SaTC: CORE: Medium: Collaborative: Automatically Answering People's Privacy Questions

随着新技术收集越来越多的关于我们的数据，人们无法跟上并控制他们的数据会发生什么。目前法律上对隐私的态度集中在“通知和选择”的概念上，即期望向人们提供有关其数据收集和使用的充分信息，并为这些做法提供有意义的选择（例如，选择退出或选择加入）。这种方法的一个主要元素依赖于隐私策略来将这些信息传达给人们。在实践中，这些政策往往是冗长、模糊和模棱两可的。不出所料，很少有人有时间去读它们，而那些读了的人往往很难理解它们在说什么。这个多学科项目旨在开发一种新技术，使人们能够通过简单地询问与他们有关的隐私问题，而不是要求他们阅读冗长的、千篇一律的隐私政策，从而重新获得控制感。除了产生新的知识和技术并为改善美国的隐私状况做出贡献外，该项目还将为参与大学的本科生和研究生创造教育和研究机会，包括扩大妇女和代表性不足的少数民族在这一重要计算机科学领域的参与活动。并促进技术的发展，有可能帮助视障人士利用隐私政策文本中的信息。这个多学科项目建立在自然语言处理、机器学习、代码分析和用户建模的最新进展基础上，重新发明通知和选择，从冗长且难以理解的通知转向与用户的交互式隐私对话。这项研究的一个重要部分涉及到问题回答功能的开发，使用户能够就那些对他们真正重要的问题提出问题，而不是向他们提供千篇一律的隐私通知。另一个涉及补充隐私政策中发现的披露信息的额外信息来源，如背景知识（例如关于常见数据实践和相关法律的知识）和代码分析，以消除声明的歧义，并在重要时向用户提供额外的详细信息（例如，他们的数据实际上与谁共享）。这项研究将以以用户为中心的设计方法为指导，在这种方法中，新技术的设计将由人类受试者研究的结果提供信息，并在日益丰富和现实的场景中部署和评估技术。本研究的产品将包括原型隐私问答功能，以及从隐私政策文本和代码中自动提取有关数据收集和使用实践信息的技术。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。

As novel technologies collect increasingly large and diverse amounts of data about us, people are unable to keep up and retain control over what happens to their data. The current legal approach to privacy concentrates on the concept of "Notice and Choice", namely the expectation that people are provided sufficient information about the collection and use of their data, and are offered meaningful choices about these practices (e.g., opt out, opt in). A primary element of this approach relies on privacy policies to communicate this information to people. In practice, these policies tend to be long, vague and ambiguous. Not too surprisingly, few people find the time to read them and those who do often struggle to understand what they say. This multi-disciplinary project aims to develop novel technology that will enable people to regain a sense of control by enabling them to simply ask questions about the privacy issues that matter to them rather than requiring them to read long, one-size-fits all privacy policies. In addition to producing new knowledge and technologies and contributing to improving the state of privacy in the United States, this project will also create education and research opportunities for both undergraduate and graduate students at participating universities, including activities to broaden participation of women and under-represented minorities in this important area of computer science, and contribute to the development of technologies with the potential to help the visually impaired take advantage of information found in the text of privacy policies.This multi-disciplinary project builds on recent advances in natural language processing, machine learning, code analysis and user modeling to re-invent notice and choice, moving from long and hard-to-understand notices to interactive privacy dialogues with users. An important part of this research involves the development of question answering functionality that enables users to ask questions about those issues that truly matter to them rather than presenting them with one-size-fits-all privacy notices. Another involves supplementing disclosures found in privacy policies with additional sources of information such as background knowledge (e.g. knowledge about common data practices and relevant laws) and code analysis to disambiguate statements and provide additional details to users when it matters (e.g., with whom their data is actually shared). This research will be guided by user-centered design methodologies where the design of novel technologies is informed by findings from human subject studies, and where technologies are deployed and evaluated in increasingly rich and realistic scenarios. Products of this research will include prototype privacy Question Answering functionality, as well as technology to automatically extract information about data collection and use practices from both the text of privacy policies and from code.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.