Trust is a key part of any social interaction, whether that be between humans, or humans interacting with different artificial agents. This paper investigates how an agent’s repeated incongruence failure might impact users’ trust. We augment a previously published human-robot interaction study (Nesset et al., 2023), by replacing the robot condition with a human actor. Here, we explore how users’ trust can be impacted by repeated failure depending on the agent involved and how to best repair trust once the failures take place. Our study found a significant decrease in users’ trust when a human makes an incongruence failure, but not when this failure was repeated, regardless of the repair strategy implemented. When comparing this to the previous robot condition, we found a significant difference in the trust measured in the human and the robot condition. Additionally, the repair strategy used had a significant effect on the users’ trust when the robot repeated its failure but not when the actor did. Our findings contribute to research on broken trust with repeated failures and highlight the importance of including a human comparison to better understand research findings in human-robot interactions.

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.

Trust repair strategies are an important part of human-robot interaction. In this study, we investigate how repeated failures impact users’ trust and how we might mitigate them. Specifically, we look at different repair strategies in the form of apologies, with additional features to them such as warnings and promises. Through an online study, we explore these repair strategies for repeated failures in the form of robot incongruence, where there is a mismatch of verbal and non-verbal information given by the robot. Our results show that such incongruent robot behaviour has a significant overall negative impact on participants’ trust. We found that the robot making a promise, and then breaking it, results in a significant decrease in participants’ trust, when compared to a general apology as a repair strategy. These findings contribute to the research on trust repair strategies and, additionally, shed light on how robot failures, in the form of incongruences, impact participants’ trust.

Neeraj Cherakara, Finny Varghese, Sheena Shabana, Nivan Nelson, Abhiram Karukayil, Rohith Kulothungan, Mohammed Afil Farhan, Birthe Nesset, Meriam Moujahid, Tanvi Dinkar, Verena Rieser, Oliver Lemon. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.

Trust between humans and robots is a complex, multifaceted phenomenon and measuring it subjectively and reliably is challenging. It is also context dependent and so choosing the right tool for a specific study can prove difficult. This paper aims to evaluate various trust measures and compare them in terms of sensitivity to changes in trust. This is done by comparing two validated trust questionnaires (TAS and MDMT) and one single item assessment in a COVID-19 triage scenario. We found that trust measures are equivalent in terms of sensitivity to changes in trust. Furthermore, the study showed that trust could be measured similarly through a single item assessment in comparison with other lengthier scales, in scenarios with distinct breaks in trust. This finding would be of use for experiments where lengthy questionnaires are not appropriate, such as those in the wild.

The ability to impute mental states to oneself or others, or Theory of Mind (ToM), has been intrinsically linked to trust between humans. However, less is known about how a robot mimicking ToM affects users’ trust and behaviour. We explore this through an online study, where we compare three robot personas in a cooperative maze navigation task: one neutral, one that explains its reasoning in technical terms, and one that mimics ToM. We show that ToM influences human decision-making behaviour and trust in a way that makes it more appropriate with respect to the competencies of the robot. This is key for human-robot collaboration and adoption of robotics moving forward.