With the rising prevalence of social robots in public spaces, an increasingly heterogeneous audience of people from across different characteristics (such as age or ethnicity) are possible users. In such diverse interactions, there is a risk of algorithmic bias, which results in a discrimination of certain user groups. Human-robot interaction (HRI) research has thus far predominantly focused on homogeneous samples to describe instances and consequences of algorithmic bias, which is prone to leading to evasive findings. We address this gap by conducting thirteen focus group interviews with a total of N = 92 participants and exploring if and how people have already experienced algorithmic bias in their daily lives. Additionally, we contrast the findings from our socially diversified sample with those obtained from a more homogeneous group of people. Our findings uncover various experiences and coping mechanisms regarding algorithmic bias and demonstrate that examining a more diverse sample reveals findings that would otherwise have remained unnoticed.

As robots become increasingly autonomous, they may evoke feelings of discomfort, particularly when users lack control over the interaction. One potential approach to mitigating these negative perceptions is granting users control over the robot (e.g., through an external interface for direct operation) and fostering a sense of psychological ownership (PO). We assume that control and ownership of a robot support the fulfillment of the Basic Psychological Needs—autonomy, competence, and relatedness—according to Self-Determination Theory, which play a key role in technology acceptance and a positive user experience. Additionally, we examine whether subjective perceptions of control and ownership differ from their objective counterparts and how these differences impact Basic Psychological Need fulfillment and robot perceptions. A between-subjects design laboratory study (N = 64) was conducted with three experimental conditions: (1) no control or ownership, where the robot autonomously completed a task (Wizard of Oz); (2) control, where participants directly operated the robot to perform the task; and (3) control and ownership, where participants controlled the robot and temporarily assumed ownership of it during the task. Results indicate that the combination of control and ownership significantly reduced feelings of discomfort towards the robot and enhanced the psychological need for participants competence. Moreover, subjective perceptions of control and ownership played a crucial role in confirming the hypothesized relationships with Basic Psychological Needs.

Transparent communication about how social robots collect and process personal data is essential, especially as their presence in public spaces continues to grow and their applications become more widespread. While previous research has primarily focused on the content and creation of explainable transparency, less attention has been given to which modalities robots should use to convey transparency in the first place. To close this gap, we examined different single modality approaches for communicating transparency and compared them to various combined modalities for transparency explanation, since these have the potential to convey information more efficiently through multiple channels. We conducted a virtual reality (VR) two-part laboratory experiment in which N = 106 participants interacted with a virtual Pepper robot and had to disclose personal data to it. The study design consisted of six conditions: a control group without transparency communication, two multimodal conditions where transparency declarations were presented through multiple channels, and three unimodal conditions where a single channel was used for transparency communication. The results show that the unimodal group was more effective than both the multimodal and control groups in delivering clear and understandable transparency declarations. This suggests that unimodal approaches to transparency may be the preferable option. This study provides insights into transparency declarations in HRI and offers key takeaways on how transparency can be communicated most effectively.

Looking at state-of-the-art robots, the majority have a white surface. This raises the questions of why robots are designed predominantly white, and how surface color affects the robot’s social perception. This not only reinforces human stereotypes but also influences the acceptance and usage of robots. Accordingly, this online study with a 2 (black vs. white) x 3 (Pepper robot vs. Temi robot vs. Kuka robot) within-subjects design (N = 100) investigated the effect on racist stereotypical perception and the role of anthropomorphism using implicit (Implicit Association Test) and explicit (Robotic Social Attributes Scale) measures and questionnaires on behavioral intentions. Our results show that robot color influences the implicit perception of robots but rarely affects explicit perception and behavior measures. Moreover, no effects of the robots’ anthropomorphism on racist perceptions were found. As more effects of anthropomorphism on our outcome variables were found than for robot color, we assume that robot color activates stereotypes in terms of color rather than race.

The deployment of social robots in public spaces has received increased interest over the past years. These robots need to process a wide array of personal data to offer services that are tailored to users’ requirements. While much research has been carried out regarding the creation of explainable content, little research has dealt with how data transparency - as a way to address uncertainty and concerns regarding the handling of personal data - is conveyed to users. To examine the impact of different transparency declarations on trust, performance, and robot perception, we conducted a virtual reality (VR) supported laboratory experiment with N = 53 participants who interacted with a robot in a public setting (a library). The interaction between users and robots was accompanied by information on the handling of users’ personal data using three different modalities (via posters, the robot’s tablet, or verbally). The results imply that, while all modalities are understandable and perceived as useful, there is no preference for any modality. Our findings contribute to HRI research by examining different modalities for transparency declarations, in an effort to foster understandable and transparent processing of data.

Conversational agents (CAs) lack of possibilities to enrich the interaction with emotional cues, although this makes the conversation more human-like and enhances user engagement. Thus, the potential of CAs is not fully exploit and possibilities to convey emotional cues are needed. In this work, CAs use colored ambient lights to display moral emotions during the interaction. To evaluate this approach, a between-subject lab experiment $(N=64)$ was conducted. Participants played a cooperation game with Amazon’s Alexa. Depending on the experimental condition participants received different light expressions: no light, neutral light, or morally emotional light (yellow = joy, blue = sorrow, red = anger matching the game decisions). The effect of the light expressions on the perception of the CA, users’ empathy and cooperation behavior was tested. Against our assumptions, the results indicated no positive effect of the emotional light cues. Limitations, next steps, and implications are discussed.

Studying in social isolation is a reality for many students that was further reinforced after the start of the COVID-19 pandemic. Research shows that isolation can lead to decreased learning efficiency and is intensified by the increased asynchronous online teaching during the pandemic. This change is not only challenging for students, but also for teachers, as students do not have a direct communication and feedback channel when learning content is presented in form of pre-recorded videos in a learning management system. In this paper, we present VGather2Learn Analytics, which is an extension to the already existing collaborative learning system VGather2Learn, which makes it possible for teachers to analyse the learning behavior of students in asynchronous video-teaching. The information presented in a dashboard will allow teachers to better understand how students interact while watching learning videos collaboratively and can improve online-teaching.

This paper examines how conversational agents (CAs) can communicate emotions non-verbally using light as communication modality. Therefore, we manipulated the CA Alexa to demonstrate emotions (joy and sorrow) using different light modalities (Echo Dot ring, a Hue lamp and the combination) with either a congruent verbal context (party or funeral) or no verbal context. In an online study 167 participants evaluated the perceived emotion of Alexa, their own emotional state as well as the perception of Alexa after watching a video with a user interacting with Alexa. Although the perceived emotions of Alexa were not affected by the experimental conditions, the results indicate that the perception of Alexa as well as the user’s emotion is affected by the displayed communication modality. As external light can be used to manipulate the users’ perception of CAs, the findings give relevant implications for the design of CAs.

The increase of social isolation amongst students due to remote teaching during the COVID-19 pandemic leads to decreased learning efficiency as well as extenuated satisfaction with students' learning experiences. In this paper we present an approach to tackle these problems by providing a tool that helps students organise collaborative learning with videos.

The present study investigated the role of age in the perception of emotional nonverbal behaviors of a virtual assistant in a 2 (seniors vs young participants) x 2 (happy vs sad situations) x 4 (three emotional nonverbal behaviors related to each situation vs neutral behavior) mixed factorial design. In the study, a virtual agent acted as an assistant to review the imaginary monthly schedule of the participant. After uttering each schedule, the agent showed different emotional nonverbal behaviors to express empathy. The stimulus materials were presented to 60 participants (30 elderly people and 30 younger adults) in 25 videos. All participants had to rate the agent's friendliness, intelligence, empathy, trustworthiness, and helpfulness immediately after watching each video. The results indicated that in the presence of the emotional nonverbal behaviors the elderly rated the agent as more empathic and trustworthy compared to the younger adults. The data also revealed that there were differences with respect to rating specific emotional nonverbal behaviors as more empathic than others. Elderly people perceived Dropping the Arms plus Sad Face, Head down, Sad Face, Head Nod plus Smile, and Smile as more empathic, and the nonverbal behaviors perceived as most empathic for the younger adults were Dropping the Arms plus Sad Face, and Head down.

First evidence suggests that introducing robots by means of a narrative story can lead to more positive interactions and evaluations [1]. It is unclear whether this positive framing of robots by narratives works equally for different robot design approaches and appearances. To address this open question we conducted 2×6 between-subjects online experiment and varied the introduction (narrative vs. instruction manual) and appearance of the robot (6 different robot appearances). We replicated previous results on evaluation effects for different robot appearances. Results indicate that robots introduced by a narrative story were evaluated as being more likable, intelligent, autonomous, and humanlike. They were also perceived to be less mechanical and less uncanny. However, there were no interaction effects between narration and robot appearance suggesting that narration is beneficial for robots regardless of their appearance and hence is a strong mechanism to shape positive expectations before actually interacting with a robot.