CAREER: New Frameworks for Ethical Statistical Learning: Algorithmic Fairness and Privacy

职业：道德统计学习的新框架：算法公平性和隐私

• 批准号: 2340241
• 负责人: Linjun Zhang
• 金额: $ 45万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340241&HistoricalAwards=false
• 关键词: CAREER; New; Frameworks; Ethical; Statistical

With the unprecedented impact of data science and machine learning in many aspects of our daily lives, such as healthcare, finance, education, and law, there is an urgent need to design ethical statistical learning algorithms that account for fairness and privacy. This project tackles the challenge of integrating ethical principles into the fabric of statistical learning. The approach prioritizes fairness by enhancing statistical algorithms to perform equitably, particularly in scenarios with limited sample sizes and where sensitive attributes are restricted by legal or societal norms. In parallel, this project addresses privacy by developing a general framework for studying the privacy-accuracy trade-off under new privacy constraints emerging with the advances in generative AI. The practical upshot of this work is the application of these methods to biomedical fields, accompanied by the release of open-source software, broadening the impact and encouraging ethical practices in statistical learning across various domains. This project promotes equitable and private data handling and provides research training opportunities to students.The research objective of this project is to develop rigorous statistical frameworks for ethical machine learning, with a focus on algorithmic fairness and data privacy. More specifically, the project will: (1) develop innovative statistical methods that ensure fairness in a finite-sample and distribution-free manner; (2) design algorithms that ensure fairness while complying with societal and legal constraints on sensitive data; (3) establish new frameworks to elucidate the trade-off between statistical accuracy and new privacy concepts in generative AI, including machine unlearning and copyright protection. Taken together, the outcome of this research will build a firm foundation of ethical statistical learning and shed light on the development of new theoretical understanding and practical methodology with algorithmic fairness and privacy guarantees.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.

随着数据科学和机器学习在我们日常生活的许多方面的前所未有的影响，例如医疗保健，金融，教育和法律，迫切需要设计道德的统计学习算法，以说明公平和隐私。该项目应对将道德原则纳入统计学习结构的挑战。该方法通过增强统计算法以公平执行的统计算法来优先考虑公平性，尤其是在样本量有限的情况下，敏感属性受法律或社会规范限制的情况。同时，该项目通过开发一个一般框架来解决隐私，以研究新的隐私限制下的隐私准确性权衡，并随着生成AI的进步而出现。这项工作的实践结果是将这些方法应用于生物医学领域，并伴随着开源软件的发布，扩大了影响并鼓励跨各个领域的统计学习中的道德实践。该项目促进公平和私人数据处理，并为学生提供研究培训机会。该项目的研究目标是为道德机器学习开发严格的统计框架，重点是算法公平和数据隐私。更具体地说，该项目将：（1）开发创新的统计方法，以无限样本和无分配方式确保公平性； （2）设计算法，这些算法在遵守敏感数据的社会和法律限制的同时确保公平性； （3）建立新的框架，以阐明生成AI中统计准确性和新隐私概念之间的权衡，包括机器未学习和版权保护。综上所述，这项研究的结果将建立道德统计学习的坚定基础，并阐明了具有算法公平和隐私保证的新理论理解和实践方法的发展。该奖项反映了NSF的法定使命，并通过使用该基金会的智力功能和广泛的影响来评估Criteria criteria criteria criteria criteria criteria。