可适应的自动化透明度:是否应为人类提供自我选择透明度信息的灵活性?

Adaptable Automation Transparency: Should Humans Be Provided Flexibility to Self-Select Transparency Information?

Human Factors The Journal of the Human Factors and Ergonomics Society · 2025
被引 1
ABS 3

中文导读

研究允许操作者自行选择自动化透明度水平(可适应透明度)是否能提高自动化使用准确性,发现可适应透明度并未改善使用效果,但高固定透明度可减少决策时间并提高准确性。

Abstract

ObjectiveWe examined whether allowing operators to self-select automation transparency level (adaptable transparency) could improve accuracy of automation use compared to nonadaptable (fixed) low and high transparency. We examined factors underlying higher transparency selection (decision risk, perceived difficulty).BackgroundIncreased fixed transparency typically improves automation use accuracy but can increase bias toward agreeing with automated advice. Adaptable transparency may further improve automation use if it increases the perceived expected value of high transparency information.MethodsAcross two studies, participants completed an uninhabited vehicle (UV) management task where they selected the optimal UV to complete missions. Automation advised the optimal UV but was not always correct. Automation transparency (fixed low, fixed high, adaptable) and decision risk were manipulated within-subjects.ResultsWith adaptable transparency, participants selected higher transparency on 41% of missions and were more likely to select it for missions perceived as more difficult. Decision risk did not impact transparency selection. Increased fixed transparency (low to high) did not benefit automation use accuracy, but reduced decision times. Adaptable transparency did not improve automation use compared to fixed transparency.ConclusionWe found no evidence that adaptable transparency improved automation use. Despite a lack of fixed transparency effects in the current study, an aggregated analysis of our work to date using the UV management paradigm indicated that higher fixed transparency improves automation use accuracy, reduces decision time and perceived workload, and increases trust in automation.ApplicationThe current study contributes to the emerging evidence-base regarding optimal automation transparency design in the modern workplace.

人机交互自动化系统决策支持透明度设计