The Measurement of the Propensity to Trust Automation

Jessup, Sarah A.; Schneider, Tamera R.; Alarcon, Gene M.; Ryan, Tyler J.; Capiola, August

doi:10.1007/978-3-030-21565-1_32

Cited by 61 publications

(43 citation statements)

References 35 publications

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“…Many researchers used questionnaires (Körber, 2018) and behavioral methods (Miller et al, 2016;Jessup et al, 2019) to evaluate trust in automation and in AVs. For instance, Körber, (2018) built a multidimensional model to measure trust in automation using a survey study.…”

Section: Modeling Techniques Of Trust In Avs and Automationmentioning

confidence: 99%

Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability

Ayoub

Yang

Zhou

2021

Transportation Research Part F: Traffic Psychology and Behaviou

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Technological advances in the automotive industry are bringing automated driving closer to road use. However, one of the most important factors affecting public acceptance of automated vehicles (AVs) is the public's trust in AVs. Many factors can influence people's trust, including perception of risks and benefits, feelings, and knowledge of AVs. This study aims to use these factors to predict people's dispositional and initial learned trust in AVs using a survey study conducted with 1175 participants. For each participant, 23 features were extracted from the survey questions to capture his/her knowledge, perception, experience, behavioral assessment, and feelings about AVs.These features were then used as input to train an eXtreme Gradient Boosting (XGBoost) model to predict trust in AVs. With the help of SHapley Additive exPlanations (SHAP), we were able to interpret the trust predictions of XGBoost to further improve the explainability of the XGBoost model. Compared to traditional regression models and black-box machine learning models, our findings show that this approach was powerful in providing a high level of explainability and predictability of trust in AVs, simultaneously.

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Section: Modeling Techniques Of Trust In Avs and Automationmentioning

confidence: 99%

Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability

Ayoub

Yang

Zhou

2021

Transportation Research Part F: Traffic Psychology and Behaviou

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“…In the military study, a measurement of the Propensity to Trust Automation [ 41 ], adapted from the Propensity to Trust in Technology scale [ 79 ] was administered. This scale consisted of five items (e.g., “I think it’s a good idea to rely on automated agents for help.”) (α = 0.81).…”

Section: Methodsmentioning

confidence: 99%

Trust Development in Military and Civilian Human–Agent Teams: The Effect of Social-Cognitive Recovery Strategies

Kox

Siegling

Kerstholt

2022

Int J of Soc Robotics

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Autonomous agents (AA) will increasingly be deployed as teammates instead of tools. In many operational situations, flawless performance from AA cannot be guaranteed. This may lead to a breach in the human’s trust, which can compromise collaboration. This highlights the importance of thinking about how to deal with error and trust violations when designing AA. The aim of this study was to explore the influence of uncertainty communication and apology on the development of trust in a Human–Agent Team (HAT) when there is a trust violation. Two experimental studies following the same method were performed with (I) a civilian group and (II) a military group of participants. The online task environment resembled a house search in which the participant was accompanied and advised by an AA as their artificial team member. Halfway during the task, an incorrect advice evoked a trust violation. Uncertainty communication was manipulated within-subjects, apology between-subjects. Our results showed that (a) communicating uncertainty led to higher levels of trust in both studies, (b) an incorrect advice by the agent led to a less severe decline in trust when that advice included a measure of uncertainty, and (c) after a trust violation, trust recovered significantly more when the agent offered an apology. The two latter effects were only found in the civilian study. We conclude that tailored agent communication is a key factor in minimizing trust reduction in face of agent failure to maintain effective long-term relationships in HATs. The difference in findings between participant groups emphasizes the importance of considering the (organizational) culture when designing artificial team members.

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“…As a result, measuring trust depends on capturing other factors or underlying constructs. As different types of trust are investigated: trust propensity [32], dispositional trust [16], history-based trust [33], and affective and cognitive trust [5], what is measured continues to vary widely within the existing literature. Trust measurements are mainly done via self-reports by the human subjects under study [34]- [38].…”

Section: B Trust Measurement-scales and Approachesmentioning

confidence: 99%

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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The Measurement of the Propensity to Trust Automation

Cited by 61 publications

References 35 publications

Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability

Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability

Trust Development in Military and Civilian Human–Agent Teams: The Effect of Social-Cognitive Recovery Strategies

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

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