Trust Lengthens Decision Time on Unexpected Recommendations in Human-agent Interaction

Tokushige, Hiroyuki; Narumi, Takuji; Ono, Sayaka; Fuwamoto, Yoshitaka; Tanikawa, Tomohiro; Hirose, Michitaka

doi:10.1145/3125739.3125751

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Severity of the outcome affected compliance with robot requests (Salem et al, 2015 ). Similar effects were found by Tokushige et al ( 2017 ) as a result of unexpected recommendations.…”

Section: A Unified Information Processing Model For User Centered Faisupporting

confidence: 87%

Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development

2018

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While substantial effort has been invested in making robots more reliable, experience demonstrates that robots operating in unstructured environments are often challenged by frequent failures. Despite this, robots have not yet reached a level of design that allows effective management of faulty or unexpected behavior by untrained users. To understand why this may be the case, an in-depth literature review was done to explore when people perceive and resolve robot failures, how robots communicate failure, how failures influence people's perceptions and feelings toward robots, and how these effects can be mitigated. Fifty-two studies were identified relating to communicating failures and their causes, the influence of failures on human-robot interaction (HRI), and mitigating failures. Since little research has been done on these topics within the HRI community, insights from the fields of human computer interaction (HCI), human factors engineering, cognitive engineering and experimental psychology are presented and discussed. Based on the literature, we developed a model of information processing for robotic failures (Robot Failure Human Information Processing, RF-HIP), that guides the discussion of our findings. The model describes the way people perceive, process, and act on failures in human robot interaction. The model includes three main parts: (1) communicating failures, (2) perception and comprehension of failures, and (3) solving failures. Each part contains several stages, all influenced by contextual considerations and mitigation strategies. Several gaps in the literature have become evident as a result of this evaluation. More focus has been given to technical failures than interaction failures. Few studies focused on human errors, on communicating failures, or the cognitive, psychological, and social determinants that impact the design of mitigation strategies. By providing the stages of human information processing, RF-HIP can be used as a tool to promote the development of user-centered failure-handling strategies for HRIs.

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“…Severity of the outcome affected compliance with robot requests (Salem et al, 2015 ). Similar effects were found by Tokushige et al ( 2017 ) as a result of unexpected recommendations.…”

Section: A Unified Information Processing Model For User Centered Faisupporting

confidence: 87%

Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development

2018

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“…All the numbers reported are as of beginning of January 2021 4. We found several keywords including automated decision aid (e.g.,[91]), AI-based decision support system (e.g.,[24]), intelligent assistant (e.g.,[2]), intelligent agent (e.g.,[214]), classifier (e.g.,[237]), etc 5. Henceforth, all reported percentages are rounded up to the nearest tenth.Proc.…”

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confidence: 99%

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

Vereschak

Bailly

Caramiaux

2021

Proc. ACM Hum.-Comput. Interact.

105

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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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“…For example, while trusting belief usually has more emphasis on integrity of the trustee, trusting behavior focuses more on integrity and benevolence of the trustee [18]. There have been early results that suggest a mismatch between trust beliefs and trust behavior [28], which needs further investigation. We mainly focused on lack of accuracy as a cause for trust breakdown and improved accuracy as a form of trust recovery.…”

Section: Hypothesis Resultsmentioning

confidence: 99%

“…If the system is indeed unreliable or inaccurate, the user takes longer to decide whether to follow the system's advice [28]. In robots, Desai et al [6] found that early unreliability had a greater impact on trust formation than unreliability later on.…”

Section: Trust and Accuracymentioning

confidence: 99%

Second Chance for a First Impression? Trust Development in Intelligent System Interaction

Tolmeijer

Gadiraju

Ghantasala

et al. 2021

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

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There is a growing use of intelligent systems to support human decision-making across several domains. Trust in intelligent systems, however, is pivotal in shaping their widespread adoption. Little is currently understood about how trust in an intelligent system evolves over time and how it is mediated by the accuracy of the system. We aim to address this knowledge gap by exploring trust formation over time and its relation to system accuracy. To that end, we built an intelligent house recommendation system and carried out a longitudinal study consisting of 201 participants across 3 sessions in a week. In each session, participants were tasked with finding housing that fit a given set of constraints using a conventional web interface that reflected a typical housing search website. Participants could choose to use an intelligent decision support system to help them find the right house. Depending on the group, participants received a variation of accurate or inaccurate advice from the intelligent system throughout each session. We measured trust using a trust in automation scale at the end of each session.We found evidence suggesting that trust development is a slow process that evolves over multiple sessions, and that first impressions of the intelligent system are highly influential. Our results echo earlier research on trust formation in single session interactions, corroborating that reliability, validity, predictability, and dependability all influence trust formation. We also found that the age of the participants and their affinity with technology had an effect on their trust in the intelligent system. Our findings highlight the importance of first impressions and improvement of system accuracy for trust development. Hence, our study is an important first step in understanding trust development, breakdown of trust, and trust repair over multiple system interactions, informing improved system design.

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Trust Lengthens Decision Time on Unexpected Recommendations in Human-agent Interaction

Cited by 10 publications

References 14 publications

Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development

Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development

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

Second Chance for a First Impression? Trust Development in Intelligent System Interaction

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