Shaping Trust Through Transparent Design: Theoretical and Experimental Guidelines

Lyons, Joseph B.; Sadler, Garrett; Koltai, Kolina; Battiste, Henri; Ho, Nhut; Hoffmann, Lauren; Smith, David E.; Johnson, Walter W.; Shively, Robert J.

doi:10.1007/978-3-319-41959-6_11

Cited by 57 publications

(30 citation statements)

References 8 publications

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“…Opening up such an internal knowledge base of a system to the user is commonly referred to as an open learner model [11] or system transparency [58,59,61]. Open learner models often take the form of a series of skill meters [10,38,39,57] and have been shown to help students to better regulate their efforts [10] or to improve their problem selection [63].…”

Section: Scaffolding and Transparency Through Robot's Explanationmentioning

confidence: 99%

“…Open learner models often take the form of a series of skill meters [10,38,39,57] and have been shown to help students to better regulate their efforts [10] or to improve their problem selection [63]. Transparency about system states has further been demonstrated to enhance trust in the system as a whole by making its behavior more understandable [58,59,61] and, therefore, reducing uncertainty in the user. We thus investigate if children (aged 4-7 years) can benefit from a robot that, as an additional scaffold, uses a strategy to reveal its current beliefs about the child's knowledge state and, thereby, explains why it structures the language learning session in a particular way.…”

Section: Scaffolding and Transparency Through Robot's Explanationmentioning

confidence: 99%

“…Based on findings from work on open learner models [11] and transparency [58,59,61], we hypothesized that verbalizing the robot's belief during the tutoring interaction will make the interaction more meaningful, which will increase the children's motivation to continue interacting (H1), overall learning gain (H2), and perceived learning (H3). For the re-engagement actions, we hypothesized that children who display dis-engagement will re-focus and continue learning after a re-engaging act by the robot (H4a), especially if the initiation of such an action is additionally explained by the robot (H4b, indicated also in Figure 2).…”

Section: Experimental Studymentioning

confidence: 99%

See 2 more Smart Citations

Adapt, Explain, Engage—A Study on How Social Robots Can Scaffold Second-language Learning of Children

Schodde

Hoffmann

Stange

et al. 2019

J. Hum.-Robot Interact.

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Social robots are increasingly applied to support children's learning, but how a robot can foster (or may hinder) learning is still not fully clear. One technique used by teachers is scaffolding, temporarily assisting learners to achieve new skills or levels of understanding they would not reach on their own. We ask if and how a social robot can be utilized to scaffold second-language learning of children at kindergarten age (4-7 years). Specifically, we explore an adapt-and-explain scaffolding strategy in which a robot acts as a peer-like tutor who dynamically adapts its behavior or the learning tasks to the cognitive and affective state of the child, and provides verbal explanations of these adaptations. An evaluation study with 40 children shows that children benefit from the learning adaptation and that the explanations have a positive effect especially for slower learners. Further, in 76% of all cases the robot managed to "re-engage" children who started to disengage from the learning interaction, helping them to achieve an overall higher learning gain. These findings demonstrate that a social robot equipped with suitable scaffolding mechanisms can increase engagement and learning, especially when being adaptive to the individual behavior and states of a child learner. CCS Concepts: • Human-centered computing → Empirical studies in HCI; • Applied computing → Interactive learning environments; • Computing methodologies → Cognitive robotics; Reasoning about belief and knowledge;

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Section: Scaffolding and Transparency Through Robot's Explanationmentioning

confidence: 99%

Section: Scaffolding and Transparency Through Robot's Explanationmentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

See 1 more Smart Citation

Adapt, Explain, Engage—A Study on How Social Robots Can Scaffold Second-language Learning of Children

Schodde

Hoffmann

Stange

et al. 2019

J. Hum.-Robot Interact.

View full text Add to dashboard Cite

show abstract

“…However, most of these systems are black boxes, whose decision-making process remains opaque to users. This has been shown to negatively impact user trust [12,39] and acceptance of system suggestions [9], and has raised concerns about algorithmic fairness and discrimination [13]. One way of mitigating these negative effects and making intelligent systems understandable and trustworthy is introducing explanation of algorithmic decision-making.…”

Section: Explainable Intelligent Systemsmentioning

confidence: 99%

“I’d like an Explanation for That!”Exploring Reactions to Unexpected Autonomous Driving

Wiegand

Eiband

Haubelt

et al. 2020

22nd International Conference on Human-Computer Interaction With Mobile Devices and Services

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Autonomous vehicles are complex systems that may behave in unexpected ways. From the drivers' perspective, this can cause stress and lower trust and acceptance of autonomous driving. Prior work has shown that explanation of system behavior can mitigate these negative effects. Nevertheless, it remains unclear in which situations drivers actually need an explanation and what kind of interaction is relevant to them. Using thematic analysis of real-world experience reports, we first identified 17 situations in which a vehicle behaved unexpectedly. We then conducted a think-aloud study (N = 26) in a driving simulator to validate these situations and enrich them with qualitative insights about drivers' need for explanation. We identified six categories to describe the main concerns and topics during unexpected driving behavior (emotion and evaluation, interpretation and reason, vehicle capability, interaction, future driving prediction and explanation request times). Based on these categories, we suggest design implications for autonomous vehicles, in particular related to collaboration insights, user mental models and explanation requests. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models; Empirical studies in HCI ; Scenario-based design.

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“…Extensive research has been conducted examining the factors that influence a human operator's trust in automation [7,10,22]. Many studies [4,15] have emphasized the importance of system transparency to maintain proper trust calibration. Studies on visualizing car uncertainty during automated driving [8,11] have indicated that providing good transparency by constantly presenting the system information is important to maintain continuous trust calibration.…”

Section: Trust Calibrationmentioning

confidence: 99%

Adaptive Trust Calibration for Supervised Autonomous Vehicles

Okamura

Yamada

2018

Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Poor trust calibration in autonomous vehicles often degrades total system performance in safety or efficiency. Existing studies have primarily examined the importance of system transparency of autonomous systems to maintain proper trust calibration, with little emphasis on how to detect over-trust and under-trust nor how to recover from them. With the goal of addressing these research gaps, we first provide a framework to detect a calibration status on the basis of the user's behavior of reliance. We then propose a new concept with cognitive cues called trust calibration cues (TCCs) to trigger the user to quickly restore appropriate trust calibration. With our framework and TCCs, a novel method of adaptive trust calibration is explored in this study. We will evaluate our framework and examine the effectiveness of TCCs with a newly developed online drone simulator.

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Shaping Trust Through Transparent Design: Theoretical and Experimental Guidelines

Cited by 57 publications

References 8 publications

Adapt, Explain, Engage—A Study on How Social Robots Can Scaffold Second-language Learning of Children

Adapt, Explain, Engage—A Study on How Social Robots Can Scaffold Second-language Learning of Children

“I’d like an Explanation for That!”Exploring Reactions to Unexpected Autonomous Driving

Adaptive Trust Calibration for Supervised Autonomous Vehicles

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