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

职业：高风险应用中公平性和可解释性的信息论测量

• 批准号: 2340006
• 负责人: Sanghamitra Dutta
• 金额: $ 66.56万
• 依托单位: University of Maryland, College Park
• 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: 2024-01-15 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340006&HistoricalAwards=false
• 关键词: CAREER; Information; Theoretic; Measures; Fairness

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.

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，例如金融、教育和就业，这些机器学习模型在历史数据中的学习模式方面取得了显着的成功。所有模式的不同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持久差异，这可能会对某些人群产生不利影响。该项目旨在推进道德和社会责任机器学习的基础。通过使用户能够系统地识别、解释和减轻差异的来源，重新思考解决公平性和可解释性的传统范式，该研究项目将通过统一的信息理论视角，通过广泛的宣传和学生共同检验公平性和可解释性。该项目致力于关注机器学习的社会影响，旨在向本科生和高中生（特别是少数族裔学生）灌输对数学原理方法和 STEM 教育的兴趣，以引领下一代该研究项目将通过利用称为部分信息分解（PID）的信息理论工作，与统计中的 Blackwell 充分性原理密切相关，为负责任的机器学习提供一种新颖的信息理论观点。决策理论并提供了一种正式的方法来量化随机变量何时比另一个目标变量“提供更多信息”，结合估计和优化技术，该项目将使我们能够理清联合信息。几个随机变量与另一个目标变量共享的内容，例如性别、种族、年龄、国籍等受保护的属性。将调查四个研究重点：（i）提供一个信息论框架来解释尊重差异的来源受保护的属性（性别、种族等）；（ii）通过差异控制进行系统特征选择和表示学习；（iii）重点研究分布式和联合设置的基本限制；以及（iv）验证这些发现；这项研究将为工程师和政策制定者奠定基础指导原则，使人工智能能够真正带来社会公益。该奖项是 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持。优点和更广泛的影响审查标准。