User Trust Dynamics

Yu, Kun; Berkovsky, Shlomo; Taib, Ronnie; Conway, Dan; Zhou, Jianlong; Chen, Fang

doi:10.1145/3025171.3025219

Cited by 62 publications

(40 citation statements)

References 30 publications

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“…Above this, in line with earlier research in HRI (e.g., van Maris et al, 2017;Yu et al, 2017) and the interaction with other automated technology, for example, plant simulations (Lee and Moray, 1994) and automated driving (e.g., Beggiato et al, 2015;Hergeth et al, 2015;Kraus et al, 2019b), in this study, trust in the robot was found to increase throughout the interaction incrementally. As long as there is no negative information like an experience of restricted reliability (e.g., automation malfunction; Kraus et al, 2019b), a violation of initial expectations, or realization of initial concerns and fears, accumulated positive information and experiences lead to an increase in trust over time.…”

Section: Interrelation Of Different Trust Layerssupporting

confidence: 91%

“…The more often one interacts with a partner, the better and more realistically the trustworthiness can be evaluated and aligned with one's own experiences. This is supported by numerous findings regarding trust in automation in general and trust in HRI in particular, which show trust to increase over time with repeated error-free interaction and growing familiarity (e.g., Muir and Moray, 1996;Beggiato and Krems, 2013;van Maris et al, 2017;Yu et al, 2017;Kraus et al, 2019b). Therefore, it is hypothesized that:…”

Section: Negative Attitude Toward Robotsmentioning

confidence: 80%

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More Than a Feeling—Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety

Miller¹,

Kraus²,

Babel³

et al. 2021

Front. Psychol.

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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.

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Section: Interrelation Of Different Trust Layerssupporting

confidence: 91%

Section: Negative Attitude Toward Robotsmentioning

confidence: 80%

More Than a Feeling—Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety

Miller¹,

Kraus²,

Babel³

et al. 2021

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…Among them, we differentiate virtual penalties (𝑛 = 11, 16.9% e.g. game points [154,[244][245][246]), negative virtual consequences for participants (𝑛 = 29, 44.6%, e.g. car accident [136,178]) or negative virtual consequences for other stakeholders (𝑛 = 9, 13.4%, e.g.…”

Section: Virtual Incentivesmentioning

confidence: 99%

“…Thus, designing and ensuring trustworthy AI has raised interest in HCI. For instance, previous work has looked at what factors influence users' trust and how [26,183,248], how trust is established and developed [3,167,245], and how it can be modeled [2,105]. However, trust remains a highly challenging theoretical concept to study due to its multidisciplinary and multifaceted nature [119,129].…”

Section: Introductionmentioning

confidence: 99%

“…The second is the initial experience when interacting with the system. Several studies make the system error-free for the first recommendation [237] or in the first group of recommendations [244][245][246] to evoke positive expectations. Indeed, the effect of a mistake occurring during the early stages of an interaction on trust is unlikely to diminish over time [116,134,184].…”

Section: Introducing the System Performancementioning

confidence: 99%

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How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies

Vereschak

Bailly

Caramiaux

2021

Proc. ACM Hum.-Comput. Interact.

104

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The spread of AI-embedded systems involved in human decision making makes studying human trust in these systems critical. However, empirically investigating trust is challenging. One reason is the lack of standard protocols to design trust experiments. In this paper, we present a survey of existing methods to empirically investigate trust in AI-assisted decision making and analyse the corpus along the constitutive elements of an experimental protocol. We find that the definition of trust is not commonly integrated in experimental protocols, which can lead to findings that are overclaimed or are hard to interpret and compare across studies. Drawing from empirical practices in social and cognitive studies on human-human trust, we provide practical guidelines to improve the methodology of studying Human-AI trust in decision-making contexts. In addition, we bring forward research opportunities of two types: one focusing on further investigation regarding trust methodologies and the other on factors that impact Human-AI trust.CCS Concepts: • Human-centered computing → HCI theory, concepts and models.

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Investigating the Effects of Social Interactive Behaviours of a Robot on People’s Trust During a Navigation Task

Rossi

García

Maya

et al. 2019

Towards Autonomous Robotic Systems

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User Trust Dynamics

Cited by 62 publications

References 30 publications

More Than a Feeling—Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety

More Than a Feeling—Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety

How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies

Investigating the Effects of Social Interactive Behaviours of a Robot on People’s Trust During a Navigation Task

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