Trust is a very popular concept in Human–Robot Interaction (HRI) to explain why and how people interact with robots. However, the definition of trust and the methods used to study the concept vary widely, often leading to confusion instead of insight. In this position paper, we discuss possible reasons for the confusion and disagreement by reviewing theory and methods. Our main criticism is that HRI researchers have recently taken an oversimplified approach by not adhering to the process model of trust. Instead, we have primarily measured perceived trustworthiness (often as a proxy for trust) and assumed that it accurately predicts behavior or is satisfactory as an end goal. In addition, many experimental paradigms fail to account for the critical elements of risk and vulnerability that are essential for trust to guide behavior. With this position paper, we aim to shed light on these “trust issues” in HRI and to improve the HRI community’s approach by providing suggestions for enhancing the quality of future research.

Scales are commonly employed in Human–Robot Interaction (HRI) research, yet due to its multidisciplinary nature, many in this community lack direct training in psychometrics. This poses challenges for appropriate scale selection, accurate assessments of reliability and validity, and use. We provide a tutorial to empower researchers without scale development expertise to assess scale quality efficiently. We detail a guideline that provides high-level questions and examples to help the reader make confident evaluations of existing scales in HRI. The guideline is then used to evaluate the Godspeed and Robotic Social Attributes Scale (RoSAS). RoSAS is found to be adequately validated, whereas Godspeed warrants further investigation before it should be used in HRI contexts. The article concludes by offering advice on the use of custom scales and provides references for further enhancing expertise in this domain.

Affective states induced through robotic speech play a crucial role in communication between robots and humans. The embodiment hypothesis suggests that the perception of and behavior toward robots is heightened by their physical presence, yet little is known about how this embodiment influences affective responses in humans. Our laboratory study involved a depicted and an embodied robot to examine whether a robot's affective speech elicits corresponding arousal and emotional valence in humans. The findings revealed three key insights: First, positive and negative speech significantly increased arousal, with positive speech also resulting in a more positive emotional valence. Second, participants experienced higher arousal levels when interacting with an embodied robot compared to a depicted one. Third, there was a clear preference for future interactions with embodied robots over their depicted counterparts. These results emphasize the importance of both speech and embodiment in shaping human emotional responses in human-robot interactions.

There are currently no psychometrically valid tools to measure the perceived danger of robots. To fill this gap, we provided a definition of perceived danger and developed and validated a 12-item bifactor scale through four studies. An exploratory factor analysis revealed four subdimensions of perceived danger: affective states, physical vulnerability, ominousness, and cognitive readiness. A confirmatory factor analysis confirmed the bifactor model. We then compared the perceived danger scale to the Godspeed perceived safety scale and found that the perceived danger scale is a better predictor of empirical data. We also validated the scale in an in-person setting and found that the perceived danger scale is sensitive to robot speed manipulations, consistent with previous empirical findings. Results across experiments suggest that the perceived danger scale is reliable, valid, and an adequate predictor of both perceived safety and perceived danger in human-robot interaction contexts.

The Robotic Social Attributes Scale (RoSAS) is widely used in human-robot interaction research to measure the social perception of robots, including warmth, competence, and discomfort. As previous researchers have found ambiguous support for the RoSAS's three-factor structure, the current study aims to evaluate the proposed structure by conducting a confirmatory factor analysis (CFA) using openly available datasets. The CFA (n = 1107) showed that the three-factor model had a poor model fit. This suggests that the RoSAS's three dimensions might better be used as separate scales instead of measuring a broad concept of social perception. When separating by stimulus type, only stimuli using words and vignettes had an acceptable model fit, indicating that the RoSAS might be more suitable for word/vignette stimuli. We recommend using the RoSAS's individual subscales as separate constructs rather than measuring social attributes in general. This approach also aligns with what most research has already adopted.

Construct measurement scales are commonly employed in HRI research. We provide a half-day tutorial (4 hours) that aims to empower researchers with the tools to find appropriate scales for their research and assess the quality of those scales confidently and efficiently. There are no prerequisites required for attendees. We aim to recruit researchers interested in using scales but who lack confidence in evaluating their development. The first part of the tutorial will teach attendees how to assess the quality of HRI scales. To accomplish this, we will review basic topics in psychometric theory and a guideline (developed by the organizers) that outlines best practices in scale development and validation. In the second part, we will apply this guideline to two frequently used HRI scales: Godspeed and RoSAS. Attendees are also encouraged to bring scales they are interested in reviewing. The third part aims to help attendees find appropriate scales for their research. To accomplish this, we will debut a new HRI scale database we have developed. This database is the first centralized online repository of HRI scales and contains over 40 of the most used and cited HRI scales covering a wide array of topics of interest such as, trust, embodiment, safety, and attitudes towards robots. We will demonstrate how to access and use the information contained within the database. Our goal for this tutorial is to promote active engagement from attendees throughout the session, ultimately striving to improve the quality and replicability of results in HRI studies.

The perception of danger in HRI settings has become increasingly important as interactions between robots and humans become more commonplace. Previously, a perceived danger scale was developed and validated. Here, we shortened this scale to create the Perceived Danger-Short Form (PD-SF) scale. Experiment 1 used pre-existing data and standard procedures to shorten the scale from 12 items to 4. Experiment 2 validated the short form in a new experiment where participants observed images of robots holding kitchen items of varying levels of danger in close proximity to a human. PD-SF was able to capture differences across the kitchen items. Results from both experiments indicate that PD-SF is a reliable and psychometrically valid measure of perceived danger in HRI contexts.

AI-based decision support is increasingly implemented to support operators in dynamic control tasks. While these systems continuously improve, to truly achieve human–system synergy, one must also study humans’ system understanding and behavior. Accordingly, we investigated the impact of explainability instructions regarding a specific system weakness on performance and trust in two experiments (with higher task demands in Experiment 2). Participants performed a dynamic control task with support from either an explainable AI (XAI, information on a system weakness), a non-explainable AI (nonXAI, no information on system weakness), or without support (manual, only in Experiment 2). Results show that participants with XAI support outperformed those in the nonXAI group, particularly in situations where the AI actually erred. Notably, informing users of system weaknesses did not affect trust once they had interacted with the system. In addition, Experiment 2 showed the general benefit of decision support over working manually under higher task demands. These findings suggest that AI support can enhance performance in complex tasks and that providing information on potential system weaknesses aids in managing system errors and resource allocation without compromising trust. • Two experiments examined explainable AI in a dynamic supervisory control task. • One group of subjects was informed about AI weaknesses; i.e. where errors can occur. • Explainability improved performance, especially in cases where the AI erred. • Weakness information did not negatively affect trust in the AI after the interaction. • Explaining AI weaknesses enhanced human–AI collaboration without compromising trust.

In a growing number of application domains, human decision-making is being supported by automated systems. While previous research has focused extensively on the negative consequences of automation support in terms of an overuse of such systems, we argue that this focus has largely overlooked another crucial issue: Humans often deteriorate the performance of automation. Specifically, human–automation dyads commonly perform worse than the system alone because humans, in an attempt to improve decisions, unfortunately interfere with correct system recommendations. This problem will only grow as systems based on artificial intelligence (AI) become more reliable and the gap between human-only and system-only performance continues to widen. We therefore outline the need for research that addresses this persisting and increasingly relevant issue. One approach to counteract this problem is to make systems more transparent and give humans more information on the system. However, while numerous explainability approaches have been brought forward, only very few show convincing effects. To be truly useful, we argue that systems need to be explainable in terms of effective behavioral guidance. Furthermore, beyond just thinking about how to enable humans to better adapt to the system (as is the case with explainability approaches), systems should be more human-centric, taking into account human strengths and weaknesses, and ultimately adapting to humans to enable synergy between humans and AI.

While the use of robots in public spaces is increasing, still few studies explore the resulting everyday human-robot interactions (HRI). The present study sought to bridge the disparity between real-world interactions and the frequently examined hypothetical interactions. To do so, we investigate the imagined and actual interaction with an ice cream serving robot. In two studies and an exploratory study comparison, we examined user acceptance and preference for the degree of anthropomorphic appearance. Although a typical human service task was taken over by a robot, an industrial robot was preferred according to participants' ratings in both studies. Moreover, both studies demonstrated that robot enthusiasm significantly relates to participants' acceptance of the robot for the task. Besides these commonalities, the results showed also that while humans were preferred over robots in the imagined setting, no clear preference was found in the real-life setting. Additional analyses compared the free text answers of the two studies and provided insights into participants' general attitudes toward robots in the workforce. In line with the higher preferences for humans over robots in the imagined setting, considerably more participants mentioned a better customer experience with humans as important in the imagined study compared to the participants who interacted with the robot. The studies strikingly demonstrated that imaginary settings yield similar outcomes to those where participants physically engage with the robot in certain aspects, such as their preference for anthropomorphism. However, this phenomenon does not appear to hold for other facets, such as their favored service agent.

Anthropomorphism, as a design feature of robots, is widely applied to enhance human-robot interaction in the social domain. Current knowledge of how anthropomorphism influences perception, attitudes, and actual behavior is gained via research with depicted and embodied robots. However, it remains unclear how comparable gained insights of both approaches are. Results of a current meta-analysis suggest that anthropomorphism positively influences subjective and objective measures in case of embodied robots, whereas in case of depicted robots predominantly effects on subjective measures seem to emerge. This follow-up analysis aims to further investigate this difference by using a recoded dataset including data of 41 studies, involving over 3,000 participants. The results illustrate that anthropomorphism investigated via embodied robots indeed facilitates both subjective and objective outcomes. Remarkably, studies concerning effects of anthropomorphism using depicted robots showed positive effects on a subjective level but failed to show any effects on an objective level. In conclusion, the results show that the consequences of anthropomorphism in human-robot interaction depend on how robots are presented to participants. Moreover, they reveal that the transfer of results gained via depicted robots to embodied human-robot interaction might lead to both overestimation on a subjective level and underestimation on an objective level regarding the consequences of anthropomorphism.