CICI: UCSS: Maximizing Data Utility and Participant Privacy through Usable, Secure Data Workflows for Human-Centered AI Research

改善隐私的一个好方法是收集更少的数据。然而，所有使用人工智能(AI)的系统，其中许多人每天都依赖于(例如，搜索引擎、导航应用、欺诈检测等)，都需要数据来工作。该项目平衡了获取数据以改进人工智能技术和最大限度地保护为研究研究贡献个人信息的人的隐私之间的竞争需求。该项目与人工智能研究社区合作，开发了一个原型系统，以帮助人工智能研究人员保护收集的关于人的数据，同时提高他们做出新科学发现的能力。具体地说，原型系统将生成隐私增强的人口统计问题，并建议数据收集计划，以确保收集的关于人的数据平衡对统计准确性和社区代表性的需求。通过这样做，这个系统通过减少收集的关于人的数据量，并帮助创建可用的、公平的、准确的和值得信任的人工智能系统，提高了以人为中心的人工智能研究的质量、安全和效率。设计人类受试者研究，在保护研究参与者的隐私和安全的同时，仍然产生稳健的结果是棘手的。该项目利用数据最小化等网络安全技术，帮助以人为中心的人工智能研究人员更好地保护研究参与者的隐私，同时确保他们研究的统计能力和普适性。该项目与以人为中心的人工智能研究社区合作，建立了一个可用的工具链，用于生成数据最小化的人口调查问题，并确定统计上强大的研究样本量和人口统计学上的多样化组成，以确保研究结果的完整性。该系统最大限度地保护了人类受试者的隐私，并建议了平衡统计能力和代表性的样本量和组成。这种方法从研究设计的角度提升了科学发现的质量。通过与人工智能研究人员一起发现需求、迭代工具链完善和可用性测试的过程，该项目构建了一个更安全、更高效、更健壮的以人为中心的人工智能研究系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

A good way to improve privacy is to collect less data. However, all systems using artificial intelligence (AI), many of which people rely upon everyday (e.g., search engines, navigation apps, fraud detection, etc.), need data to work. This project balances the competing needs of obtaining data to improve AI technologies and maximizing the privacy of people contributing their personal information to research studies. Working in conjunction with the AI research community, this project develops a prototype system to help AI researchers secure data collected about people while at the same time improving their ability to make new scientific discoveries. Specifically, the prototype system will generate privacy-enhanced demographic questions and suggest data collection plans that ensure data collected about people balances needs for statistical rigor and community representativeness. In doing so, this system improves the quality, security, and efficiency of human-centered AI research by reducing the amount of data collected about people and helping to create AI systems that are usable, fair, accurate, and trustworthy.Designing human subjects studies that preserve research participants' privacy and security while still generating robust results is tricky. This project leverages cybersecurity techniques such as data-minimization to help human-centered AI researchers better protect research participants' privacy while ensuring their studies' statistical power and generalizability. In collaboration with the human-centered AI research community, this project builds a usable toolchain for generating data-minimizing demographic survey questions and determining statistically well-powered study sample size and demographically diverse composition to ensure the integrity of research results.The system maximizes the privacy of human subjects and recommends a sample size and composition which balances statistical power and representativeness. This approach promotes the quality of scientific discoveries at the point of study design. Through a process of need-finding, iterative toolchain refinement, and usability testing with AI researchers, this project builds a system for more secure, efficient, and robust human-centered AI research.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.