Clark and Fischer (C&F) claim that trait attribution has major limitations in explaining human–robot interactions. We argue that the trait attribution approach can explain the three issues posited by C&F. We also argue that the trait attribution approach is parsimonious, as it assumes that the same mechanisms of social cognition apply to human–robot interaction.

Despite the rapid adoption of technology in human resource departments, there is little empirical work that examines the potential challenges of algorithmic decision-making in the recruitment process. In this paper, we take the perspective of job applicants and examine how they perceive the use of algorithms in selection and recruitment. Across four studies on Amazon Mechanical Turk, we show that people in the role of a job applicant perceive algorithm-driven recruitment processes as less fair compared to human only or algorithm-assisted (...) human processes. This effect persists regardless of whether the outcome is favorable to the applicant or not. A potential mechanism underlying algorithm resistance is the belief that algorithms will not be able to recognize their uniqueness as a candidate. Although the use of algorithms has several benefits for organizations such as improved efficiency and bias reduction, our results highlight a potential cost of using them to screen potential employees during recruitment. (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)

Several technological developments, such as self-service technologies and artificial intelligence, are disrupting the retailing industry by changing consumption and purchase habits and the overall retail experience. Although AI represents extraordinary opportunities for businesses, companies must avoid the dangers and risks associated with the adoption of such systems. Integrating perspectives from emerging research on AI, morality of machines, and norm activation, we examine how individuals morally behave toward AI agents and self-service machines. Across three studies, we demonstrate that consumers’ moral concerns (...) and behaviors differ when interacting with technologies versus humans. We show that moral intention is less likely to emerge for AI checkout and self-checkout machines compared with human checkout. In addition, moral intention decreases as people consider the machine less humanlike. We further document that the decline in morality is caused by less guilt displayed toward new technologies. The non-human nature of the interaction evokes a decreased feeling of guilt and ultimately reduces moral behavior. These findings offer insights into how technological developments influence consumer behaviors and provide guidance for businesses and retailers in understanding moral intentions related to the different types of interactions in a shopping environment. (shrink)

In two experiments (total N=693) we explored whether people are willing to consider paintings made by AI-driven robots as art, and robots as artists. Across the two experiments, we manipulated three factors: (i) agent type (AI-driven robot v. human agent), (ii) behavior type (intentional creation of a painting v. accidental creation), and (iii) object type (abstract v. representational painting). We found that people judge robot paintings and human painting as art to roughly the same extent. However, people are much less (...) willing to consider robots as artists than humans, which is partially explained by the fact that they are less disposed to attribute artistic intentions to robots. (shrink)

Gaze following occurs automatically in social interactions, but the degree to which gaze is followed depends on whether an agent is perceived to have a mind, making its behavior socially more relevant for the interaction. Mind perception also modulates the attitudes we have towards others, and deter-mines the degree of empathy, prosociality and morality invested in social interactions. Seeing mind in others is not exclusive to human agents, but mind can also be ascribed to nonhuman agents like robots, as long (...) as their appearance and/or behavior allows them to be perceived as intentional beings. Previous studies have shown that human appearance and reliable behavior induce mind perception to robot agents, and positively affect attitudes and performance in human-robot interaction. What has not been investigated so far is whether different triggers of mind perception have an independent or interactive effect on attitudes and performance in human-robot interaction. We examine this question by manipulating agent appearance (human vs. robot) and behavior (reliable vs. random) within the same paradigm and examine how congruent (human/reliable vs. robot/random) versus incongruent (human/random vs. robot/reliable) combinations of these triggers affect performance (i.e., gaze fol-lowing) and attitudes (i.e., agent ratings) in human-robot interaction. The results show that both ap-pearance and behavior affect human-robot interaction but that the two triggers seem to operate in iso-lation, with appearance more strongly impacting attitudes, and behavior more strongly affecting per-formance. The implications of these findings for human-robot interaction are discussed. (shrink)

Cognitive scientists have revealed systematic errors in human reasoning. There is disagreement about what these errors indicate about human rationality, but one upshot seems clear: human reasoning does not seem to fit traditional views of human rationality. This concern about rationality has made its way through various fields and has recently caught the attention of philosophers. The concern is that if philosophers are prone to systematic errors in reasoning, then the integrity of philosophy would be threatened. In this paper, I (...) present some of the more famous work in cognitive science that has marshaled this concern. Then I present reasons to think that those with training in philosophy will be less prone to certain systematic errors in reasoning. The suggestion is that if philosophers could be shown to be less prone to such errors, then the worries about the integrity of philosophy could be constrained. Then I present evidence that, according to performance on the CRT (Frederick 2005), those who have benefited from training and selection in philosophy are indeed less prone to one kind of systematic error: irrationally arbitrating between intuitive and reflective responses. Nonetheless, philosophers are not entirely immune to this systematic error, and their proclivity for this error is statistically related to their responses to a variety of philosophical questions. So, while the evidence herein puts constraints on the worries about the integrity of philosophy, it by no means eliminates these worries. The conclusion, then, is that the present evidence offers prima facie reasons to ascribe a mitigated privilege to philosophers' ability to rationally arbitrate between intuitive and reflective responses. (shrink)

The current research aims to answer the following question: “who will be held responsible for harm involving an artificial intelligence system?” Drawing upon the literature on moral judgments, we assert that when people perceive an AI system’s action as causing harm to others, they will assign blame to different entity groups involved in an AI’s life cycle, including the company, the developer team, and even the AI system itself, especially when such harm is perceived to be intentional. Drawing upon the (...) theory of mind perception, we hypothesized that two dimensions of mind: perceived agency—attributing intention, reasoning, pursuing goals, and communicating to AI, and perceived experience—attributing emotional states, such as feeling pain and pleasure, personality, and consciousness to AI—mediated the relationship between perceived intentional harm and blame judgments toward AI. We also predicted that people are likely to attribute higher mind characteristics to AI when harm is perceived to be directed to humans than when it is perceived to be directed to non-humans. We tested our research model in three experiments. In all experiments, we found that perceived intentional harm led to blame judgments toward AI. In two experiments, we found perceived experience, not agency, mediated the relationship between perceived intentional harm and blame judgments. We also found that companies and developers were held responsible for moral violations involving AI, with developers received the most blame among the entities involved. Our third experiment reconciles the findings by showing that perceived intentional harm directed to a non-human entity did not lead to increased attributions of mind to AI. These findings have implications for theory and practice concerning unethical outcomes and behavior associated with AI use. (shrink)

A human driver and an automated driving system might share control of automated vehicles in the near future. This raises many concerns associated with the assignment of responsibility for negative outcomes caused by them; one is that the human driver might be required to bear the brunt of moral and legal responsibilities. The psychological consequences of responsibility misattribution have not yet been examined. We designed a hypothetical crash similar to Uber’s 2018 fatal crash. We incorporated five legal responsibility attributions. Participants (...) chose their preferred liability attribution and then were randomly assigned into one of the five actual liability attribution conditions. They then responded to a series of questions concerning liability assignment, the crash, and AVs. Slightly more than 50% of participants thought that the human driver should bear full or primary liability. Legal responsibility misattribution negatively influenced these mentioned responses, regardless of overly attributing human or manufacturer liability. Overly attributing human liability had more negative influences. Improper liability attribution might hinder the adoption of AVs. Public opinion should not be ignored in developing a legal framework for AVs. (shrink)

When labeling an infectious disease, officially sanctioned scientific names, e.g., “H1N1 virus,” are recommended over place-specific names, e.g., “Spanish flu.” This is due to concerns from policymakers and the WHO that the latter might lead to unintended stigmatization. However, with little empirical support for such negative consequences, authorities might be focusing on limited resources on an overstated issue. This paper empirically investigates the impact of naming against the current backdrop of the 2019–2020 pandemic. The first hypothesis posited that using place-specific (...) names associated with China leads to greater levels of sinophobia, the negative stigmatization of Chinese individuals. The second hypothesis posited that using a scientific name leads to increased anxiety, risk aversion, beliefs about contagiousness of the virus, and beliefs about mortality rate. Results from two preregistered studies [N = 504; N = 412], conducted across three countries with the first study during the early outbreak and the second study at a later stage of the pandemic, found no evidence of any adverse effects of naming on sinophobia and strong support for the null hypothesis using Bayesian analyses. Moreover, analyses found no impact of naming on anxiety, risk aversion, beliefs about contagiousness of the virus, or beliefs about mortality rate, with mild to strong support for the null hypothesis across outcomes. Exploratory analyses also found no evidence for the effect of naming being moderated by political affiliation. In conclusion, results provide no evidence that virus naming impacted individual’s attitudes toward Chinese individuals or perceptions of the virus, with the majority of analyses finding strong support for the null hypothesis. Therefore, based on the current evidence, it appears that the importance given to naming infectious diseases might be inflated. (shrink)

Compared to human-operated vehicles, autonomous vehicles offer numerous potential benefits. However, public acceptance of AVs remains low. Using 4 studies, including 1 preregistered experiment, the present research examines the role of trust in AV adoption decisions. Using the Trust-Confidence-Cooperation model as a conceptual framework, we evaluate whether perceived integrity of technology—a previously underexplored dimension of trust that refers to perceptions of the moral agency of a given technology—influences AV policy support and adoption intent. We find that perceived technology integrity predicts (...) adoption intent for AVs and that messages that increase perceived integrity of AV technology result in greater AV adoption intent and policy support. This knowledge can be used to guide communication efforts aimed at increasing public trust in AVs, and ultimately enhance integration of AVs into transport systems. (shrink)

As the autonomous vehicles technology gradually enters the public eye, understanding consumers' psychological motivations for accepting autonomous vehicles is critical for the development of autonomous vehicles and society. Previously, researchers have explored the determinants of fully autonomous vehicles but the relevant research is far from enough. Moreover, the relationship between anthropomorphism and users' behavior has been ignored to a large extent. Therefore, this study aim to fill the gap by using anthropomorphism and the unified theory of acceptance and use of (...) technology to explore how system attributes and UTAUT attributes influence consumers' acceptance behavior. The data were collected via questionnaire survey conducted in Beijing, China, which can be a promising early adopter of AVs. Structural equation modeling was used to analyze the data. The results reveal that perceived anthropomorphism and perceived intelligence have a direct positive influence on the adoption of AVs; performance expectancy, effort expectancy, and facilitating conditions have an indirect positive influence on intention to adopt AVs. Also, this research contributes to the literature by enriching studies on psychological determinants of autonomous vehicles' adoption by taking an initial step to highlight anthropomorphism perceptions. This can provide managerial implications for policy-makers and businesses on how to effectively allocate resources to enhance autonomous vehicle adoption. (shrink)

Recent advances in automation technology have increased the opportunity for collaboration between humans and multiple autonomous systems such as robots and self-driving cars. In research on autonomous system collaboration, the trust users have in autonomous systems is an important topic. Previous research suggests that the trust built by observing a task can be transferred to other tasks. However, such research did not focus on trust in multiple different devices but in one device or several of the same devices. Thus, we (...) do not know how trust changes in an environment involving the operation of multiple different devices such as a construction site. We investigated whether trust can be transferred among multiple different devices, and investigated the effect of two factors: the similarity among multiple devices and the agency attributed to each device, on trust transfer among multiple devices. We found that the trust a user has in a device can be transferred to other devices and that attributing different agencies to each device can clarify the distinction among devices, preventing trust from transferring. (shrink)

The transfer of tasks with sometimes far-reaching implications to autonomous systems raises a number of ethical questions. In addition to fundamental questions about the moral agency of these systems, behavioral issues arise. We investigate the empirically accessible question of whether the imposition of harm by an agent is systematically judged differently when the agent is artificial and not human. The results of a laboratory experiment suggest that decision-makers can actually avoid punishment more easily by delegating to machines than by delegating (...) to other people. Our results imply that the availability of artificial agents could provide stronger incentives for decision-makers to delegate sensitive decisions. (shrink)

BackgroundThe practice of giving animal research subjects proper names is frowned on by the academic scientific community. While researchers provide a number of reasons for desisting from giving their animal subjects proper names, the most common are that naming leads to anthropomorphising which, in turn, leads to data and results that are unobjective and invalid; and while naming does not necessarily entail some mistake on the researcher’s part, some feature of the research enterprise renders the practice impossible or ill-advised.ObjectivesMy aim (...) is to assess whether the scientific community’s attitude towards naming animal research subjects is justified. That is, I wish to consider whether the practice of naming animal research subjects is good or bad for the purposes of scientific research.MethodAfter reviewing the extant literature, I constructed a list of the main arguments researchers provide for desisting from naming their animal research subjects. I then analysed these arguments, with a view to determining whether they in fact provide good reasons to avoid naming animal research subjects.ConclusionI argue that none of the aforementioned reasons usually provide good grounds for not naming animal research subjects. Moreover, there are usually powerful reasons in favour of researchers giving their research animals proper names. This is because the practice usually leads to greater empathy and so to improved animal well-being. This, in turn, leads to better animal science. Thus, the scientific community’s attitude towards naming animal research subjects is not justified. (shrink)

Spatial communications are essential to the survival and social interaction of human beings. In science fiction and the near future, robots are supposed to be able to understand spatial languages to collaborate and cooperate with humans. However, it remains unknown whether human speakers regard robots as human-like social partners. In this study, human speakers describe target locations to an imaginary human or robot addressee under various scenarios varying in relative speaker–addressee cognitive burden. Speakers made equivalent perspective choices to human and (...) robot addressees, which consistently shifted according to the relative speaker–addressee cognitive burden. However, speakers’ perspective choice was only significantly correlated to their social skills when the addressees were humans but not robots. These results suggested that people generally assume robots and humans with equal capabilities in understanding spatial descriptions but do not regard robots as human-like social partners. (shrink)

Autonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our study (...) undertakes a bibliometric and performance analysis to identify the conceptual and intellectual structures of trust and risk narratives within the AV research field by investigating engineering, social sciences, marketing, and business and infrastructure domains to offer an interdisciplinary approach. Our analysis provides an overview of the key research area across the search categories of ‘trust’ and ‘risk’. Our results show three main clusters with regard to trust and risk, namely, behavioural aspects of AV interaction; uptake and acceptance; and modelling human–automation interaction. The synthesis of the literature allows a better understanding of the public perception of AV and its historical conception and development. It further offers a robust model of public perception in AV, outlining the key themes found in the literature and, in turn, offers critical directions for future research. (shrink)