SaTC: CORE: Medium: Designing Privacy-Aware Social Companion Robots

社交伴侣机器人可以用类似人类的方式与人互动，使用语音、手势和物理存在。他们的目的是帮助家庭进行饮食计划、儿童发展和交流等活动。然而，它们先进的感知和推理能力可能会导致隐私问题，因为它们可以收集、推断和共享有关其用户的个人信息。例如，当他们在家里移动和行动时，他们可以偷听私人谈话。它们还可以进入敏感环境，比如卧室，并且可以被设计成看起来像熟悉的物体或人，这可能导致人们过度分享个人信息。他们也可能不恰当地向其他住户或访客透露他们了解到的信息。因此，设计更多具有“隐私意识”的社交伴侣机器人是至关重要的，这些机器人可以根据情境和人们对隐私的需求，判断何时收集和共享数据，何时不收集和共享数据。这个跨学科项目旨在为具有隐私意识的社交机器人创建设计原则，以改善家庭环境中的人机交互。这些原则将为家庭、工作场所、学校和医疗保健中如何设计、使用和接受机器人提供指导。此外，通过外展活动，该项目将通过媒体报道、研讨会和办公时间提高家庭对智能环境中隐私的理解。该项目的教育活动将在隐私系统、家庭研究和人机交互方面培训K-12和大学生。最后，项目团队将根据工作共享代码、出版物、原型和数据集。本项目分四个阶段为人机交互开发、实现和评估一个新的隐私框架。该框架模拟了用户的隐私期望，采用了基于人机交互的新概念，开发了新的方法来帮助用户管理自己的隐私，并使家庭内部的隐私动态研究成为可能。具体而言，第一阶段模拟了用户对社交机器人的隐私关注和期望，开发了一个隐私框架，以确定隐私意识最重要的日常情况。第二阶段将框架作为隐私控制器实例化，用于捕捉社交隐私动态的机器人动作。隐私控制器的核心是一个可定制的架构，该架构基于从机器人环境中提取的高级上下文因素（如房屋位置和在场人数）生成隐私感知动作。第三阶段探索机器人如何利用隐私控制器架构向用户发出信号、演示或解释机器人的隐私感知动作。通过部署研究，第四阶段评估隐私意识对机器人参与家庭生活时复杂隐私权衡管理的影响。这些研究的结果将为理解具有隐私意识的社交机器人对家庭动态的好处、影响和限制提供经验基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Social companion robots can interact with people in a human-like way, using speech, gestures, and physical presence. They are designed to help families with activities like meal planning, child development, and communication. However, their advanced sensing and reasoning capabilities can lead to privacy concerns, as they can collect, infer, and share personal information about their users. For instance, they can overhear private conversations as they move and act in the home. They also access sensitive environments, such as bedrooms, and can be designed to look like familiar objects or people, which can lead to people oversharing personal information. They might also inappropriately disclose information they learned with other home occupants or visitors. Therefore, it is crucial to design more “privacy-aware” social companion robots that can reason about when to collect and share data, and when not to, in ways that are context-appropriate and match people’s needs for privacy. This interdisciplinary project aims to create design principles for privacy-aware social robots to improve human-robot interactions in home environments. These principles will inform how robots are designed, used, and accepted in homes, workplaces, schools, and healthcare. Further, through outreach activities, the project will improve families’ understanding of privacy in smart environments through media coverage, workshops, and office hours. The educational activities of the project will train K-12 and college students in privacy systems, family studies, and human-computer interaction. Finally, the project team will share code, publications, prototypes, and datasets based on the work.This project develops, implements, and evaluates a novel privacy framework for human-robot interaction in four phases. This framework models users' privacy expectations, employs novel concepts grounded in human-robot interaction, develops new methods to help users manage their own privacy, and enables a study of privacy dynamics within families. Specifically, Phase I models users' privacy concerns and expectations regarding social robots, developing a privacy framework to identify everyday situations in which privacy awareness is most important. Phase II instantiates the framework as a privacy controller for the robot’s actions that captures the dynamics of social privacy. At the core of the privacy controller is a customizable architecture that generates privacy-aware actions based on extracted high-level contextual factors from the robot's environment, such as location in the house and number of people present. Phase III explores how the robot can leverage the privacy controller architecture to signal, demonstrate, or explain the robot's privacy-aware actions to users. Through deployment studies, phase IV assesses the impact of privacy awareness on the robot's management of the complex privacy trade-offs involved in participation in family life. The findings of these studies will provide an empirical basis for understanding a privacy-aware social robot's benefits, effects, and limitations on family dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.