Confidant: A Privacy Controller for Social Robots
Confidant: A Privacy Controller for Social Robots
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DOI：
10.1109/hri53351.2022.9889540
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Brian Tang;Dakota Sullivan;Bengisu Cagiltay;Varun Chandrasekaran;Kassem Fawaz;Bilge Mutlu
中科院分区：
文献类型：
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作者：
Brian Tang;Dakota Sullivan;Bengisu Cagiltay;Varun Chandrasekaran;Kassem Fawaz;Bilge Mutlu
随着社交机器人在日常环境中变得越来越普遍，它们将参与对话并适当管理与其共享的信息。然而，人们对机器人如何正确识别信息的敏感性知之甚少，这对人机信任具有重大影响。作为解决此问题的第一步，我们为会话社交机器人设计了一个隐私控制器 Confidant，能够使用会话中的上下文元数据（例如情绪、关系、主题）来建模隐私边界。随后，我们进行了两项众包用户研究。第一项研究 ($n=174$) 重点关注各种人与人之间的互动场景是否被视为私密/敏感或非私密/不敏感。我们第一项研究的结果用于生成关联规则。我们的第二项研究 ($n=95$) 通过将使用我们的隐私控制器的机器人与没有隐私控制的基线机器人进行比较，评估了隐私控制器在人机交互场景中的有效性和准确性。我们的结果表明，带有隐私控制器的机器人在隐私意识、可信度和社交意识方面优于没有隐私控制器的机器人。我们的结论是，将隐私控制器集成到真实的人机对话中可以让机器人变得更值得信赖。这个初始的隐私控制器将作为更复杂的解决方案的基础。
As social robots become increasingly prevalent in day-to-day environments, they will participate in conversations and appropriately manage the information shared with them. However, little is known about how robots might appropriately discern the sensitivity of information, which has major implications for human-robot trust. As a first step to address a part of this issue, we designed a privacy controller, Confidant, for conversational social robots, capable of using contextual metadata (e.g., sentiment, relationships, topic) from conversations to model privacy boundaries. Afterwards, we conducted two crowdsourced user studies. The first study ($n=174$) focused on whether a variety of human-human interaction scenarios were perceived as either private/sensitive or non-private/non-sensitive. The findings from our first study were used to generate association rules. Our second study ($n=95$) evaluated the effectiveness and accuracy of the privacy controller in human-robot interaction scenarios by comparing a robot that used our privacy controller against a baseline robot with no privacy controls. Our results demonstrate that the robot with the privacy controller outperforms the robot without the privacy controller in privacy-awareness, trustworthiness, and social-awareness. We conclude that the integration of privacy controllers in authentic human-robot conversations can allow for more trustworthy robots. This initial privacy controller will serve as a foundation for more complex solutions.