Human-robot Trust Research Articles

A Meta-Analysis of Vulnerability and Trust in Human–Robot Interaction

In human–robot interaction studies, trust is often defined as a process whereby a trustor makes themselves vulnerable to a trustee. The role of vulnerability however is often overlooked in this process but could play an important role in the gaining and maintenance of trust between users and robots. To better understand how vulnerability affects human–robot trust, we first reviewed the literature to create a conceptual model of vulnerability with four vulnerability categories. We then performed a meta-analysis, first to check the overall contribution of the variables included on trust. The results showed that overall, the variables investigated in our sample of studies have a positive impact on trust. We then conducted two multilevel moderator analysis to assess the effect of vulnerability on trust, including: (1) an intercept model that considers the relationship between our vulnerability categories and (2) a non-intercept model that treats each vulnerability category as an independent predictor. Only model 2 was significant, suggesting that to build trust effectively, research should focus on improving robot performance in situations where the users are unsure how reliable the robot will be. As our vulnerability variable is derived from studies of human–robot interaction and researcher reflections about the different risks involved, we relate our findings to these domains and make suggestions for future research avenues.

The Effects of Human-Robot Interactions and the Human-Robot Relationship on Robot Competence, Trust, and Acceptance

As social robots may be used by a single user or multiple users different social scenarios are becoming more important for defining human-robot relationships. Therefore, this study explored human-robot relationships between robots and users in different interaction modes to improve user interaction experience. Specifically, education and companion were selected as the most common areas in the use of social robots. The interaction modes used include single-user interaction and multi-user interaction. The three human-robot relationships were adopted. The robot competence scale, human-robot trust scale, and acceptance of robot scale were used to evaluate subjects’ views on robots. The results demonstrate that in the two scenarios, people were more inclined to maintain a more familiar and closer relationship with the social robot when the robot interacted with a single user. When multiple persons interact in an education scenario, setting the robot to Acquaintance relationships is recommended to improve its competence and people’s trust in the robot. Similarly, in multi-person interaction, Acquaintance relationships would be more accepted and trusted by people in a companion scenario. Based on these results, robot sensors can be added to further optimize human-robot interaction sensing systems. By identifying the number of users in the interaction environment, robots can automatically employ the best human-robot relationship for interaction. Optimizing human-robot interaction sensing systems can also improve robot performance perceived in the interaction to meet different users’ needs and achieve more natural human-robot interaction experiences.

Effects of Robots’ Character and Information Disclosure on Human–Robot Trust and the Mediating Role of Social Presence

Effects of Robots’ Character and Information Disclosure on Human–Robot Trust and the Mediating Role of Social Presence

Do Humans Trust Robots that Violate moral trust?

The increasing use of robots in social applications requires further research on human-robot trust. The research on human-robot trust needs to go beyond the conventional definition that mainly focuses on how human-robot relations are influenced by robot performance. The emerging field of social robotics considers optimizing a robot’s personality a critical factor in user perceptions of experienced human-robot interaction (HRI). Researchers have developed trust scales that account for different dimensions of trust in HRI. These trust scales consider one performance aspect (i.e., the trust in an agent’s competence to perform a given task and their proficiency in executing the task accurately) and one moral aspect (i.e., trust in an agent’s honesty in fulfilling their stated commitments or promises) for human-robot trust. The question that arises here is to what extent do these trust aspects affect human trust in a robot? The main goal of this study is to investigate whether a robot’s undesirable behavior due to the performance trust violation would affect human trust differently than another similar undesirable behavior due to a moral trust violation. We designed and implemented an online human-robot collaborative search task that allows distinguishing between performance and moral trust violations by a robot. We ran these experiments on Prolific and recruited 100 participants for this study. Our results showed that a moral trust violation by a robot affects human trust more severely than a performance trust violation with the same magnitude and consequences.

AI and human-robot interaction: A review of recent advances and challenges

The integration of artificial intelligence (AI) into human-robot interaction (HRI) has witnessed significant advancements in recent years, revolutionizing the way humans and robots collaborate and coexist. This review provides a comprehensive overview of the latest breakthroughs in AI-driven HRI and identifies the challenges that lie ahead. Recent years have seen a surge in AI-driven capabilities that enhance human-robot interaction. Machine learning algorithms enable robots to adapt to user preferences and behaviors, creating personalized and intuitive interactions. Natural language processing (NLP) facilitates seamless communication between humans and robots, enabling voice commands and context-aware responses. Computer vision advancements empower robots with enhanced perception, enabling them to recognize and interpret human gestures, emotions, and facial expressions. Reinforcement learning has played a pivotal role in enabling robots to learn from human feedback and optimize their actions in real-time. Socially assistive robots leverage AI to provide emotional support and companionship, particularly in healthcare and elderly care settings. Despite these advancements, challenges persist in the field of AI-driven HRI. Ethical considerations, including privacy concerns and the responsible use of AI in influencing human behavior, demand careful attention. Ensuring the safety and security of AI-driven robotic systems remains paramount, requiring robust measures against malicious attacks and unintended consequences. Human-robot trust remains a critical challenge, necessitating transparent AI algorithms and effective communication strategies. Interdisciplinary collaboration between AI researchers, roboticists, psychologists, and ethicists is essential to address the complex socio-technical aspects of HRI. The fusion of AI and human-robot interaction holds immense potential to redefine various facets of our daily lives. This review highlights recent strides in AI-driven HRI, emphasizing the need for interdisciplinary efforts to address challenges and ensure the responsible development and deployment of AI-powered robotic systems. As researchers continue to innovate, the dynamic landscape of AI and human-robot interaction promises a future where seamless collaboration and coexistence between humans and robots become an integral part of our societal fabric.

Facets of Trust and Distrust in Collaborative Robots at the Workplace: Towards a Multidimensional and Relational Conceptualisation

The relevance of trust on the road to successful human-robot interaction is widely acknowledged. Thereby, trust is commonly understood as a monolithic concept characterising dyadic relations between a human and a robot. However, this conceptualisation seems oversimplified and neglects the specific interaction context. In a multidisciplinary approach, this conceptual analysis synthesizes sociological notions of trust and distrust, psychological trust models, and ideas of philosophers of technology in order to pave the way for a multidimensional, relational and context-sensitive conceptualisation of human-robot trust and distrust. In this vein, trust is characterised functionally as a mechanism to cope with environmental complexity when dealing with ambiguously perceived hybrid robots such as collaborative robots, which enable human-robot interactions without physical separation in the workplace context. Common definitions of trust in the HRI context emphasise that trust is based on concrete expectations regarding individual goals. Therefore, I propose a three-dimensional notion of trust that binds trust to a reference object and accounts for various coexisting goals at the workplace. Furthermore, the assumption that robots represent trustees in a narrower sense is challenged by unfolding influential relational networks of trust within the organisational context. In terms of practical implications, trust is distinguished from acceptance and actual technology usage, which may be promoted by trust, but are strongly influenced by contextual moderating factors. In addition, theoretical arguments for considering distrust not only as the opposite of trust, but as an alternative and coexisting complexity reduction mechanism are outlined. Finally, the article presents key conclusions and future research avenues.

The Effects of Robot Managers’ Reward-Punishment Behaviours on Human–Robot Trust and Job Performance

The Effects of Robot Managers’ Reward-Punishment Behaviours on Human–Robot Trust and Job Performance

Robots abide by ethical principles promote human-robot trust? The reverse effect of decision types and the human-robot projection hypothesis

Robots abide by ethical principles promote human-robot trust? The reverse effect of decision types and the human-robot projection hypothesis

A matter of consequences

Abstract On reviewing the literature regarding acceptance and trust in human-robot interaction (HRI), there are a number of open questions that needed to be addressed in order to establish effective collaborations between humans and robots in real-world applications. In particular, we identified four principal open areas that should be investigated to create guidelines for the successful deployment of robots in the wild. These areas are focused on: (1) the robot’s abilities and limitations; in particular when it makes errors with different severity of consequences, (2) individual differences, (3) the dynamics of human-robot trust, and (4) the interaction between humans and robots over time. In this paper, we present two very similar studies, one with a virtual robot with human-like abilities, and one with a Care-O-bot 4 robot. In the first study, we create an immersive narrative using an interactive storyboard to collect responses of 154 participants. In the second study, 6 participants had repeated interactions over three weeks with a physical robot. We summarise and discuss the findings of our investigations of the effects of robots’ errors on people’s trust in robots for designing mechanisms that allow robots to recover from a breach of trust. In particular, we observed that robots’ errors had greater impact on people’s trust in the robot when the errors were made at the beginning of the interaction and had severe consequences. Our results also provided insights on how these errors vary according to the individuals’ personalities, expectations and previous experiences.

ETHICAL CONSIDERATION IN HOME MONITORING ASSISTIVE ROBOT FOR COGNITIVE IMPAIRMENT: A SCOPING REVIEW

Abstract As the aging population continues to grow, the demand for innovative technologies to support individuals with cognitive impairment in their daily lives has risen significantly. Home monitoring assistive robots (HMAR) have emerged as a promising solution, offering assistance with tasks, scheduling and safety monitoring. This scoping review aims to explore and synthesize the ethical considerations surrounding the use of HMARs for persons with cognitive impairment. We adopted the Arksey and O’Malley framework, encompassing five stages: identifying the research question, identifying relevant studies, selecting studies, charting the data, and summarizing and reporting results. A comprehensive search strategy was employed across electronic databases, including PubMed, PsycINFO, Scopus, and CINAHL, using search terms derived from titles, abstracts, and keywords identified in key publications and previous reviews relevant to the scope of this review. The synthesis of the 25 included literature highlights several key ethical dimensions related to HMARs for individuals with cognitive impairment, including autonomy and decision-making, privacy and surveillance, family dynamics and dyadic relationships, human-robot trust, cultural considerations, and equitable access to technology. The review underscores the complexity of ethical dilemmas posed by HMARs, including issues of informed consent, data security, power differentiation between persons with cognitive impairment and their caregivers, and disparities in technology access. The findings of this scoping review contribute to the ongoing discourse on the ethical implications of utilizing HMARs for cognitive impairment and provide insights for the development of responsible and inclusive technological solutions.

The dynamics of human–robot trust attitude and behavior — Exploring the effects of anthropomorphism and type of failure

In two experiments centered around trust dynamics in human–robot interaction, we explored the interplay between attribute-based characteristic of robots (i.e., anthropomorphism and type of failure) and trust attitude, perceived reliability, as well as trust behavior. In general, trust attitude is considered to be one of the most important factors for successful collaboration. However, although previous research has shown that performance and attribute-based characteristics of robots lead to changes in trust attitudes, the question remains whether this is also linked to behavioral adaptations. To investigate this relationship, participants collaborated with robots via voice commands that failed. In the first experiment, participants worked with either an anthropomorphic or technical robot. In the second experiment, all participants worked with the technical robot, which made a failure in either information acquisition/processing or action implementation. Participants trusted the technical robot significantly more than the anthropomorphic one. Moreover, failures in information acquisition/processing seemed to lead to more trust dissolution than failures in action implementation. In both experiments, the dynamics of trust attitudes as a function of interaction experience were shown, in particular, trust dissolution due to failure. However, there was no evidence of behavioral adaptation following a failure experience. In general, the effects on the attitudinal level did not clearly translate into behavioral adaptations. For trust attitude, purely decorative anthropomorphic robot design and failures related to the cognitive abilities of robots should be avoided.

“It's not Paul, it's a robot”: The impact of linguistic framing and the evolution of trust and distrust in a collaborative robot during a human-robot interaction

An appropriate level of human-robot trust is widely acknowledged as a crucial factor for successful human-robot interaction (HRI). Previous research has addressed anthropomorphic design but mostly neglected linguistic framing as a means to influence trust by altering the perceived human-likeness of a robot. Similarly, time-dependant patterns of trust and distrust during an HRI have rarely been investigated, hindering the development of a coherent theoretical framework on framing effects, their formation conditions, and their relation to trust and distrust evolution in the HRI context. A previous online study suggested that linguistic framing modulates inexperienced factory workers’ trust in a collaborative robot at the workplace. This article presents a follow-up study with a sample of students and an realistic HRI setting. Despite using similar framing stimuli, the study failed to replicate significant framing effects on trust, suggesting their context-sensitivity and dependence on individual characteristics of the recipients of the framing. Additionally, a significant increase in trust and decrease in distrust during the HRI highlights the need for time-dependant considerations. The results call for a more fine-grained investigation of linguistic framing effects as well as of trust and distrust evolution in order to develop a comprehensive theoretical framework applicable to the HRI context.

The theory of mind and human–robot trust repair

Nothing is perfect and robots can make as many mistakes as any human, which can lead to a decrease in trust in them. However, it is possible, for robots to repair a human’s trust in them after they have made mistakes through various trust repair strategies such as apologies, denials, and promises. Presently, the efficacy of these trust repairs in the human–robot interaction literature has been mixed. One reason for this might be that humans have different perceptions of a robot’s mind. For example, some repairs may be more effective when humans believe that robots are capable of experiencing emotion. Likewise, other repairs might be more effective when humans believe robots possess intentionality. A key element that determines these beliefs is mind perception. Therefore understanding how mind perception impacts trust repair may be vital to understanding trust repair in human–robot interaction. To investigate this, we conducted a study involving 400 participants recruited via Amazon Mechanical Turk to determine whether mind perception influenced the effectiveness of three distinct repair strategies. The study employed an online platform where the robot and participant worked in a warehouse to pick and load 10 boxes. The robot made three mistakes over the course of the task and employed either a promise, denial, or apology after each mistake. Participants then rated their trust in the robot before and after it made the mistake. Results of this study indicated that overall, individual differences in mind perception are vital considerations when seeking to implement effective apologies and denials between humans and robots.

The Theory of Mind and Human-Robot Trust Repair

The Theory of Mind and Human-Robot Trust Repair

Three Strikes and you are out!: The impacts of multiple human–robot trust violations and repairs on robot trustworthiness

Robots like human co-workers can make mistakes violating a human’s trust in them. When mistakes happen, humans can see robots as less trustworthy which ultimately decreases their trust in them. Trust repair strategies can be employed to mitigate the negative impacts of these trust violations. Yet, it is not clear whether such strategies can fully repair trust nor how effective they are after repeated trust violations. To address these shortcomings, this study examined the impact of four distinct trust repair strategies: apologies, denials, explanations, and promises on overall trustworthiness and its sub-dimensions: ability, benevolence, and integrity after repeated trust violations. To accomplish this, a between-subjects experiment was conducted where participants worked with a robot co-worker to accomplish a task. The robot violated the participant’s trust and then provided a particular repair strategy. Results indicated that after repeated trust violations, none of the repair strategies ever fully repaired trustworthiness and two of its sub-dimensions: ability and integrity. In addition, after repeated interactions, apologies, explanations, and promises appeared to function similarly to one another, while denials were consistently the least effective at repairing trustworthiness and its sub-dimensions. In sum, this paper contributes to the literature on human–robot trust repair through both of these original findings.

Three Strikes and You are Out!: The Impacts of Multiple Human-Robot Trust Violations and Repairs on Robot Trustworthiness

Three Strikes and You are Out!: The Impacts of Multiple Human-Robot Trust Violations and Repairs on Robot Trustworthiness

The Influence of Robots' Fairness on Humans' Reward-Punishment Behaviors and Trust in Human-Robot Cooperative Teams.

Based on social exchange theory, this study investigates the effects of robots' fairness and social status on humans' reward-punishment behaviors and trust in human-robot interactions. In human-robot teamwork, robots show fair behaviors, dedication (altruistic unfair behaviors), and selfishness (self-interested unfair behaviors), but few studies have discussed the effects of these robots' behaviors on teamwork. This study adopts a 3 (the independent variable is the robot's fairness: self-interested unfair behaviors, fair behaviors, and altruistic unfair behaviors) × 3 (the moderator variable is the robot's social status: superior, peer, and subordinate) experimental design. Each participant and a robot completed the experimental task together through a computer. When robots have different social statuses, the more altruistic the fairness of the robot, the more reward behaviors, the fewer punishment behaviors, and the higher human-robot trust of humans. Robots' higher social status weakens the influence of their fairness on humans' punishment behaviors. Human-robot trust will increase humans' reward behaviors and decrease humans' punishment behaviors. Humans' reward-punishment behaviors will increase repaired human-robot trust. Robots' fairness has a significant impact on humans' reward-punishment behaviors and trust. Robots' social status moderates the effect of their fair behavior on humans' punishment behavior. There is an interaction between humans' reward-punishment behaviors and trust. The study can help to better understand the interaction mechanism of the human-robot team and can better serve the management and cooperation of the human-robot team by appropriately adjusting the robots' fairness and social status.

The Effects of Robots’ Altruistic Behaviours and Reciprocity on Human-robot Trust

The Effects of Robots’ Altruistic Behaviours and Reciprocity on Human-robot Trust

Trust in humanoid robots in footwear stores: A large-N crisp-set qualitative comparative analysis (csQCA) model

Based upon Computers-Are-Social-Actors and Social Exchange theories, this study investigated causal configurations of Human-Robot Trust (HRT), social intelligence, and retail assistance performance of humanoid robots that may increase consumers’ intention to visit a footwear store. A pretest and main study (n = 455) were conducted with a video-based stimulus. Using Crist-Set Qualitative Comparative Analysis (csQCA), the study took a holistic approach to the emerging topic of humanoid robots, and proposed a QCA-based conceptual framework. The results revealed that consumers will always demonstrate a strong intention to visit a robot-operated store when the configurations of HRT and retail assistance performance or HRT and social intelligence are present (sufficient conditions). Most importantly, consumers will never demonstrate a strong intention to visit the store in the absence of HRT (necessary condition). This study serves as an example of new alternative analytical methods, such as large-N csQCA applications, to traditional multivariate analyses.

Insights into the relationship between usability and willingness to use a robot in the future workplaces: Studying the mediating role of trust and the moderating roles of age and STARA.

Background and aimHuman–robot collaboration is the key component of the fourth industrial revolution concept. Workers’ willingness to collaborate with industrial robots is a basic requirement for an efficient and effective interaction. The roles of human-robot trust and technology affinity as mediators in the relationship between robot usability and worker willingness were analyzed in this study. As other critical variables, the mediator roles of Age and STARA were also calculated.Materials and methodsThis study included 400 workers from a car company who interacted with industrial robots in their daily work activities. After examining the questionnaires’ validity and reliability, the main variables were determined to be willingness to use robots and robot usability. AMOS software also considered human-robot trust and worker technology affinity as mediators. The bootstrapping method was used to evaluate indirect relationships. A set of goodness-of-fit indices were presented to determine the adequacy of the goodness of fit between the proposed model and the data.ResultsBased on model fit indices, an overall satisfactory model fit was obtained for the direct/indirect relationship between robot usability and worker willingness to use it (with mediating role of human-robot trust). Workers’ age and fear of Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) were identified as moderators in the relationship between usability and willingness.ConclusionAttention to the robot usability and the role of workers’ trust in robots appears to be required to ensure workers’ willingness to use robots and the success of human-robot collaboration in future workplaces. As the workers age and their fear of robots grows, usability can play a larger role in increasing their willingness to put robots to work.