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.

提高隐私的一种好方法是收集更少的数据。但是，所有使用人工智能（AI）的系统，其中许多人每天都依靠这些系统（例如，搜索引擎，导航应用程序，欺诈检测等）都需要数据来工作。该项目可以平衡获取数据以改善AI技术的竞争需求，并最大程度地提高人们为研究贡献其个人信息的隐私。该项目与AI研究社区合作，开发了一种原型系统，可帮助AI研究人员确保收集有关人员的数据，同时提高他们做出新的科学发现的能力。具体而言，原型系统将产生具有隐私增强的人口统计问题，并建议数据收集计划，以确保收集有关人员平衡需求的数据对统计严格和社区代表性的需求。在此过程中，该系统通过减少有关人员收集的数据的数量并帮助创建可用，公平，准确且可信赖的AI系统来提高以人为中心的AI研究的质量，安全性和效率。设计参与者的隐私和安全性，同时仍然产生强大的结果是棘手的。该项目利用网络安全技术（例如数据最小化）来帮助以人为本的AI研究人员更好地保护研究参与者的隐私，同时确保他们的研究”统计能力和普遍性。该项目与以人为中心的AI研究社区合作，建立了可用的工具链，用于生成数据最小化的人口统计调查问题，并确定统计上能力良好的研究样本大小和人口统计学多样化的组成，以确保研究的完整性。系统最大程度地提高了人类受试者的隐私性，并推荐了样本大小和组成的均衡能力和代表性均衡的能力和代表性。这种方法在研究设计时促进了科学发现的质量。通过与AI研究人员进行需求调查，迭代工具链精炼和可用性测试的过程，该项目建立了一个系统，以更安全，高效，以人为中心为中心的AI研究。该奖项反映了NSF的法定任务，并被认为是通过该基金会的知识分子功能和广泛的影响来评估CRETERIA的评估。