Linking precursors of interpersonal trust to human-automation trust: An expanded typology and exploratory experiment

Calhoun, Christopher S.; Bobko, Philip; Gallimore, Jennie J.; Lyons, Joseph B.

doi:10.1080/21515581.2019.1579730

Cited by 41 publications

(23 citation statements)

References 40 publications

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“…Empirical research demonstrates the positive impact of AI transparency and explainability on trust [e.g. 48,49,50]. Experimental research undertaken in military settings indicates that when human operators and AI agents collaborate, increased transparency enhances trust [48,49].…”

Section: Transparency and Explainabilitymentioning

confidence: 99%

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A Review of Trust in Artificial Intelligence: Challenges, Vulnerabilities and Future Directions

Lockey

Gillespie

Holm³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Artificial Intelligence (AI) can benefit society, but it is also fraught with risks. Societal adoption of AI is recognized to depend on stakeholder trust in AI, yet the literature on trust in AI is fragmented, and little is known about the vulnerabilities faced by different stakeholders, making it is difficult to draw on this evidence-base to inform practice and policy. We undertake a literature review to take stock of what is known about the antecedents of trust in AI, and organize our findings around five trust challenges unique to or exacerbated by AI. Further, we develop a concept matrix identifying the key vulnerabilities to stakeholders raised by each of the challenges, and propose a multi-stakeholder approach to future research.

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Section: Transparency and Explainabilitymentioning

confidence: 99%

“…48,49,50]. Experimental research undertaken in military settings indicates that when human operators and AI agents collaborate, increased transparency enhances trust [48,49]. Explanations have been shown to increase trust in the results of a product release planning tool [51].…”

Section: Transparency and Explainabilitymentioning

confidence: 99%

A Review of Trust in Artificial Intelligence: Challenges, Vulnerabilities and Future Directions

Lockey

Gillespie

Holm³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Previous research has demonstrated user's trustworthiness perceptions in robots to be an important factor (Alarcon et al, 2021). These contextspecific antecedents to trust have demonstrated predictive validity for trust and reliance across a wide range of studies in the interpersonal trust literature (Colquitt et al, 2007) and more recently have been investigated in the trust in automation literature (Calhoun et al, 2019).…”

Section: Trust Toward Automation and Hrimentioning

confidence: 99%

Exploring the Effects of Swarm Degradations on Trustworthiness Perceptions, Reliance Intentions, and Reliance Behaviors

Hamdan

Capiola

Alarcon

et al. 2021

Proceedings of the Human Factors and Ergonomics Society Annual

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Swarms comprise robotic assets operating autonomously through local control laws. Research on human-swarm interaction (HSwI) investigates how human operators collaborate with swarms to accomplish shared goals. Researchers have begun to investigate the role of trust in HSwI, specifically which aspects of robotic swarms affect human trust. Through a human factors lens, the present research builds on earlier HSwI work and investigates the effect of swarm asset degradations on trustworthiness perceptions, reliance intentions, and reliance behaviors. Results showed that trustworthiness perceptions of and intentions to rely on swarms (but not reliance behaviors) were correlated, demonstrating the relation between theoretically relevant antecedents to trust in HSwI contexts. Contrary to past work, the results showed no statistical evidence that asset degradations differentially affect trustworthiness perceptions, reliance intentions, or reliance behaviors. Limitations of the current work (e.g., heterogeneity of post-intervention foraging behavior, sample size) are discussed and followed with future research suggestions.

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“…While benevolence and integrity are not directly attributable to the technology, users are able to personify technology (Nass and Moon, 2000). Systems that parallel human-like characteristics and personas (i.e., humanoid robots, intelligent agents such as Alexa) tend to have more trust than systems designed with the same capacities and purpose, but with non-anthropomorphized characteristics (Hancock et al, 2011;de Visser et al, 2017;Calhoun et al, 2019). However, there is a point where extreme similarity between a technology and human can result in a significant drop in trust levels, often referred to as the uncanny valley (Flemisch et al, 2017).…”

Section: Basis Of Trust In Interpersonal Verses Technology Automationmentioning

confidence: 99%

Neural Correlates of Trust in Automation: Considerations and Generalizability Between Technology Domains

Hopko

Mehta

2021

Front. Neuroergonomics

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Investigations into physiological or neurological correlates of trust has increased in popularity due to the need for a continuous measure of trust, including for trust-sensitive or adaptive systems, measurements of trustworthiness or pain points of technology, or for human-in-the-loop cyber intrusion detection. Understanding the limitations and generalizability of the physiological responses between technology domains is important as the usefulness and relevance of results is impacted by fundamental characteristics of the technology domains, corresponding use cases, and socially acceptable behaviors of the technologies. While investigations into the neural correlates of trust in automation has grown in popularity, there is limited understanding of the neural correlates of trust, where the vast majority of current investigations are in cyber or decision aid technologies. Thus, the relevance of these correlates as a deployable measure for other domains and the robustness of the measures to varying use cases is unknown. As such, this manuscript discusses the current-state-of-knowledge in trust perceptions, factors that influence trust, and corresponding neural correlates of trust as generalizable between domains.

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Linking precursors of interpersonal trust to human-automation trust: An expanded typology and exploratory experiment

Cited by 41 publications

References 40 publications

A Review of Trust in Artificial Intelligence: Challenges, Vulnerabilities and Future Directions

A Review of Trust in Artificial Intelligence: Challenges, Vulnerabilities and Future Directions

Exploring the Effects of Swarm Degradations on Trustworthiness Perceptions, Reliance Intentions, and Reliance Behaviors

Neural Correlates of Trust in Automation: Considerations and Generalizability Between Technology Domains

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