论文标题
知己：社会机器人的隐私控制器
CONFIDANT: A Privacy Controller for Social Robots
论文作者
论文摘要
随着社交机器人在日常环境中变得越来越普遍，他们将参与对话并适当管理与他们共享的信息。但是，关于机器人如何适当地辨别信息的敏感性，这对人类机器人信任具有重大影响。作为解决此问题一部分的第一步，我们为会话社会机器人设计了一个隐私控制器，即能够使用上下文元数据（例如，情感，关系，主题），从对话到模型隐私边界。之后，我们进行了两次众包用户研究。第一项研究（n = 174）的重点是将各种人类相互作用方案视为私人/敏感或非私有/非敏感的情况。我们的第一项研究的发现用于生成关联规则。我们的第二项研究（n = 95）通过比较使用我们的隐私控制器与没有隐私控制的基线机器人的机器人进行比较，评估了隐私控制器在人机交互情况下的有效性和准确性。我们的结果表明，带有隐私控制器的机器人在隐私意识，可信度和社会意识方面优于无隐私控制器的机器人。我们得出的结论是，在真实的人类机器人对话中，隐私控制器的整合可以允许更具值得信赖的机器人。最初的隐私控制器将成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.
