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，包括一些高风险的应用，如金融，教育和就业。这些机器学习模型在学习历史数据中存在的模式方面取得了显着的成功。然而，不加选择地学习所有模式有时会导致意想不到的后果，例如基于性别、种族和其他受保护属性的差异永久化，这可能会对某些人群产生不利影响。该项目旨在通过使用户能够系统地识别，解释和减轻差异的来源来推进道德和社会责任机器学习的基础。本研究将透过资讯论的统一透镜，重新思考传统的公平性与可解释性分别处理的范式，共同检视公平性与可解释性。此外，通过广泛的宣传和学生参与机器学习的社会影响，该项目旨在向本科生和高中生，特别是代表性不足的少数民族学生灌输对科学原则方法和STEM教育的兴趣，以引领下一代社会责任技术。该研究项目将提供一种新的负责任机器学习的信息理论观点，通过利用信息理论中称为部分信息分解（PID）的工作。PID与统计决策理论中的Blackwell充分性原则紧密相连，并提供了一种正式的量化方法，当一个随机变量比另一个目标变量“信息量更大”时。结合估计和优化技术，该项目将使我们能够解开几个随机变量关于另一个目标变量共享的联合信息内容，例如，受保护的属性，如性别，种族，年龄，国籍等四个研究重点将进行调查：（一）提供一个信息理论框架，解释与受保护的属性（性别，种族等）的差异来源; (ii)执行系统的特征选择和表示学习与差异控制;（iii）调查基本限制，重点是分布式和联邦设置;以及（iv）验证这些发现在金融和教育的真实世界数据集。这项研究将为工程师和政策制定者奠定基本的指导原则，使人工智能真正带来社会公益。该奖项反映了NSF的法定使命，通过使用基金会的知识价值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.