions continue to benefit from the incorporation of human–robot teams (HRTs). USAR environments can be ambiguous, hazardous, and unstable. The integration of robot teammates into USAR missions has enabled human teammates to access areas of uncertainty, including hazardous locations. For HRTs to be effective, it is pertinent to understand the factors that influence team effectiveness, such as having shared goals, mutual understanding, and efficient communication. The purpose of our research is to determine how to (1) better establish human trust, (2) identify useful levels of robot transparency and robot explanations, (3) ensure situation awareness, and (4) encourage a bipartisan role amongst teammates. By implementing robot transparency and robot explanations, we found that the driving factors for effective HRTs rely on robot explanations that are context-driven and are readily available to the human teammate.