研究家庭健康机器人与用户记忆冲突时,透明度如何影响信任与决策。
"Who Should I Believe?": User Interpretation and Decision-Making When a Family Healthcare Robot Contradicts Human Memory
- 通过改变机器人透明度与亲和力,观察用户对信息矛盾的解读方式。
- 高透明度下用户更倾向认为是他人修改了机器人设定,而非自己记错。
- 用户普遍过度信任机器人,即使怀疑系统故障仍优先听从其建议。
智能医疗机器人在家庭环境中提供物理协助、心理支持和日常健康管理的能力不断提升,使其部署日益可行。然而,当机器人提供的信息与用户记忆冲突时,会引发用户信任与决策问题。本研究通过2×2组间在线实验(176名参与者)考察机器人透明度与亲和力如何影响用户对信息冲突的解释、决策及感知信任。参与者观看由Furhat机器人扮演家庭医疗助手的视频,该机器人建议虚构用户在与用户记忆不同的时间服药。结果表明,低透明度机器人使用户更倾向于认为自己记错了时间;而高透明度机器人则促使用户将差异归因于外部因素,如伴侣或其他家庭成员修改了机器人设置。此外,用户表现出过度信任倾向,即使怀疑系统故障或第三方干预,也常优先采纳机器人建议。研究揭示了透明度机制的重要性,强调多用户家庭环境中系统访问控制的复杂性,并警示在医疗等敏感领域中用户对机器人过度依赖的风险。
原文摘要 · Abstract (English)
Advancements in robotic capabilities for providing physical assistance, psychological support, and daily health management are making the deployment of intelligent healthcare robots in home environments increasingly feasible in the near future. However, challenges arise when the information provided by these robots contradicts users' memory, raising concerns about user trust and decision-making. This paper presents a study that examines how varying a robot's level of transparency and sociability influences user interpretation, decision-making and perceived trust when faced with conflicting information from a robot. In a 2 x 2 between-subjects online study, 176 participants watched videos of a Furhat robot acting as a family healthcare assistant and suggesting a fictional user to take medication at a different time from that remembered by the user. Results indicate that robot transparency influenced users' interpretation of information discrepancies: with a low transparency robot, the most frequent assumption was that the user had not correctly remembered the time, while with the high transparency robot, participants were more likely to attribute the discrepancy to external factors, such as a partner or another household member modifying the robot's information. Additionally, participants exhibited a tendency toward overtrust, often prioritizing the robot's recommendations over the user's memory, even when suspecting system malfunctions or third-party interference. These findings highlight the impact of transparency mechanisms in robotic systems, the complexity and importance associated with system access control for multi-user robots deployed in home environments, and the potential risks of users' over reliance on robots in sensitive domains such as healthcare.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。