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

CICI：UCSS：通过可用、安全的数据工作流程实现以人为本的人工智能研究，最大限度地提高数据效用和参与者隐私

• 批准号: 2232690
• 负责人: Kelly Caine
• 金额: $ 60万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232690&HistoricalAwards=false
• 关键词: CICI; UCSS; Maximizing; Data; Utility

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.

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（其中许多是人们日常依赖的系统）（例如搜索引擎、导航应用程序、欺诈检测等）都需要数据才能运行。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贡献个人信息的人们的隐私的竞争需求。该项目与人工智能研究社区合作，开发了一个原型系统，帮助人工智能研究人员保护收集到的有关人类的数据，同时提高他们做出新科学发现的能力。具体来说，原型系统将生成增强隐私的人口统计问题，并提出数据收集计划，以确保收集的有关人们的数据平衡统计严谨性和社区代表性的需求。在此过程中，该系统通过减少收集的有关人类的数据量，并帮助创建可用、公平、准确和值得信赖的人工智能系统，提高了以人为中心的人工智能研究的质量、安全性和效率。 设计人类受试者研究保护研究参与者的隐私和安全，同时仍然产生可靠的结果是很棘手的。该项目利用数据最小化等网络安全技术，帮助以人为本的人工智能研究人员更好地保护研究参与者的隐私，同时确保其研究的统计效力和普遍性。该项目与以人为中心的人工智能研究社区合作，构建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，并确定统计上有力的研究样本量和人口多样化的构成，以确保研究结果的完整性。人类受试者的隐私，并建议平衡统计功效和代表性的样本量和组成。这种方法在研究设计方面提高了科学发现的质量。通过与人工智能研究人员一起进行需求发现、迭代工具链完善和可用性测试，该项目构建了一个系统，以实现更安全、更高效、更稳健的以人为本的人工智能研究。该奖项反映了 NSF 的法定使命，并被认为是值得的。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来提供支持。