机器人
意外后果
渲染(计算机图形)
感觉
感知
互联网隐私
心理学
服务(商务)
计算机科学
人机交互
社会心理学
业务
人工智能
营销
政治学
神经科学
法学
作者
Xiaoyu Chang,Yanheng Li,Sijia Liu,Ling Ma,RAY LC
标识
DOI:10.1145/3610977.3634959
摘要
Robots are increasingly deployed in crowded, large-scale environments where the demands on their services can outweigh their ability to respond. When robots fail to respond, humans may interpret the unintended consequence negatively as a form of rejection, leading to a loss of trust. How do service robots recover from such rejection to remediate human trust due to perceived rejection? We created a task mimicking shopping malls where the robot arm is asked to provide coffee, juice, or tea to participants. When the robot rendered service elsewhere, participants reported feeling excluded and less trusting of the robot. When the robot subsequently apologized or provided promise of future favor, participants regained trust in the robot, with favor rendering yielding significantly more trust responses. This study highlights the importance of understanding inadvertently negative consequences of robot behaviors, and suggests design solutions for overcoming this negative perception through remediation strategies.
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