Trust is a very popular concept in Human–Robot Interaction (HRI) to explain why and how people interact with robots. However, the definition of trust and the methods used to study the concept vary widely, often leading to confusion instead of insight. In this position paper, we discuss possible reasons for the confusion and disagreement by reviewing theory and methods. Our main criticism is that HRI researchers have recently taken an oversimplified approach by not adhering to the process model of trust. Instead, we have primarily measured perceived trustworthiness (often as a proxy for trust) and assumed that it accurately predicts behavior or is satisfactory as an end goal. In addition, many experimental paradigms fail to account for the critical elements of risk and vulnerability that are essential for trust to guide behavior. With this position paper, we aim to shed light on these “trust issues” in HRI and to improve the HRI community’s approach by providing suggestions for enhancing the quality of future research.

Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and ready to take control if needed. Partially automated driving introduces new complexities to human interactions with cars and can even increase collision risk. A better understanding of drivers’ trust in automation may help reduce these complexities. Much of the existing research on trust in ADAS has relied on use of surveys and physiological measures to assess trust and has been conducted using driving simulators. There have been relatively few studies that use telemetry data from real automated vehicles to assess trust in ADAS. In addition, although some ADAS technologies provide alerts when, for example, drivers’ hands are not on the steering wheel, these systems are not personalized to individual drivers. Needed are adaptive technologies that can help drivers of autonomous vehicles avoid crashes based on multiple real-time data streams. In this paper, we propose an architecture for adaptive autonomous driving assistance. Two layers of multiple sensory fusion models are developed to provide appropriate voice reminders to increase driving safety based on predicted driving status. Results suggest that human trust in automation can be quantified and predicted with 80% accuracy based on vehicle data, and that adaptive speech-based advice can be provided to drivers with 90 to 95% accuracy. With more data, these models can be used to evaluate trust in driving assistance tools, which can ultimately lead to safer and appropriate use of these features.

In two surveys of adults in the United States (N=723), we asked about perceptions of the degree to which a variety of behaviors, when engaged in with a sex robot or a human, would constitute monogamous relationship infidelity (Study 1), and also asked respondents to consider monogamous partner behavior when committed with a robot that was matched to sexual partner preferences (Study 2). Study 1 revealed that acts committed with sex robots were considered less severe and less likely to be judged as infidelity as those same acts committed with another human. Results further revealed that male survey respondents rated all partner behaviors with sex robots as less likely to constitute cheating behavior than their female counterparts. This finding may be explained by the portrayal of sex robots as hyper-feminized female sexual partners for men, both in the way these technologies are presented as well as how they are sold. However, when asked to consider a sex robot that was matched to males or females (Study 2), this difference disappeared. For all respondents, giving sex robots specificity as either male or female resulted in higher ratings of partner infidelity as compared to Study 1. This work allows us to empirically speak to a common concern at the center of many debates over the societal implications of sex robots---potential harm to human relationships.

The HRI community is working to develop interactive robots for a wide variety of pro-social tasks and ideals. As such we naturally focus on the positive side of HRI including how robots and humans may collaborate and the benefits of doing so. This workshop, in contrast, will focus on the dark side of HRI with the goal of identifying, understanding and guarding against the potential negative consequences of interactive robots. The primary objective of the workshop is to articulate and discuss the most pertinent ethical issues facing the HRI community and to develop a set of common community guidelines.

Socially intelligent artificial agents and robots are anticipated to become ubiquitous in home, work, and military environments. With the addition of such agents to human teams it is crucial to evaluate their role in the planning, decision making, and conflict mediation processes. We conducted a study to evaluate the utility of a virtual agent that provided mission planning support in a three-person human team during a military strategic mission planning scenario. The team consisted of a human team lead who made the final decisions and three supporting roles, two humans and the artificial agent. The mission outcome was experimentally designed to fail and introduced a conflict between the human team members and the leader. This conflict was mediated by the artificial agent during the debriefing process through discuss or debate and open communication strategies of conflict resolution [1]. Our results showed that our teams experienced conflict. The teams also responded socially to the virtual agent, although they did not find the agent beneficial to the mediation process. Finally, teams collaborated well together and perceived task proficiency increased for team leaders. Socially intelligent agents show potential for conflict mediation, but need careful design and implementation to improve team processes and collaboration.

A proposal for a unified theory of learned trust implemented in a cognitive architecture is presented. The theory is instantiated as a computational cognitive model of learned trust that integrates several seemingly unrelated categories of findings from the literature on interpersonal and human-machine interactions and makes unintuitive predictions for future studies. The model relies on a combination of learning mechanisms to explain a variety of phenomena such as trust asymmetry, the higher impact of early trust breaches, the black-hat/white-hat effect, the correlation between trust and cognitive ability, and the higher resilience of interpersonal as compared to human-machine trust. In addition, the model predicts that trust decays in the absence of evidence of trustworthiness or untrustworthiness. The implications of the model for the advancement of the theory on trust are discussed. Specifically, this work suggests two more trust antecedents on the trustor's side: perceived trust necessity and cognitive ability to detect cues of trustworthiness.