Anthropomorphic framing and failure comprehensibility influence different facets of trust towards industrial robots

Introduction: Utilizing anthropomorphic features in industrial robots is a prevalent strategy aimed at enhancing their perception as collaborative team partners and promoting increased tolerance for failures. Nevertheless, recent research highlights the presence of potential drawbacks associated with this approach. It is still widely unknown, how anthropomorphic framing influences the dynamics of trust especially, in context of different failure experiences. Method: The current laboratory study wanted to close this research gap. To do so, fifty-one participants interacted with a robot that was either anthropomorphically or technically framed. In addition, each robot produced either a comprehensible or an incomprehensible failure. Results: The analysis revealed no differences in general trust towards the technically and anthropomorphically framed robot. Nevertheless, the anthropomorphic robot was perceived as more transparent than the technical robot. Furthermore, the robot's purpose was perceived as more positive after experiencing a comprehensible failure. Discussion: The perceived higher transparency of anthropomorphically framed robots might be a double-edged sword, as the actual transparency did not differ between both conditions. In general, the results show that it is essential to consider trust multi-dimensionally, as a uni-dimensional approach which is often focused on performance might overshadow important facets of trust like transparency and purpose.

The (Im)perfect Automation Schema: Who Is Trusted More, Automated or Human Decision Support?

OBJECTIVE

This study's purpose was to better understand the dynamics of trust attitude and behavior in human-agent interaction.

BACKGROUND

Whereas past research provided evidence for a perfect automation schema, more recent research has provided contradictory evidence.

METHOD

To disentangle these conflicting findings, we conducted an online experiment using a simulated medical X-ray task. We manipulated the framing of support agents (i.e., artificial intelligence (AI) versus expert versus novice) between-subjects and failure experience (i.e., perfect support, imperfect support, back-to-perfect support) within subjects. Trust attitude and behavior as well as perceived reliability served as dependent variables.

RESULTS

Trust attitude and perceived reliability were higher for the human expert than for the AI than for the human novice. Moreover, the results showed the typical pattern of trust formation, dissolution, and restoration for trust attitude and behavior as well as perceived reliability. Forgiveness after failure experience did not differ between agents.

CONCLUSION

The results strongly imply the existence of an imperfect automation schema. This illustrates the need to consider agent expertise for human-agent interaction.

APPLICATION

When replacing human experts with AI as support agents, the challenge of lower trust attitude towards the novel agent might arise.

Feeling with a robot-the role of anthropomorphism by design and the tendency to anthropomorphize in human-robot interaction

The implementation of anthropomorphic features in regard to appearance and framing is widely supposed to increase empathy towards robots. However, recent research used mainly tasks that are rather atypical for daily human-robot interactions like sacrificing or destroying robots. The scope of the current study was to investigate the influence of anthropomorphism by design on empathy and empathic behavior in a more realistic, collaborative scenario. In this online experiment, participants collaborated either with an anthropomorphic or a technical looking robot and received either an anthropomorphic or a technical description of the respective robot. After the task completion, we investigated situational empathy by displaying a choice-scenario in which participants needed to decide whether they wanted to act empathically towards the robot (sign a petition or a guestbook for the robot) or non empathically (leave the experiment). Subsequently, the perception of and empathy towards the robot was assessed. The results revealed no significant influence of anthropomorphism on empathy and participants' empathic behavior. However, an exploratory follow-up analysis indicates that the individual tendency to anthropomorphize might be crucial for empathy. This result strongly supports the importance to consider individual difference in human-robot interaction. Based on the exploratory analysis, we propose six items to be further investigated as empathy questionnaire in HRI.

(Hu)man-Like Robots: The Impact of Anthropomorphism and Language on Perceived Robot Gender

Implementing anthropomorphic features to robots is a frequently used approach to create positive perceptions in human-robot interaction. However, anthropomorphism does not always lead to positive consequences and might trigger a more gendered perception of robots. More precisely, anthropomorphic features of robots seem to evoke a male-robot bias. Yet, it is unclear if this bias is induced via a male appearance of higher anthropomorphic robots, a general male-technology bias, or even due to language aspects. As the word robot is differently grammatically gendered in different languages, this might be associated with the representation of robot gender. To target these open questions, we investigated how the degree of anthropomorphism and the way the word robot is gendered in different languages, as well as within one language influence the perceived gender of the robot. We therefore conducted two online-studies in which participants were presented with pictures of differently anthropomorphic robots. The first study investigated two different samples from which one was conducted in German, as grammatically-gendered language, and one in English as natural gender language. We did not find significant differences between both languages. Robots with a higher degree of anthropomorphism were perceived as significantly more male than neutral or female. The second study investigated the effect of grammatically-gendered descriptions (feminine, masculine, neuter) on the perception of robots. This study revealed that masculine grammatical gender tends to reinforce a male ascription of gender-neutral robots. The results suggest that the male-robot bias found in previous studies seems to be associated with appearance of most anthropomorphic robots, and the grammatical gender the robot is referenced by.

Embodiment Matters in Social HRI Research: Effectiveness of Anthropomorphism on Subjective and Objective Outcomes

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 both, research with depicted and with 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 data set 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.

Challenging presumed technological superiority when working with (artificial) colleagues

Technological advancements are ubiquitously supporting or even replacing humans in all areas of life, bringing the potential for human-technology symbiosis but also novel challenges. To address these challenges, we conducted three experiments in different task contexts ranging from loan assignment over X-Ray evaluation to process industry. Specifically, we investigated the impact of support agent (artificial intelligence, decision support system, or human) and failure experience (one vs. none) on trust-related aspects of human-agent interaction. This included not only the subjective evaluation of the respective agent in terms of trust, reliability, and responsibility, when working together, but also a change in perspective to the willingness to be assessed oneself by the agent. In contrast to a presumed technological superiority, we show a general advantage with regard to trust and responsibility of human support over both technical support systems (i.e., artificial intelligence and decision support system), regardless of task context from the collaborative perspective. This effect reversed to a preference for technical systems when switching the perspective to being assessed. These findings illustrate an imperfect automation schema from the perspective of the advice-taker and demonstrate the importance of perspective when working with or being assessed by machine intelligence.

Why Context Matters: The Influence of Application Domain on Preferred Degree of Anthropomorphism and Gender Attribution in Human–Robot Interaction

The application of anthropomorphic design features is widely believed to facilitate human–robot interaction. However, the preference for robots’ anthropomorphism is highly context sensitive, as different application domains induce different expectations towards robots. In this study the influence of application domain on the preferred degree of anthropomorphism is examined. Moreover, as anthropomorphic design can reinforce existing gender stereotypes of different work domains, gender associations were investigated. Therefore, participants received different context descriptions and subsequently selected and named one robot out of differently anthropomorphic robots in an online survey. The results indicate that lower degrees of anthropomorphism are preferred in the industrial domain and higher degrees of anthropomorphism in the social domain, whereas no clear preference was found in the service domain. Unexpectedly, mainly functional names were ascribed to the robots and if human names were chosen, male names were given more frequently than female names even in the social domain. The results support the assumption that the preferred degree of anthropomorphism depends on the context. Hence, the sociability of a domain might determine to what extent anthropomorphic design features are suitable. Furthermore, the results indicate that robots are overall associated more functional, than gendered (and if gendered then masculine). Therefore, the design features of robots should enhance functionalities, rather than specific gendered anthropomorphic attributes to avoid stereotypes and not further reinforce the association of masculinity and technology.

Trust Me, I’m Not a Doctor! Determinants of Laypersons’ Trust in Medical Decision Aids: Experimental Study (Preprint)

Background

Objective

Methods

Results

Conclusions

Trial Registration

A meta-analysis on the effectiveness of anthropomorphism in human-robot interaction

The application of anthropomorphic design features is widely assumed to facilitate human-robot interaction (HRI). However, a considerable number of study results point in the opposite direction. There is currently no comprehensive common ground on the circumstances under which anthropomorphism promotes interaction with robots. Our meta-analysis aims to close this gap. A total of 4856 abstracts were scanned. After an extensive evaluation, 78 studies involving around 6000 participants and 187 effect sizes were included in this meta-analysis. The majority of the studies addressed effects on perceptual aspects of robots. In addition, effects on attitudinal, affective, and behavioral aspects were also investigated. Overall, a medium positive effect size was found, indicating a beneficial effect of anthropomorphic design features on human-related outcomes. However, closer scrutiny of the lowest variable level revealed no positive effect for perceived safety, empathy, and task performance. Moreover, the analysis suggests that positive effects of anthropomorphism depend heavily on various moderators. For example, anthropomorphism was in contrast to other fields of application, constantly facilitating social HRI. The results of this analysis provide insights into how design features can be used to improve the quality of HRI. Moreover, they reveal areas in which more research is needed before any clear conclusions about the effects of anthropomorphic robot design can be drawn.

A Taxonomy to Structure and Analyze Human–Robot Interaction

Robotic systems are one of the core technologies that will shape our future. Robots already change our private and professional life by working together with humans in various domains. Evoked by this increasing trend, great variability exists in terms of robots and interaction scenarios. This has boosted research regarding shaping factors of human–robot interaction (HRI). Nevertheless, this variety hinders the comparability and the generalizability of insights. What is needed for efficient research is a structured approach that allows the analysis of superordinate attributes, making previous HRI research comparable, revealing research gaps and thus guiding future research activities. Based on the review of previous HRI frameworks we developed a new HRI taxonomy that (1) takes into account the human, the robot, the interaction and the context of the HRI, (2) is applicable to various HRI scenarios and (3) provides predefined categories to enable structured comparisons of different HRI scenarios. A graphical representation of the taxonomy, including all possible classifications, eases the application to specific HRI scenarios. To demonstrate the use and value of this taxonomy, it is applied to different studies in HRI in order to identify possible reasons for contrasting results. The exemplified applications of the taxonomy underline its value as a basis for reviews and meta-analyses. Moreover, the taxonomy offers a framework for future HRI research as it offers guidance for systematic variations of distinctive variables in HRI.

The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception

Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of "Follow Me" robots influences how they are perceived by humans. To this end, a Follow Me robot was built and tested in a user study for a number of subjective variables. A total of 18 participants interacted with the robot, with the robot's lateral offset and distance varied in a within-subject design. After each interaction, participants were asked to rate the movement of the robot on the dimensions of comfort, expectancy conformity, human likeness, safety, trust, and unobtrusiveness. Results show that users generally prefer robot following distances in the social space, without a lateral offset. However, we found a main influence of affinity for technology, as those participants with a high affinity for technology preferred closer following distances than participants with low affinity for technology. The results of this study show the importance of user-adaptiveness in human-robot-interaction.

Anemia versus disease activity as cause of fatigue in rheumatoid arthritis

BACKGROUND

Fatigue is a common complaint in rheumatoid arthritis patients and contributes to loss of quality of life.

OBJECTIVE

To study the influence of hemoglobin levels and disease activity index upon fatigue in patients with rheumatoid arthritis (RA).

METHODS

We studied 130 RA patients for DAS 28, hemoglobin levels and fatigue as measured by FACIT F.

RESULTS

No association between fatigue with hemoglobin levels was observed. A positive association with DAS-28 was found. Decomposing DAS-28, no association could be detected with sedimentation rate but a positive correlation with analogical scale for general health, number of swollen and painful joints was found.

CONCLUSION

Although a positive association of fatigue with DAS-28 is found it appears that the most important items in connection with fatigue are swollen and tender joints as well as general health status. Hemoglobin levels were not related to fatigue in our patients.