Initial validation of the trust of automated systems test (TOAST)

Wojton, Heather; Porter, Daniel; Lane, Stephanie; Bieber, Chad; Madhavan, Poornima

doi:10.1080/00224545.2020.1749020

Cited by 36 publications

(24 citation statements)

References 27 publications

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“…The Trust in Automated Systems Test (TOAST) scale developed by Wojton et al (2020) captures two dimensions of trust: understanding the system and system performance ( Wojton et al, 2020 ). The scale was tested for model fit and validated against existing measures of trust.…”

Section: Measures Of Tiamentioning

confidence: 99%

“…This may duplicate efforts, wasting the time and resources of scientists interested in exploring trust because there is no coherent narrative and no clear “state of the art” for TiA research. Exceptions do exist to this critique: Some recent measures such as the TOAST ( Wojton et al, 2020 ) and multi-dimensional measure of trust (MDMT) ( Malle and Ullman 2021 ) explicitly and intentionally relate to existing models. These measures are some of the few exceptions that prove the general rule.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of Trust in Automation: A Narrative Review and Reference Guide

et al. 2021

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With the rise of automated and autonomous agents, research examining Trust in Automation (TiA) has attracted considerable attention over the last few decades. Trust is a rich and complex construct which has sparked a multitude of measures and approaches to study and understand it. This comprehensive narrative review addresses known methods that have been used to capture TiA. We examined measurements deployed in existing empirical works, categorized those measures into self-report, behavioral, and physiological indices, and examined them within the context of an existing model of trust. The resulting work provides a reference guide for researchers, providing a list of available TiA measurement methods along with the model-derived constructs that they capture including judgments of trustworthiness, trust attitudes, and trusting behaviors. The article concludes with recommendations on how to improve the current state of TiA measurement.

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Section: Measures Of Tiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement of Trust in Automation: A Narrative Review and Reference Guide

et al. 2021

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“…Self-Report Measurements: These approaches measure trust by collecting responses in the form of surveys and questionnaires from participants. There is currently no standard for assessing human-machine trust [44], [48]. At present, it is measured by researchers using custom scales or validated measurements.…”

Section: B Trust Measurement-scales and Approachesmentioning

confidence: 99%

“…The Trust of Automated Systems Test (TOAST) measuring trust based on the theory of trust formation was proposed in [48]. Scale structure reliability and criteria validity were evaluated with civilian and military-affiliated samples.…”

Section: B Trust Measurement-scales and Approachesmentioning

confidence: 99%

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A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives

Gebru

Zeleke

Blankson

et al. 2022

IEEE Trans. Human-Mach. Syst.

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As complex autonomous systems become increasingly ubiquitous, their deployment and integration into our daily lives will become a significant endeavor. Human-machine trust relationship is now acknowledged as one of the primary aspects that characterize a successful integration. In the context of human-machine interaction (HMI), proper use of machines and autonomous systems depends both on the human and machine counterparts. On one hand, it depends on how well the human relies on the machine regarding the situation or task at hand based on willingness and experience. On the other hand, it depends on how well the machine carries out the task and how well it conveys important information on how the job is done. Furthermore, proper calibration of trust for effective HMI requires the factors affecting trust to be properly accounted for and their relative importance to be rightly quantified. In this article, the functional understanding of humanmachine trust is viewed from two perspectives-human-centric and machine-centric. The human aspect of the discussion outlines factors, scales, and approaches, which are available to measure and calibrate human trust. The discussion on the machine aspect spans trustworthy artificial intelligence, built-in machine assurances, and ethical frameworks of trustworthy machines.

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