With the growing influence of artificial intelligence (AI) in our lives, the ethical implications of AI have received attention from various communities. Building on previous work on trust in people and technology, we advance a multidimensional, multilevel conceptualization of trust in AI and examine the relationship between trust and ethics using the data from a survey of a national sample in the U.S. This paper offers two key dimensions of trust in AI—human-like trust and functionality trust—and presents a multilevel conceptualization (...) of trust with dispositional, institutional, and experiential trust each significantly correlated with trust dimensions. Along with trust in AI, we examine perceptions of the importance of seven ethics requirements of AI offered by the European Commission’s High-Level Expert Group. Then the association between ethics requirements and trust is evaluated through regression analysis. Findings suggest that the ethical requirement of societal and environmental well-being is positively associated with human-like trust in AI. Accountability and technical robustness are two other ethical requirements, which are significantly associated with functionality trust in AI. Further, trust in AI was observed to be higher than trust in other institutions. Drawing from our findings, we offer a multidimensional framework of trust that is inspired by ethical values to ensure the acceptance of AI as a trustworthy technology. (shrink)

This study reviews the current information concerning the measurement of the situation awareness (SA) of the teleoperated drivers of remotely controlled cars. The teleoperated drivers who drive these cars are in a remote location, and they control the cars through a communication interface. The objective methods with probes are beneficial in measuring SA on a closed circuit without real traffic. Questions specifically should address the information provided on the road by haptic sensations, such as the slope of the road and (...) the vehicle's speed. Methods for measuring SA that involve probes and interruptions obviously are not suitable for use on public roads. A stable environment for the display and control of the communication interface is suitable for an eye tracker in measuring SA. These features also facilitate the use of subjective observer-rating methods. Both of these methods are suitable for driving on real roads because they are not intrusive. SA research in a real-road environment also should demonstrate how the SA of other drivers is affected by seeing a car without a driver. Given the remote character of driving, cultural differences in cognition may have a significant influence on the SA of the teleoperated driver. (shrink)

Studying trust in the context of human-robot interaction is of great importance given the increasing relevance and presence of robotic agents in the social sphere, including educational and clinical. We investigated the acquisition, loss and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in-vivo. The relationship between trust and the representation of the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. Additionally, to outline children’s beliefs (...) about the mental competencies of the robot, we further evaluated the attribution of mental states to the interactive agent. In general, no substantial differences were found in children’s trust in the play-partner as a function of agency (human or robot). Nevertheless, 3-years-olds showed a trend toward trusting the human more than the robot, as opposed to 7-years-olds, who displayed the reverse pattern. These findings align with results showing that, for 3 and 7-year-olds, the cognitive ability to switch was significantly associated with trust restoration in the human and the robot, respectively. Additionally, supporting previous findings, we found a dichotomy between attributions of mental states to the human and robot and children’s behavior: while attributing to the robot significantly lower mental states than the human, in the trusting game children behaved in a similar way when they related to the human and the robot. Altogether, the results of this study highlight that similar psychological mechanisms are at play when children are to establish a novel trustful relationship with a human and robot partner. Furthermore, the findings shed light on the interplay – during development – between children’s quality of attachment relationships and the development of a Theory of Mind, which act differently on trust dynamics as a function of the children’s age as well as the interactive partner’s nature (human vs. robot). (shrink)

This paper discusses the robotization of the workplace, and particularly the question of whether robots can be good colleagues. This might appear to be a strange question at first glance, but it is worth asking for two reasons. Firstly, some people already treat robots they work alongside as if the robots are valuable colleagues. It is worth reflecting on whether such people are making a mistake. Secondly, having good colleagues is widely regarded as a key aspect of what can make (...) work meaningful. In discussing whether robots can be good colleagues, the paper compares that question to the more widely discussed questions of whether robots can be our friends or romantic partners. The paper argues that the ideal of being a good colleague has many different parts, and that on a behavioral level, robots can live up to many of the criteria typically associated with being a good colleague. Moreover, the paper also argues that in comparison with the more demanding ideals of being a good friend or a good romantic partner, it is comparatively easier for a robot to live up to the ideal of being a good colleague. The reason for this is that the “inner lives” of our friends and lovers are more important to us than the inner lives of our colleagues. (shrink)

New forms of artificial intelligence on the one hand and the ubiquitous networking of “everything with everything” on the other hand characterize the fourth industrial revolution. This results in a changed understanding of human–machine interaction, in new models for production, in which man and machine together with virtual agents form hybrid teams. The empirical study “Socializing with robots” aims to gain insight especially into conditions of development and processes of hybrid human–machine teams. In the experiment, human–robot actions and interactions were (...) closely observed in a virtual environment. Robots as partners differed in shape and behavior. Participants were instructed to achieve an objective that could only be achieved via close teamwork. This paper unites different aspects from core disciplines of social robotics and psychology contributing to anthropomorphization with the empirical insights of the experiment. It focuses on the psychological effects on anthropomorphization and mechanization, taking the inter- and transdisciplinary field of social robotics as a starting point. (shrink)

This article investigates the challenge of developing a robot capable of determining if a social situation demands trust. Solving this challenge may allow a robot to react when a person over or under trusts the system. Prior work in this area has focused on understanding the factors that influence a person’s trust of a robot (Hancock, et al., 2011). In contrast, by using game-theoretic representations to frame the problem, we are able to develop a set of conditions for determining if (...) an interactive situation demands trust. In two separate experiments, human subjects were asked to evaluate either written narratives or mazes in terms of whether or not they require trust. The results indicate aΦ1= +0.592 andΦ2= +0.406 correlation respectively between the subjects’ evaluations and the condition’s predictions. This is a strong correlation for a study involving human subjects. (shrink)

This article investigates the challenge of developing a robot capable of determining if a social situation demands trust. Solving this challenge may allow a robot to react when a person over or under trusts the system. Prior work in this area has focused on understanding the factors that influence a person’s trust of a robot (Hancock, et al., 2011). In contrast, by using game-theoretic representations to frame the problem, we are able to develop a set of conditions for determining if (...) an interactive situation demands trust. In two separate experiments, human subjects were asked to evaluate either written narratives or mazes in terms of whether or not they require trust. The results indicate aΦ1= +0.592 andΦ2= +0.406 correlation respectively between the subjects’ evaluations and the condition’s predictions. This is a strong correlation for a study involving human subjects. (shrink)

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. (shrink)

With the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the human–robot interaction (HRI) interface. However, building trust between humans and robots involves more than improving the machine’s technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned (...) stages of trust, our proposed decision to trust model considers more extensively the human and situational factors influencing how trust manifests within social relations. Priority is given to the human user of technology—the initiator of human—robot trust relations—at all stages of decision-making. The envisioned formation of optimal conditions in which trust emerges requires the collective participation of practitioners, policymakers, and members of the community. With trust facilitating the smooth transition of robots into more socially embedded roles, positive receptivity of the best engineering project arises from the presence of harmonious robot-human trust relations in community spaces. (shrink)

Definition of the problem This article critically addresses the conceptualization of trust in the ethical discussion on artificial intelligence (AI) in the specific context of social robots in care. First, we attempt to define in which respect we can speak of ‘social’ robots and how their ‘social affordances’ affect the human propensity to trust in human–robot interaction. Against this background, we examine the use of the concept of ‘trust’ and ‘trustworthiness’ with respect to the guidelines and recommendations of the High-Level (...) Expert Group on AI of the European Union. Arguments Trust is analyzed as a multidimensional concept and phenomenon that must be primarily understood as departing from trusting as a human functioning and capability. To trust is an essential part of the human basic capability to form relations with others. We further want to discuss the concept of _responsivity _which has been established in phenomenological research as a foundational structure of the relation between the self and the other. We argue that trust and trusting as a capability is fundamentally _responsive_ and needs responsive others to be realized. An understanding of _responsivity_ is thus crucial to conceptualize trusting in the ethical framework of human flourishing. We apply a phenomenological–anthropological analysis to explore the link between certain qualities of social robots that construct responsiveness and thereby simulate responsivity and the human propensity to trust. Conclusion Against this background, we want to critically ask whether the concept of trustworthiness in social human–robot interaction could be misguided because of the limited ethical demands that the constructed responsiveness of social robots is able to answer to. (shrink)

The popularization of intelligent machines such as service robot and industrial robot will make human–machine interaction, an essential work mode. This requires employees to adapt to the new work content through learning. However, the research involved human–machine interaction that how influences the employee’s learning is still rarely. This paper was to reveal the relationship between human–machine interaction and employee’s learning from the perspective of job characteristics and competence perception of employees. We sent questionnaire to 500 employees from 100 artificial intelligence (...) companies in China and received 319 valid and complete responses. Then, we adopted a hierarchical regression for the test. Empirical results show that human–machine interaction has a U-shaped curvilinear relationship with employee learning, and employee’s vitality mediates the curvilinear relationship. In addition, job characteristics moderate the U-shaped curvilinear relationship between human–machine interaction and employee’s vitality, especially the results of moderating effects varying with employee’s competence perception. Exploring the mechanism of the effect of human–machine interaction on employee’s learning enriches the socially embedded model. Moreover, it provides managerial implications how to enhance individual adaptability with the introduction of AI into firms. However, our research focuses more on the impact of human–machine interaction on employees at the initial stage of AI development, and the level of machine intelligence in various industries will reach a high degree of autonomy in the future. The future research can explore the impact of human–machine interaction on individual’s behavior at different stages, and the results may vary depending on the technologies mastered by different individuals. The study has theoretical and practical significance to human–machine interaction literature by underscoring the important of individual’s behavior among individuals with different skills. (shrink)

Social robots are designed to perform intelligent, emotional, and autonomous behavior in order to establish intimate relationships with humans, for instance, in the context of elderly care. However, the imitation of qualities usually assumed to be necessary for human reciprocal interaction may impact our understanding of social interaction. Against this background, we compare the technical operations based on which social robots imitate human-like behavior with the concepts of emotionality, intelligence, and autonomy as usually attached to humans. In doing so, we (...) aim at making a conceptual contribution to the intuition that the technical imitation of emotional, intelligent, and autonomous behavior differs qualitatively from human forms of emotionality, intelligence, and autonomy. We conclude that the increasing tendency of social robots to imitate human characteristics and the thus increased human tendency of anthropomorphizing technical systems put forth new forms of human–machine interactions (HMI) that call for a critical examination. Thus, the emerging forms of interaction may lead, for instance, to false, reinforced, and perplexing assumptions regarding reciprocity. Therefore, studies are needed that critically examine the potential consequences and psychological effects that may be associated with the use of social robots, especially with regard to vulnerable groups such as children or the elderly. (shrink)

With service robots becoming more ubiquitous in social life, interaction design needs to adapt to novice users and the associated uncertainty in the first encounter with this technology in new emerging environments. Trust in robots is an essential psychological prerequisite to achieve safe and convenient cooperation between users and robots. This research focuses on psychological processes in which user dispositions and states affect trust in robots, which in turn is expected to impact the behavior and reactions in the interaction with (...) robotic systems. In a laboratory experiment, the influence of propensity to trust in automation and negative attitudes toward robots on state anxiety, trust, and comfort distance toward a robot were explored. Participants were approached by a humanoid domestic robot two times and indicated their comfort distance and trust. The results favor the differentiation and interdependence of dispositional, initial, and dynamic learned trust layers. A mediation from the propensity to trust to initial learned trust by state anxiety provides an insight into the psychological processes through which personality traits might affect interindividual outcomes in human-robot interaction (HRI). The findings underline the meaningfulness of user characteristics as predictors for the initial approach to robots and the importance of considering users’ individual learning history regarding technology and robots in particular. (shrink)

Robotization and artificial intelligence are expected to change societies profoundly. Trust is an important factor of human–technology interactions, as robots and AI increasingly contribute to tasks previously handled by humans. Currently, there is a need for studies investigating trust toward AI and robots, especially in first-encounter meetings. This article reports findings from a study investigating trust toward robots and AI in an online trust game experiment. The trust game manipulated the hypothetical opponents that were described as either AI or robots. (...) These were compared with control group opponents using only a human name or a nickname. Participants lived in the United States. Describing opponents with robots or AI did not impact participants’ trust toward them. The robot called jdrx894 was the most trusted opponent. Opponents named “jdrx894” were trusted more than opponents called “Michael.” Further analysis showed that having a degree in technology or engineering, exposure to robots online and robot use self-efficacy predicted higher trust toward robots and AI. Out of Big Five personality characteristics, openness to experience predicted higher trust, and conscientiousness predicted lower trust. Results suggest trust on robots and AI is contextual and it is also dependent on individual differences and knowledge on technology. (shrink)

Recent times have seen increasing interest in conversational assistants (e.g., Amazon Alexa) designed to help users in their daily tasks. In military settings, it is critical to design assistants that are, simultaneously, helpful and able to minimize the user’s cognitive load. Here we show that embodiment plays a key role in achieving that goal. We present an experiment where participants engaged in the desert survival task in augmented reality. Participants were paired with a voice assistant, an embodied assistant, or no (...) assistant. The assistants made suggestions verbally throughout the task, whereas the embodied assistant further used gestures and emotion to communicate with the user. Our results indicate that both assistant conditions led to higher performance over the no assistant condition, but the embodied assistant achieved this with less cognitive burden on the decision maker than the non-embodied assistant. We discuss implications for the design of intelligent collaborative systems for the warfighter. (shrink)

Trust is a key dimension of human-robot interaction, and has often been studied in the HRI community. A common challenge arises from the difficulty of assessing trust levels in ecologically invalid environments: we present in this paper two independent laboratory studies, totalling 160 participants, where we investigate the impact of different types of errors on resulting trust, using both behavioural and subjective measures of trust. While we found a general effect of errors on reported and observed level of trust, no (...) significant differences between the type of errors were found in either of our studies. We discuss this negative result in light of our experimental protocols, and argue for the community to move towards alternative methodologies to assess trust. (shrink)

The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that due to (...) their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare. (shrink)

Prior research has demonstrated that trust in robots and performance of robots are two important factors that influence human–autonomy teaming. However, other factors may influence users’ perceptions and use of autonomous systems, such as perceived intent of robots and decision authority of the robots. The current study experimentally examined participants’ trust in an autonomous security robot (ASR), perceived trustworthiness of the ASR, and desire to use an ASR that varied in levels of decision authority and benevolence. Participants (N = 340) (...) were recruited from Amazon Mechanical Turk. Results revealed the participants had increased trust in the ASR when the robot was described as having benevolent intent compared to self-protective intent. There were several interactions between decision authority and intent when predicting the trust process, showing that intent may matter the most when the robot has discretion on executing that intent. Participants stated a desire to use the ASR in a military context compared to a public context. Implications for this research demonstrate that as robots become more prevalent in jobs paired with humans, factors such as transparency provided for the robot's intent and its decision authority will influence users’ trust and trustworthiness. (shrink)

Effective human–robot teaming increasingly requires humans to work with intelligent, autonomous machines. However, novel features of intelligent autonomous systems such as social agency and incomprehensibility may influence the human’s trust in the machine. The human operator’s mental model for machine functioning is critical for trust. People may consider an intelligent machine partner as either an advanced tool or as a human-like teammate. This article reports a study that explored the role of individual differences in the mental model in a simulated (...) environment. Multiple dispositional factors that may influence the dominant mental model were assessed. These included the Robot Threat Assessment, which measures the person’s propensity to apply tool and teammate models in security contexts. Participants were paired with an intelligent robot tasked with making threat assessments in an urban setting. A transparency manipulation was used to influence the dominant mental model. For half of the participants, threat assessment was described as physics-based ; the remainder received transparency information that described psychological cues. We expected that the physics-based transparency messages would guide the participant toward treating the robot as an advanced machine, while psychological messaging would encourage perceptions of the robot as acting like a human partner. We also manipulated situational danger cues present in the simulated environment. Participants rated their trust in the robot’s decision as well as threat and anxiety, for each of 24 urban scenes. They also completed the RoTA and additional individual-difference measures. Findings showed that trust assessments reflected the degree of congruence between the robot’s decision and situational danger cues, consistent with participants acting as Bayesian decision makers. Several scales, including the RoTA, were more predictive of trust when the robot was making psychology-based decisions, implying that trust reflected individual differences in the mental model of the robot as a teammate. These findings suggest scope for designing training that uncovers and mitigates the individual’s biases toward intelligent machines. (shrink)

This article investigates the challenge of developing a robot capable of determining if a social situation demands trust. Solving this challenge may allow a robot to react when a person over or under trusts the system. Prior work in this area has focused on understanding the factors that influence a person’s trust of a robot. In contrast, by using game-theoretic representations to frame the problem, we are able to develop a set of conditions for determining if an interactive situation demands (...) trust. In two separate experiments, human subjects were asked to evaluate either written narratives or mazes in terms of whether or not they require trust. The results indicate a _Φ_1= +0.592 and _Φ_2 = +0.406 correlation respectively between the subjects’ evaluations and the condition’s predictions. This is a strong correlation for a study involving human subjects. (shrink)

Strategies for improving the explainability of artificial agents are a key approach to support the understandability of artificial agents’ decision-making processes and their trustworthiness. However, since explanations are not inclined to standardization, finding solutions that fit the algorithmic-based decision-making processes of artificial agents poses a compelling challenge. This paper addresses the concept of trust in relation to complementary aspects that play a role in interpersonal and human–agent relationships, such as users’ confidence and their perception of artificial agents’ reliability. Particularly, this (...) paper focuses on non-expert users’ perspectives, since users with little technical knowledge are likely to benefit the most from “post-hoc”, everyday explanations. Drawing upon the explainable AI and social sciences literature, this paper investigates how artificial agent’s explainability and trust are interrelated at different stages of an interaction. Specifically, the possibility of implementing explainability as a trust building, trust maintenance and restoration strategy is investigated. To this extent, the paper identifies and discusses the intrinsic limits and fundamental features of explanations, such as structural qualities and communication strategies. Accordingly, this paper contributes to the debate by providing recommendations on how to maximize the effectiveness of explanations for supporting non-expert users’ understanding and trust. (shrink)

This work considers the future of driving in terms of both its short- and long-term horizons. It conjectures that human-controlled driving will follow in the footsteps of a wide swath of other, now either residual or abandoned human occupations. Pursuits that have preceded it into oblivion. In this way, driving will dwindle down into only a few niche locales wherein enthusiasts will still persist, much in the way that steam train hobbyists now continue their own aspirational inclinations. Of course, the (...) value of any such prognostication is in direct proportion to the degree that information is conveyed, and prospective uncertainty reduced. In more colloquial terms: the devil is in the details of these coming transitions. It is anticipated that we will see a progressive transformation of the composition of on-road traffic that will be registered most immediately in the realm of professional transportation in which the imperative for optimization exceeds that in virtually all other user segments. The transition from manual control to full automation will be more punctate than gradualist in is evolutionary development. As performance optimization slowly exhausts the commercial sector, it will progressively transition more into the discretionary realm by dint of simple technology transfer alone. The hedonic dimension of everyday driving will be dispersed and pursued by progressively fewer individuals. The traveling window of generational expectation will soon mean that human driving will be largely ‘forgotten,’ as each sequential generation matures without this, still presently common, experience. Indications of this stage of progress are beginning to... (shrink)

This study aims to contribute to emerging human-robot interaction research by adding longitudinal findings to a limited number of long-term social robotics home studies. We placed 70 robots in users’ homes for a period of up to six months, and used questionnaires and interviews to collect data at six points during this period. Results indicate that users’ evaluations of the robot dropped initially, but later rose after the robot had been used for a longer period of time. This is congruent (...) with the so-called mere-exposure effect, which shows an increasing positive evaluation of a novel stimulus once people become familiar with it. Before adoption, users focus on control beliefs showing that previous experiences with robots or other technologies allows to create a mental image of what having and using a robot in the home would entail. After adoption, users focus on utilitarian and hedonic attitudes showing that especially usefulness, social presence, enjoyment and attractiveness are important factors for long-term acceptance. (shrink)