Transparent Automated Advice to Mitigate the Impact of Variation in Automation Reliability.

IF 2.9 3区 心理学 Q1 BEHAVIORAL SCIENCES
Human Factors Pub Date : 2024-08-01 Epub Date: 2023-08-27 DOI:10.1177/00187208231196738
Isabella Gegoff, Monica Tatasciore, Vanessa Bowden, Jason McCarley, Shayne Loft
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引用次数: 0

Abstract

Objective: To examine the extent to which increased automation transparency can mitigate the potential negative effects of low and high automation reliability on disuse and misuse of automated advice, and perceived trust in automation.

Background: Automated decision aids that vary in the reliability of their advice are increasingly used in workplaces. Low-reliability automation can increase disuse of automated advice, while high-reliability automation can increase misuse. These effects could be reduced if the rationale underlying automated advice is made more transparent.

Methods: Participants selected the optimal UV to complete missions. The Recommender (automated decision aid) assisted participants by providing advice; however, it was not always reliable. Participants determined whether the Recommender provided accurate information and whether to accept or reject advice. The level of automation transparency (medium, high) and reliability (low: 65%, high: 90%) were manipulated between-subjects.

Results: With high- compared to low-reliability automation, participants made more accurate (correctly accepted advice and identified whether information was accurate/inaccurate) and faster decisions, and reported increased trust in automation. Increased transparency led to more accurate and faster decisions, lower subjective workload, and higher usability ratings. It also eliminated the increased automation disuse associated with low-reliability automation. However, transparency did not mitigate the misuse associated with high-reliability automation.

Conclusion: Transparency protected against low-reliability automation disuse, but not against the increased misuse potentially associated with the reduced monitoring and verification of high-reliability automation.

Application: These outcomes can inform the design of transparent automation to improve human-automation teaming under conditions of varied automation reliability.

减轻自动化可靠性差异影响的透明自动建议。
目的:研究提高自动化透明度在多大程度上可以减轻低自动化可靠性和高自动化可靠性对滥用和误用自动化建议以及自动化信任感的潜在负面影响:研究提高自动化透明度在多大程度上可以减轻低自动化可靠性和高自动化可靠性对不使用和滥用自动化建议以及对自动化的感知信任的潜在负面影响:背景:工作场所越来越多地使用自动决策辅助工具,这些工具提供的建议可靠性各不相同。低可靠性自动化会增加自动建议的废弃率,而高可靠性自动化则会增加误用率。如果自动建议的基本原理更加透明,就可以减少这些影响:方法:参与者选择最佳UV来完成任务。推荐器(自动决策辅助工具)通过提供建议来帮助参与者,但它并不总是可靠的。参与者决定推荐器是否提供了准确的信息,以及是否接受或拒绝建议。自动化透明度(中、高)和可靠性(低:65%,高:90%)在被试之间进行了调节:结果:与低可靠性自动化相比,高可靠性自动化使参与者做出的决策更准确(正确接受建议并识别信息是否准确/不准确)、更快速,并表示对自动化的信任度有所提高。透明度的提高使决策更准确、更迅速,主观工作量更低,可用性评分更高。它还消除了与低可靠性自动化相关的自动化滥用现象。然而,透明度并没有减少与高可靠性自动化相关的滥用现象:结论:透明度能防止低可靠性自动化的滥用,但不能防止与高可靠性自动化的监控和验证减少相关的滥用增加:这些结果可以为透明自动化的设计提供参考,从而在不同自动化可靠性条件下改善人机协作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Human Factors
Human Factors 管理科学-行为科学
CiteScore
10.60
自引率
6.10%
发文量
99
审稿时长
6-12 weeks
期刊介绍: Human Factors: The Journal of the Human Factors and Ergonomics Society publishes peer-reviewed scientific studies in human factors/ergonomics that present theoretical and practical advances concerning the relationship between people and technologies, tools, environments, and systems. Papers published in Human Factors leverage fundamental knowledge of human capabilities and limitations – and the basic understanding of cognitive, physical, behavioral, physiological, social, developmental, affective, and motivational aspects of human performance – to yield design principles; enhance training, selection, and communication; and ultimately improve human-system interfaces and sociotechnical systems that lead to safer and more effective outcomes.
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