CAREER: Information-Theoretic Measures for Fairness and Explainability in High-Stakes Applications

机器学习在我们生活的各个方面变得越来越普遍，包括几个高风险的应用程序，如金融、教育和就业。这些机器学习模型在学习历史数据中出现的模式方面取得了显着的成功。然而，不分青红皂白地学习所有模式有时会导致意想不到的后果，例如延续基于性别、种族和其他受保护属性的差异，这可能会对某些群体产生不利影响。该项目旨在通过使用户能够系统地识别、解释和缓解差异的来源，来推进伦理和社会负责的机器学习的基础。反思传统的分别处理公平和可解释性的范式，这项研究项目将通过统一的信息论视角联合检查公平性和可解释性。此外，通过广泛的推广和学生对机器学习的社会影响的参与，该项目旨在向本科生和高中生，特别是未被充分代表的少数族裔学生灌输对数学原理的方法和STEM教育的兴趣，以引领下一代对社会负责的技术。研究项目将通过利用信息论中的一系列称为部分信息分解(PID)的工作，为负责任的机器学习提供一种新的信息论观点。PID与统计决策理论中的Blackwell充分性原理密切相关，并提供了一种正式的方法来量化一个随机变量相对于目标变量而言比另一个变量更具信息量。结合估计和优化技术，这个项目将使我们能够理清几个随机变量共享的关于另一个目标变量的联合信息内容，例如，受保护的属性，如性别、种族、年龄、国籍等。将调查四项研究主旨：(I)提供一个信息论框架，用于解释受保护属性(性别、种族等)的差异来源；(Ii)进行系统的特征选择和表征学习，并进行差异控制；(Iii)调查基本限制，重点是分布式和联邦环境；以及(Iv)在金融和教育的现实世界数据集上验证这些发现。这项研究将为工程师和政策制定者奠定基本的指导原则，使人工智能能够真正带来社会公益。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。

Machine learning is becoming increasingly prevalent in various aspects of our lives, including several high-stakes applications, such as finance, education, and employment. These machine learning models have shown remarkable success at learning patterns present in the historical data. However, indiscriminate learning of all patterns can sometimes lead to unintended consequences, such as perpetuating disparities based on gender, race, and other protected attributes, that can adversely affect certain groups of people. This project seeks to advance the foundations of ethical and socially-responsible machine learning by empowering users to systematically identify, explain, and mitigate the sources of disparity. Rethinking the traditional paradigm of separately addressing fairness and explainability, this research project will jointly examine fairness and explainability through a unified information-theoretic lens. Furthermore, through extensive outreach and student engagements on the social impacts of machine learning, this project aims to instill interest in mathematically-principled approaches and STEM education among undergraduate and high-school students, particularly underrepresented minority students, to spearhead the next generation of socially-responsible technology.The research project will provide a novel information-theoretic view of responsible machine learning, by leveraging a body of work in information theory called Partial Information Decomposition (PID). PID is closely tethered to the principles of Blackwell sufficiency in statistical decision theory and provides a formal way of quantifying when a random variable is “more informative” than another with respect to a target variable. Combined with estimation and optimization techniques, this project will enable us to disentangle the joint information content that several random variables share about another target variable, e.g., protected attributes such as gender, race, age, nationality, etc. Four research thrusts will be investigated: (i) Providing an information-theoretic framework for explaining sources of disparity with respect to protected attributes (gender, race, etc.); (ii) Performing systematic feature selection and representation learning with disparity control; (iii) Investigating fundamental limits with a focus on distributed and federated settings; and (iv) Validating these findings on real-world datasets in finance and education. This research will lay the foundational guiding principles for engineers and policymakers so that AI can truly bring about social good.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.