Human Trust in Robot Capabilities across Tasks

Pan, S. K.; Chen, Min; Bodala, Indu P.; Nikolaidis, Stefanos; Hsu, David; Soh, Harold

doi:10.1145/3173386.3177034

Cited by 10 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some authors simply ask one question: "Do you trust this robot?" [26,32]. Some authors create questionnaires that are only narrowly relevant and specific to the interaction in their study [2,24,27].…”

Section: Background 21 Towards Standardized Measures Of Trust In Hrimentioning

confidence: 99%

Can You Trust Your Trust Measure?

Chita-Tegmark

Law

Rabb

et al. 2021

Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

Trust in human-robot interactions (HRI) is measured in two main ways: through subjective questionnaires and through behavioral tasks. To optimize measurements of trust through questionnaires, the field of HRI faces two challenges: the development of standardized measures that apply to a variety of robots with different capabilities, and the exploration of social and relational dimensions of trust in robots (e.g., benevolence). In this paper we look at how different trust questionnaires [18,30,35] fare given these challenges that pull in different directions (being general vs. being exploratory) by studying whether people think the items in these questionnaires are applicable to different kinds of robots and interactions. In Study 1 we show that after being presented with a robot (non-humanoid) and an interaction scenario (fire evacuation), participants rated multiple questionnaire items such as "This robot is principled" as "Non-applicable to robots in general" or "Non-applicable to this robot. " In Study 2 we show that the frequency of these ratings change (indeed, even for items rated as N/A to robots in general) when a new scenario is presented (game playing with a humanoid robot). Finally, while overall trust scores remained robust to N/A ratings, our results revealed potential fallacies in the way these scores are commonly interpreted. We conclude with recommendations for the development, use and results-reporting of trust questionnaires for future studies, as well as theoretical implications for the field of HRI. CCS CONCEPTS• Human-centered computing → Interaction paradigms; • Computer systems organization → Robotics.

show abstract

Section: Background 21 Towards Standardized Measures Of Trust In Hrimentioning

confidence: 99%

Can You Trust Your Trust Measure?

Chita-Tegmark

Law

Rabb

et al. 2021

Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

show abstract

“…Investigations of the factors affecting trust in HRI have listed a large number of determinants, from physical features to specific socio-cognitive abilities [6]. Over the last decades, with the increasing usage of robots in social contexts, research has investigated the role of the robot social skills on the human perception of the robots and how those competencies could affect or even predict how humans would interact with them [7], [8]. Furthermore, the robot social skills have also been used as a key for understanding the human expectations.…”

Section: Introductionmentioning

confidence: 99%

When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions

Mou¹,

Ruocco²,

Zanatto³

et al. 2021

Preprint

View full text Add to dashboard Cite

Trust is a critical issue in human-robot interactions (HRI) as it is the core of human desire to accept and use a non-human agent. Theory of Mind (ToM) has been defined as the ability to understand the beliefs and intentions of others that may differ from one's own. Evidences in psychology and HRI suggest that trust and ToM are interconnected and interdependent concepts, as the decision to trust another agent must depend on our own representation of this entity's actions, beliefs and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we investigated whether the exposure to the ToM abilities of a robot could affect humans' trust towards the robot. To this end, participants played a Price Game with a humanoid robot (Pepper) that was presented having either low-level ToM or high-level ToM. Specifically, the participants were asked to accept the price evaluations on common objects presented by the robot. The willingness of the participants to change their own price judgement of the objects (i.e., accept the price the robot suggested) was used as the main measurement of the trust towards the robot. Our experimental results showed that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.* for equal contributions in an alphabetical order.

show abstract

“…Some researchers have used a single-question measurement (Haesler et al, 2018;Rossi et al, 2018;Shu et al, 2018;), or created multiple items scales specifically for their studies (Nordheim et al, 2019;Byrne & Marín, 2018;Linnemann et al, 2018;Waytz et al, 2014;Garcia et al, 2015;Holthausen et al, 2020). Others re-used scales from psychological research (Martelaro et al, 2016;Sebo et al, 2019;Herse et al, 2018).…”

mentioning

confidence: 99%

Embodying a Virtual Agent in a Self-Driving Car

Lawson-Guidigbe,

Louveton,

Amokrane-Ferka

et al. 2023

International Journal of Mobile Human Computer Interaction

View full text Add to dashboard Cite

This article considers the visual appearance of a virtual agent designed to take over the driving task in a highly automated car, to answer the question of which visual appearance is appropriate for a virtual agent in a driving role. The authors first selected five models of visual appearance thanks to a picture sorting procedure (N = 19). Then, they conducted a survey-based study (N = 146) using scales of trust, anthropomorphism, and likability to assess the appropriateness of those five models from an early-prototyping perspective. They found that human and mechanical-human models were more trusted than other selected models in the context of highly automated cars. Instead, animal and mechanical-animal ones appeared to be less suited to the role of a driving assistant. Learnings from the methodology are discussed, and suggestions for further research are proposed.

show abstract

Human Trust in Robot Capabilities across Tasks

Cited by 10 publications

References 5 publications

Can You Trust Your Trust Measure?

Can You Trust Your Trust Measure?

When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions

Embodying a Virtual Agent in a Self-Driving Car

Contact Info

Product

Resources

About