Automation trust increases under high-workload multitasking scenarios involving risk

Satô, Tetsuya; Yamani, Yusuke; Liechty, Molly; Chancey, Eric T.

doi:10.1007/s10111-019-00580-5

Cited by 23 publications

(22 citation statements)

References 41 publications

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“…Objective performance criteria for monitoring tasks often include reaction time, detection rate (correctly identifying errors made by the automated agent), and false alarms (incorrectly identifying an error when none was made; Karpinsky et al, 2018; Molloy & Parasuraman, 1996; Sato et al, 2019). Subjective measures of mind-wandering frequency during performance also are used as criterion measures of monitoring and sustained attention tasks (e.g., Casner & Schooler, 2015).…”

Section: Identification Of Factors Affecting Aptitude Requirementsmentioning

confidence: 99%

Aptitude Requirements for Human Operators in Human–Automation Interaction

Brady¹,

Carretta

Shermadou³

et al. 2022

Aviation Psychology and Applied Human Factors

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Abstract. The purpose of this work was to identify individual differences that affect aptitude requirements for jobs involving autonomous systems and human–automation interaction (HAI). This was addressed in two stages. First, we conducted a literature review of task demands and operator states relevant to HAI. On the basis of this review, we formed a model for understanding performance as a composite of operator states, operator behaviors, and distal outcomes. Second, we conducted a meta-analysis of correlations between individual differences and criteria reflecting job demands of an HAI environment. Results suggest cognitive skills such as working memory are important to performance in an HAI context. Inconsistent findings for personality across studies underscore the need for more research. Measurement challenges and research gaps are identified.

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Section: Identification Of Factors Affecting Aptitude Requirementsmentioning

confidence: 99%

Aptitude Requirements for Human Operators in Human–Automation Interaction

Brady¹,

Carretta

Shermadou³

et al. 2022

Aviation Psychology and Applied Human Factors

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“…Moreover, Sato et al (2020) did not find a main effect of their risk manipulation on trust attitude toward their IA. Similarly, we do not expect differences in trust attitude.…”

Section: Overall Effectsmentioning

confidence: 62%

“…Evidence concerning its effects on trust is therefore rather scarce. Concerning trust attitude, Sato et al (2020) investigated the effects of risk on task performance, attention allocation and trust attitude when working with an IA. They operationalized risk between groups by either claiming that poor performance will result in a repetition of the experiment (high risk) or giving no such information (low risk).…”

Section: Situational Riskmentioning

confidence: 99%

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The effect of risk on trust attitude and trust behavior in interaction with information and decision automation

Hoesterey

Onnasch

2022

Cogn Tech Work

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Situational risk has been postulated to be one of the most important contextual factors affecting operator’s trust in automation. However, experimentally, it has received only little attention and was directly manipulated even less. To close this gap, this study used a virtual reality multi-task environment where the main task entailed making a diagnosis by assessing different parameters. Risk was manipulated via the altitude, the task was set in including the possibility of virtually falling in case of a mistake. Participants were aided either by information or decision automation. Results revealed that trust attitude toward the automation was not affected by risk. While trust attitude was initially lower for the decision automation, it was equally high in both groups at the end of the experiment after experiencing reliable support. Trust behavior was significantly higher and increased during the experiment for the decision automation supported group in the form of less automation verification behavior. However, this detrimental effect was distinctly attenuated under high risk. This implies that negative consequences of decision automation in the real world might have been overestimated by studies not incorporating risk.

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“…and internal factors (affect, expertise, attentional capacity, etc.) [42]. Further, Meyerson et al point to trust being affected by the temporal context in a group (whether a group formation is more temporary or permanent) [43].…”

Section: Expanding Trustmentioning

confidence: 99%

Trust in Clinical AI: Expanding the Unit of Analysis

Browne

Bakker

et al. 2022

HHAI2022: Augmenting Human Intellect

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From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.

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Automation trust increases under high-workload multitasking scenarios involving risk

Cited by 23 publications

References 41 publications

Aptitude Requirements for Human Operators in Human–Automation Interaction

Aptitude Requirements for Human Operators in Human–Automation Interaction

The effect of risk on trust attitude and trust behavior in interaction with information and decision automation

Trust in Clinical AI: Expanding the Unit of Analysis

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