Trust of a Military Automated System in an Operational Context

Ho, Nhut; Sadler, Garrett; Hoffmann, Lauren; Lyons, Joseph B.; Johnson, Walter W.

doi:10.1037/mil0000189

Cited by 23 publications

(12 citation statements)

References 22 publications

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“…These effects have been found in laboratory contexts as well as studies of automation in the field. For instance, in studies of pilot trust in automation, nuisance avoidance (a performance element) was a prominent factor for developing trust [28]. One pilot in the study indicated that one mistake by the automation would likely cause pilots to turn the system off and lose the advantage it provided, which tracks with other similar research that found frequent false-alarm rates lead pilots to deactivate critical alarm systems [29].…”

Section: Trust Attributes and Progressionsupporting

confidence: 56%

“…Studies in the design of aerospace automation have found that mutual understanding and respect between operators, engineers, and program managers that feature different accountability, vulnerability, and expectations is important to success [28]. Operators, as the end users accountable for successful missions, are vulnerable to failure, and expect the automation to perform acceptably.…”

Section: Accountability Vulnerability and Expectationsmentioning

confidence: 99%

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Space Trusted Autonomy Readiness Levels

Hobbs¹,

Lyons²,

Feather³

et al. 2022

Preprint

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Technology Readiness Levels are a mainstay for organizations that fund, develop, test, acquire, or use technologies. Technology Readiness Levels provide a standardized assessment of a technology's maturity and enable consistent comparison among technologies. They inform decisions throughout a technology's development life cycle, from concept, through development, to use. A variety of alternative Readiness Levels have been developed, including Algorithm Readiness Levels, Manufacturing Readiness Levels, Human Readiness Levels, Commercialization Readiness Levels, Machine Learning Readiness Levels, and Technology Commitment Levels. However, while Technology Readiness Levels have been increasingly applied to emerging disciplines, there are unique challenges to assessing the rapidly developing capabilities of autonomy. This paper adopts the moniker of Space Trusted Autonomy Readiness Levels to identify a two-dimensional scale of readiness and trust appropriate for the special challenges of assessing autonomy technologies that seek space use. It draws inspiration from other readiness levels' definitions, and from the rich field of trust and trustworthiness.

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Section: Trust Attributes and Progressionsupporting

confidence: 56%

Section: Accountability Vulnerability and Expectationsmentioning

confidence: 99%

Space Trusted Autonomy Readiness Levels

Hobbs¹,

Lyons²,

Feather³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…system pedigree or heritage), simplified and understandable performance, system intent, and reliability [84]. In Auto GCAS transparency comes through pilot training before operations and through visual indicators on the aircraft's head-up-display (HUD) [82], [92]. A discussion of the difference between safety, security, and reliability is provided in the sidebar titled "Safety, Reliability, and Security."…”

Section: Transparency and Trustmentioning

confidence: 99%

Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems

Hobbs¹,

Mote²,

Abate³

et al. 2021

Preprint

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More than three miles above the Arizona desert, an F-16 student pilot experienced a gravityinduced loss of consciousness (GLOC), passing out while turning at nearly 9Gs (nine times the force of gravity) flying over 400 knots (over 460 miles per hour). With its pilot unconscious, the aircraft turn devolved into a dive, dropping from over 17,000 feet to less than 8,000 feet in altitude in less than 10 seconds. An auditory warning in the cockpit called out to the pilot "altitude, altitude" just before he crossed through 11,000 feet, switching to a command to "pull up" around 8,000 feet. Meanwhile, the student's instructor was watching the event unfold from his own aircraft. As the student's aircraft passed through 12,500 feet, the instructor called over the radio "two recover," commanding the student ("two") to end the dive. As the student's aircraft passed through 11,000 feet the instructor's "two recover!" came with increased urgency. At 9,000 feet, and with terror rising in his voice the instructor yelled "TWO RECOVER!" Fortunately, at the same time as the instructor's third panicked radio call, a new Run Time Assurance (RTA) system kicked in to automatically recover the aircraft. The Automatic Ground Collision Avoidance System (Auto GCAS), an RTA system integrated on the jets less than two years earlier in the Fall of 2014, detected that the aircraft was about to collide, commanded a roll to wings level and pull up maneuver, and recovered the aircraft less than 3,000 feet above the

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“…Individual differences is a key and well researched topic regarding trust and reliance in AS and managing this relationship, in which many factors have been found to mediate [4]. Examples of individual differences which have been shown to influence human trust in AS include personality traits [8]- [10], culture [11]- [13], mood [14], [15], and selfconfidence [16], [17]. The research discussed in this paper contributes to that literature to inform system designers when developing systems in human-AS collaborative tasks.…”

Section: Introductionmentioning

confidence: 99%

Generalised Anxiety Disorder and Depression on Implicit and Explicit Trust Tendencies Toward Automated Systems

2021

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This paper explores whether generalised anxiety disorder (GAD) and depression have any effect on an individual's explicit general propensity to trust automated systems (trust that is unspecific to any one automated system) and whether those that do have these disorders have an implicit bias towards automated systems over other humans. The human-automated system literature to date has discovered that individual differences in humans, such as self-confidence, mood, and personality types, can influence the human-automated system relationship through human trust and reliance attitudes and behaviour. However, whether suffering from a mental disorder influences an individual's attitudes towards automated systems generally is yet to be explored. In this study, 184 UK university students responded to online experiments between December 2019 -January 2020 and were subjected to the cultural trust instrument survey and the implicit association test in a between-subjects design to measure their general propensity to trust and implicit association towards automated systems respectively. A two-way ANOVA was performed to evaluate GAD × depression interaction effects on the dependent variables. Results suggest there was a significant interaction between GAD and depression regarding propensity to trust automated systems but they have little to no influential effect on mean implicit association test scores. Furthermore, those without depression showed a significantly higher trust score when they also had GAD. It can be concluded that GAD and depression have potential critical influence over human-automated system trust, thus creating potential issues with misuse and disuse and must be accounted for in automated system design.

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Trust of a Military Automated System in an Operational Context

Cited by 23 publications

References 22 publications

Space Trusted Autonomy Readiness Levels

Space Trusted Autonomy Readiness Levels

Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems

Generalised Anxiety Disorder and Depression on Implicit and Explicit Trust Tendencies Toward Automated Systems

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