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Timing is Key for Robot Trust Repair

TLDR
The effects of a robot apologizing for its mistake, promising to do better in the future, and providing additional reasons to trust it in a simulated office evacuation conducted in a virtual environment are evaluated.
Abstract
Even the best robots will eventually make a mistake while performing their tasks. In our past experiments, we have found that even one mistake can cause a large loss in trust by human users. In this paper, we evaluate the effects of a robot apologizing for its mistake, promising to do better in the future, and providing additional reasons to trust it in a simulated office evacuation conducted in a virtual environment. In tests with 319 participants, we find that each of these techniques can be successful at repairing trust if they are used when the robot asks the human to trust it again, but are not successful when used immediately after the mistake. The implications of these results are discussed.

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Citations
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Proceedings ArticleDOI

Overtrust of Robots in Emergency Evacuation Scenarios

TL;DR: All 26 participants followed the robot in the emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before, and the majority of people did not choose to safely exit the way they entered.
Journal ArticleDOI

From ‘automation’ to ‘autonomy’: the importance of trust repair in human–machine interaction

TL;DR: This article proposes a framework to infuse a unique human-like ability, building and actively repairing trust, into autonomous systems, and proposes a model to guide the design of future autonomy.
Journal ArticleDOI

Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams

TL;DR: A novel integrative model is presented that takes a longitudinal perspective on trust development and calibration in human–robot teams and introduces the introduction of the concept relationship equity.
Book ChapterDOI

The role of trust in human-robot interaction

TL;DR: This chapter believes that, while significant progress has been made in recent years, especially in quantifying and modeling trust, there are still several places where more investigation is needed.
Journal ArticleDOI

Effect of Robot Performance on Human–Robot Trust in Time-Critical Situations

TL;DR: A set of experiments that tasked individuals with navigating a virtual maze using different methods to simulate an evacuation concluded that a mistake made by a robot will cause a person to have a significantly lower level of trust in it in later interactions.
References
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Running experiments on Amazon Mechanical Turk

TL;DR: The authors presented new demographic data about the Mechanical Turk subject population, reviewed the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and compared the magnitude of effects obtained using Mechanical Turk and traditional subject pools.
Journal ArticleDOI

Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk

TL;DR: It is shown that respondents recruited in this manner are often more representative of the U.S. population than in-person convenience samples but less representative than subjects in Internet-based panels or national probability samples.
Journal ArticleDOI

Trust in Automation: Designing for Appropriate Reliance

TL;DR: This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives, and considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust.
Posted Content

Running experiments on Amazon Mechanical Turk

TL;DR: The authors presented new demographic data about the Mechanical Turk subject population, reviewed the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and compared the magnitude of effects obtained using Mechanical Turk and traditional subject pools.
Journal ArticleDOI

Getting to know you : Reputation and trust in a two-person economic exchange

TL;DR: Using a multiround version of an economic exchange (trust game), it is reported that reciprocity expressed by one player strongly predicts future trust expressed by their partner—a behavioral finding mirrored by neural responses in the dorsal striatum that extends previous model-based functional magnetic resonance imaging studies into the social domain.
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