Overtrust of Robots in High-Risk Scenarios
Overtrust of Robots in High-Risk Scenarios
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高风险场景下对机器人的过度信任
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Jin Xu
中科院分区：
文献类型：
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作者：
Jin Xu
从个人机器人助手到自动驾驶汽车，人工智能（AI）是未来数百万高级应用的基础。随着机器人在日常生活中越来越普遍，预计在不久的将来，机器人将在许多领域增加人类劳动力。当机器人部署在真实的世界中时，基本的假设是它们能够完成给定的任务。然而，研究人员已经表明，机器人会犯错误，在某些情况下，人类倾向于过度信任机器人系统（Abney 2017; Borenstein et al. 2017; Robinette，霍华德，and瓦格纳2017）。机器人的过度信任发生在以下场景中：（1）一个人接受风险，因为这个人相信机器人可以执行它不能执行的功能，或者（2）这个人接受太多的风险，因为期望系统会减轻风险。（Abney 2017）。特别是，我们对两个新兴领域感兴趣，其中适度的信任是最低要求，过度信任可能会造成伤害：1）医疗保健场景和2）自动驾驶汽车（即自动驾驶）场景。医疗保健和自动驾驶场景通常都涉及高风险，负面结果可能对用户有害。我们的研究目标集中在1）调查导致人类对这些机器人系统过度信任的原因2）开发基于行为的计算模型来预测过度信任，以及3）开发减轻过度信任造成的后果的技术。
From personal robot assistant to self-driving vehicles, artificial intelligence (AI) is the backbone underlying millions of future advanced applications. As robots become increasingly pervasive in daily life, it is expected that robots will augment human laborers in many domains in the near future. When robots are deployed in the real world, the underlying assumption is that they are capable of accomplishing their given tasks. However, researchers have shown that robots made mistakes, and in several cases, humans tend to overtrust robotic systems (Abney 2017; Borenstein et al. 2017; Robinette, Howard, and Wagner 2017). Overtrust of a robot happens in scenarios where (1) a person accepts risk because that person believes the robot can perform a function that it cannot or (2) the person accepts too much risk because the expectation is that the system will mitigate the risk." (Abney 2017). In particular, we are interested in two emerging domains where an appropriate amount of trust is a minimal requirement and overtrust could cause harm: 1) healthcare scenarios and 2) self-driving car (i.e. autonomous driving) scenarios. Both healthcare and autonomous driving scenarios often involve high risks, and the negative outcomes could be detrimental to the user. The objective of our research focuses on 1) investigating the causes that contribute to human overtrust of these robots systems 2) developing a behavior-based computational model to predict overtrust, and 3) developing techniques to mitigate outcomes caused by the overtrust.