Trust of an Automatic Ground Collision Avoidance Technology: A Fighter Pilot Perspective

Lyons, Joseph B.; Ho, Nhut; Fergueson, William E.; Sadler, Garrett; Cals, Samantha D.; Richardson, C.; Wilkins, Mark

doi:10.1037/mil0000124

Cited by 28 publications

(22 citation statements)

References 18 publications

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“…Research has demonstrated the influence of transparency on trust calibration. Lyons [24] found increased transparency on the automatic ground collision avoidance system (Auto-GCAS) greatly increased trust in the system and the use of the system. Transparency has also been related to trustworthiness perceptions in websites, such that websites that were more readable and easily accessible were viewed as higher in trustworthiness [25].…”

Section: Figure 1 Trust Calibration Adapted From Lee and See [22]mentioning

confidence: 99%

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Trustworthiness Perceptions of Computer Code: A Heuristic-Systematic Processing Model

Alarcon¹,

Ryan²

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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The popularity of code reuse and the prospect of computer generated code raises questions of how programmers trust in computer code. Psychological understanding of computer code perceptions and comprehension has yet to be explored in regards to the decision making processes involved with software development and reuse practices. A review of current literature on trust, automation, software reuse, and the intersection of the three is presented. The authors propose a theoretical model of this decision making process, building off of a heuristic-systematic model of persuasion. Future research directions and possible applications are discussed.Trust has been called a core social motive [6]. Research on interpersonal trust extends back to Rotter's [7] seminal research on trust. Trust, according to Rotter, is defined as the generalized expectancy that

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Section: Figure 1 Trust Calibration Adapted From Lee and See [22]mentioning

confidence: 99%

“…The appropriate level of transparency depends on the user's requirements. Research on systems such as the auto-GCAS has explored transparency in the end user [24]. However, transparency requirements are different depending on how one interacts with the systems.…”

Section: Figure 1 Trust Calibration Adapted From Lee and See [22]mentioning

confidence: 99%

Trustworthiness Perceptions of Computer Code: A Heuristic-Systematic Processing Model

Alarcon¹,

Ryan²

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…While a significant amount of research has focused on techniques, efficiency and quality concerns for APR (see [32,66] for surveys), we focus attention on human judgments of trust in machine-generated repairs. Existing work has investigated the human trust process in automation [81], covering various aspects such as analyzing the links between user personality and perceptions of x-ray screening tasks [65] or personal factors in ground collision avoidance software [59]. However, little research has investigated APR from human factors perspectives [81].…”

Section: Trust and Automated Program Repairmentioning

confidence: 99%

Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

Huang

Leach

Sharafi

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Code review is a critical step in modern software quality assurance, yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code, but no consensus understanding has emerged. Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behavioral, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias. CCS CONCEPTS • Software and its engineering → Collaboration in software development; • Human-centered computing → Empirical studies in collaborative and social computing.

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“…Trust has traditionally been thought of as strictly an interpersonal process. However, recent interest has extended trust research to automation [14], trust in robots [15], and perceptions of trustworthiness of computer code [8,16]. Research in the computer science literature has explored aspects of the trust process, although the research was not labeled as such.…”

Section: Trustmentioning

confidence: 99%

“…Research on propensity to trust indicates general expectancies influence trust intentions. Specifically, people higher in propensity to trust are more likely to trust automation in experimental [30] and real-world settings [14]. However, propensity to trust is less contextually influential as the trustor becomes more familiar with the referent [12,28].…”

Section: Personalitymentioning

confidence: 99%

The Influence of Personality on Code Reuse

Ryan¹,

Walter²,

Alarcon³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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The ubiquity and necessity of computer software requires programmers to reuse extant code to keep up with increasing software demands. Researchers have started to investigate the underlying psychological processes and the programmer characteristics affecting code reuse. The present study investigated the role of programmer personality (propensity to trust, suspicion propensity) on willingness to reuse code. Programmers were recruited through Amazon's Mechanical Turk. Programmers completed propensity to trust and suspicion personality inventories and were subsequently presented with 18 pieces of computer code containing transparency and reputation manipulations. The results demonstrated that propensity to trust did not influence willingness to reuse code. However, facets of suspicion propensity did affect reuse willingness. Programmers lower in trait mal-intent perceptions and higher in cognitive activity were more likely to report they would reuse code. Implications and applications are discussed.

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Trust of an Automatic Ground Collision Avoidance Technology: A Fighter Pilot Perspective

Cited by 28 publications

References 18 publications

Trustworthiness Perceptions of Computer Code: A Heuristic-Systematic Processing Model

Trustworthiness Perceptions of Computer Code: A Heuristic-Systematic Processing Model

Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

The Influence of Personality on Code Reuse

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