Human Robot Team Design

Human-robot teams offer both benefits and new challenges. Human robot teams combine the advantages of automation such as high accuracy, speed, and repeat-ability with the flexibility, adaptability, and creative problem-solving commonly associated with humans. Several challenges, however, must first be addressed to effectively leverage such teams. One challenge is understanding effective human-robot team design (HRTD). HRTD is vital as the wrong team can lead to potentially negative outcomes. The theoretical model and methodology presented are the planned first steps towards the establishment of guidelines based on statistical models that can recommend an optimal human-robot team design based on a given set of criteria.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156399/1/Esterwood and Robert 2020b .pdfSEL

Pharmacy students’ interprofessional experience and performance in advanced pharmacy practice experience rotations amid COVID-19 pandemic

IntroductionInterprofessional education (IPE) is essential in pharmacy training, providing students with vital collaborative skills for real-world healthcare. Advanced Pharmacy Practice Experience (APPE) is integral to IPE, allowing students to apply their knowledge in diverse healthcare settings. The COVID-19 pandemic has disrupted healthcare education and raised concerns about its impact on IPE during APPE rotations. Our study investigates the pandemic’s influence on pharmacy students’ interprofessional interactions and APPE performance.ObjectiveTo assess the interprofessional experiences of fourth-year pharmacy students before and during the COVID-19 pandemic in the context of APPE.MethodsThis retrospective observational study examined the experiences of P4 pharmacy students in the United States during APPEs before and during the pandemic. We employed electronic surveys with 21 questions to gauge interactions and interprofessional team effectiveness, employing Likert scale response options. We compared responses between the 2019–2020 and 2020–2021 APPE rotations using statistical tests.ResultsOur study encompassed 83 and 86 students for the 2019–2020 and 2020–2021 APPE rotations, respectively, achieving a 100% response rate. Amid the pandemic, written communications between pharmacy students and healthcare providers in general medicine rotations increased, while in-person engagement decreased. Pre-COVID, students reported higher colleague referrals and greater interprofessional utilization during ambulatory care rotations.ConclusionCOVID-19 shifted interactions from in-person to written communication between pharmacy students and healthcare providers. Students reported decreased satisfaction with their interprofessional experiences. This research offers insights into the changing landscape of pharmacy education, helping students prepare for evolving challenges in healthcare delivery and education

Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.University of Michigan Mcubed Grant: Virtual Prototyping of Human-Robot Collaboration in Unstructured Construction EnvironmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/1/You et al. forthcoming in AutCon.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/4/You et al. 2018.pdfDescription of You et al. 2018.pdf : Published Versio

Designing for Appropriate Reliance: The Roles of AI Uncertainty Presentation, Initial User Decision, and User Demographics in AI-Assisted Decision-Making

Appropriate reliance is critical to achieving synergistic human-AI collaboration. For instance, when users over-rely on AI assistance, their human-AI team performance is bounded by the model's capability. This work studies how the presentation of model uncertainty may steer users' decision-making toward fostering appropriate reliance. Our results demonstrate that showing the calibrated model uncertainty alone is inadequate. Rather, calibrating model uncertainty and presenting it in a frequency format allow users to adjust their reliance accordingly and help reduce the effect of confirmation bias on their decisions. Furthermore, the critical nature of our skin cancer screening task skews participants' judgment, causing their reliance to vary depending on their initial decision. Additionally, step-wise multiple regression analyses revealed how user demographics such as age and familiarity with probability and statistics influence human-AI collaborative decision-making. We discuss the potential for model uncertainty presentation, initial user decision, and user demographics to be incorporated in designing personalized AI aids for appropriate reliance.Comment: Accepted to CSCW202

Understanding Differences in Social Learning

Previous research has shown that individuals with autism spectrum disorder (ASD) appear to learn from social and non-social rewards at different rates compared to typically developing individuals. Several hypotheses have been developed to explain these differences, including the social motivation hypothesis, the weak central coherence hypothesis and hypotheses related to probabilistic learning ability. However, in all cases, the literature shows only mixed support for these ideas. This dissertation focuses on identifying which assumptions from these hypotheses replicate and what replication successes and failures mean for the study of autism-spectrum traits within the general population. This work takes a “spectrum” approach to autism that assumes ASD-related traits occur on a scale continuum. It therefore is designed to test the central predictions of each of these hypotheses amongst participants sampled from the general population. The use of general population samples confers the considerable advantage of allowing adequate statistical power for hypothesis tests. In addition to these hypotheses, this dissertation explores how social behavior and interaction outcomes relate to ASD-traits and task outcomes. Interestingly, results ran contrary to many of the previous findings in the literature. Despite evidence of associations within the general population and ASD-traits, I failed to find clear associations between ASD-traits and predictions made by the Social Motivation Hypothesis, the Weak Central Coherence Hypothesis or hypotheses related to probabilistic learning ability. Despite these results, data on real social behavior and social outcomes did vary as a function of ASD-relevant traits. Specifically, the interaction partners of individuals who reported higher levels of ASD-traits experienced them as less likable and reported worse interaction quality. Additionally, individuals reporting higher levels of ASD-related traits were less expressive than those reporting fewer traits. Overall, while predictions about ASD-traits and cognitive/motivational processes did not appear to replicate within the general population, ASD-traits do appear to be related to real-life social behavior and interaction outcomes associated. Together, these findings document subtle social behavior differences associated with ASD traits in the absence of social cognitive differences and suggest that major theories of autism may not sufficiently explain the causes of altered social behavior in those with autism-spectrum conditions

Social Cues as Digital Nudges in Information Systems Usage Contexts

Analysing human cognition and decision-making has become highly relevant in information systems (IS) research. Yet, although the notion of cognitive biases has been studied for more than 40 years in psychology and other related fields, IS researchers have only recently expressed explicit interest in this phenomenon. Even more nascent is the IS stream that emphasizes the usage and understanding of biases in the favor of humanistic outcomes (e.g., the well-being of individuals) beyond previous scientific endeavors to pursue instrumental goals (e.g., the profit of companies). This fact is reflected in the recent emergence and call for digital nudges - influences that rely on heuristics and biases to guide individuals to beneficial decisions through modest adjustments of the digital choice environments. To advance the emergent research in this field, this thesis targets one of the major bias categories: the social bias (i.e., systematic errors that result from an individual’s interpretation of social cues). Within four articles, the thesis addresses the role of social cues as digital nudges in various IS usage contexts. The first two articles investigate how directly-traceable social cues can overcome service adoption hurdles: Precisely, the first article investigates how employing a verbal (i.e., platform self-disclosure) and a nonverbal social cue (i.e., message interactivity) in a conversational agent (i.e., chatbot) influence users to voluntary self-disclose private information (i.e., e-mail addresses). Moreover, the results revealed that the analysed social cues do not have individual effects, but in fact boost each other through their interaction. The second article deals with the application of various directly-traceable social cues (e.g., pictures of human avatars) as well as the role of personalized recommendations in financial advisory services to improve investors’ financial well-being. The results demonstrate that not only directly-traceable social cues but also recommendations can increase a user’s perceived social presence during the interaction, which in turn influences potential investors to invest higher amounts. The third article continues with recommendations as social cues, yet analyses them from an indirectly-traceable perspective and is devoted to investigating whether the source of the recommendation (i.e., seller or other customers) influences the acceptance of the recommendation in augmented reality applications to help customers in finding the best product for their needs. The findings indicate that customer recommendations reduce a customer’s perceived fit uncertainty of a product, resulting in a higher intention to purchase of a product that previous customers recommended. However, customers refrain from adhering to an automatically-generated recommendation despite recent technological advances that may provide more personalized and thus more suitable recommendations than generic customer recommendations. The fourth and last article examines the impact of displaying sold-out products on campaign success in reward-based crowdfunding. The valuable information indicate how potential backers make use of displayed sold-out product as social cues to derive information for their decision-making from previous backing behavior. In addition, the findings also showed that sold-out products do not have an impact on their own, however, their effect is also influenced by other factors in the environment, namely discount amount and the number of backers (i.e., another social cue). Thus, the article provides learnings for project creators on the design of reward option menus. Overall, this thesis showcases the variety and importance of social cues in numerous applications and is, therefore, to be understood as a first approach to expanding the understudied research field. Furthermore, the results enrich previous research and elucidate various underlying explanatory mechanisms of how and why biased decision-making takes place and how these mechanisms may be used to nudge users in directions beneficial for them and for the employer of these nudges. The overarching contributions of this thesis for research consists of (1) investigating the existence and effects of various social cues on user decision-making, and (2) probing social cues in several IS usage contexts with their unique circumstances and influences, not only in a vacuum but also in conjunction with other interacting variables. Additionally, this thesis provides interesting and sometimes even counterintuitive recommendations as well as actionable and generalizable guidelines on social cues that practitioners can easily apply to various contexts