Evaluating Effects of Enhanced Autonomy Transparency on Trust, Dependence, and Human-Autonomy Team Performance over Time
Evaluating Effects of Enhanced Autonomy Transparency on Trust, Dependence, and Human-Autonomy Team Performance over Time
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随着时间的推移，评估增强的自主透明度对信任、依赖和人类自主团队绩效的影响
DOI：
10.1080/10447318.2022.2097602
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Yang, X. Jessie
中科院分区：
文献类型：
--
作者：
Luo, Ruikun;Du, Na;Yang, X. Jessie
随着自主系统变得越来越复杂，人类可能很难破译自动生成的解决方案，并越来越多地将自主视为一个神秘的黑匣子。缺乏透明度导致了对自主权的信任缺失和团队绩效的次优。针对这一问题，研究人员提出了各种方法来提高自治透明度，并评估了透明度的提高如何影响人们的信任和人类自治团队的绩效。然而，大多数先前的研究在实验结束时测量信任，并在实验中的所有试验中平均行为和绩效指标，但忽略了这些变量的时间动态。我们对自治透明度如何影响信任、依赖和绩效的理解很少。因此，本研究的目的是填补差距，并检查这种时间动态。我们开发了一个游戏Treasure Hunterwhich一个人发现一个地图的宝藏的帮助下，从一个智能助理。智能助手会建议人类下一步应该去哪里。每个建议背后的基本原理可以在显式列出选项空间的显示中传达（即，所有可能的动作）以及为什么特定动作在给定上下文中是最合适的。来自28名参与者的人在回路实验的结果表明，通过显示器传达智能助理的决策原理，参与者的信任显着增加，并随着时间的推移变得更加校准。使用显示器还导致对来自智能代理的推荐的更高接受度。
As autonomous systems become more complicated, humans may have difficulty deciphering autonomy-generated solutions and increasingly perceive autonomy as a mysterious black box. The lack of transparency contributes to the lack of trust in autonomy and suboptimal team performance. In response to this concern, researchers have proposed various methods to enhance autonomy transparency and evaluated how enhanced transparency could affect the people’s trust and the human-autonomy team performance. However, the majority of prior studies measured trust at the end of the experiment and averaged behavioral and performance measures across all trials in an experiment, yet overlooked the temporal dynamics of those variables. We have little understanding of how autonomy transparency affects trust, dependence, and performanceover time. The present study, therefore, aims to fill the gap and examine such temporal dynamics. We develop a gameTreasure Hunterwherein a human uncovers a map for treasures with the help from an intelligent assistant. The intelligent assistant recommends where the human should go next. The rationale behind each recommendation could be conveyed in a display that explicitly lists the option space (i.e., all the possible actions) and the reason why a particular action is the most appropriate in a given context. Results from a human-in-the-loop experiment with 28 participants indicate that by conveying the intelligent assistant’s decision-making rationale via the display, participants’ trust increases significantly and becomes more calibrated over time. Using the display also leads to a higher acceptance of recommendations from the intelligent agent.
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