FAI: Toward Fair Decision Making and Resource Allocation with Application to AI-Assisted Graduate Admission and Degree Completion

机器学习系统在日常生活中的许多应用中都很突出，比如医疗保健、金融、招聘和教育。这些系统旨在通过在大量数据中发现超出人类直觉的模式来改进人类的决策。然而，已经证明，这些系统学习和传播人类决策中存在的类似偏见。该项目旨在发展关于人工智能公平性的一般理论和技术，并应用于提高STEM研究生项目中代表性不足群体的留校率和毕业率。最近的研究表明，仅仅关注录取率不足以提高毕业率。该项目的设想是超越设计“公平分类器”，例如在单个时刻满足静态公平概念的公平毕业生录取，并设计在一段时间内做出决策的人工智能系统，其目标是确保过程完成时的整体长期公平结果。使用数据驱动的人工智能解决方案可以发现人类遗漏的模式，从而在很长一段时间内实现有针对性的干预和公平的资源分配。该项目的研究将有助于减少招生过程中的偏见，提高研究生课程的完成率，以及机器学习的一般应用中的公平决策。该项目将重点关注用于资源分配的机器学习算法，该算法可用于整个过程中的各个点，例如教育。该团队将提出公平的新概念，并展示这些概念在有限资源（如接受项目、教师指导、专业发展、带薪助教或奖学金）公平分配给学生的环境中的适用性。所提出的研究也将超越任务特定监督学习设置中的公平性，并研究无监督学习中的公平性，以保证为多个下游任务学习公平表示或生成模型。该团队将解决现实世界序列决策系统中由于数据不一致而产生的实际问题，包括训练和测试之间的分布变化、数据不平衡以及缺少敏感属性。该提案包含一项综合计划，将其研究纳入高中，本科和研究生水平的教育，以及研究成果的内部和跨学科传播，推广和其他协同活动的计划。该奖项反映了美国国家科学基金会的法定使命，并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 systems have become prominent in many applications in everyday life, such as healthcare, finance, hiring, and education. These systems are intended to improve upon human decision-making by finding patterns in massive amounts of data, beyond what can be intuited by humans. However, it has been demonstrated that these systems learn and propagate similar biases present in human decision-making. This project aims to develop general theory and techniques on fairness in AI, with applications to improving retention and graduation rates of under-represented groups in STEM graduate programs. Recent research has shown that simply focusing on admission rates is not sufficient to improve graduation rates. This project is envisioned to go beyond designing "fair classifiers" such as fair graduate admission that satisfy a static fairness notion in a single moment in time, and designs AI systems that make decisions over a period of time with the goal of ensuring overall long-term fair outcomes at the completion of a process. The use of data-driven AI solutions can allow the detection of patterns missed by humans, to empower targeted intervention and fair resource allocation over the course of an extended period of time. The research from this project will contribute to reducing bias in the admissions process and improving completion rates in graduate programs as well as fair decision-making in general applications of machine learning.This project will focus on machine learning algorithms for resource allocation, which can be used at various points throughout a process such as in education. The team will propose new notions of fairness and show the applicability of those notions to settings in which limited resources, such as acceptance to the program, faculty mentoring, professional development, and paid assistantships or fellowships, are allocated to students fairly. The proposed research will also go beyond fairness in task-specific supervised learning settings and investigate fairness in unsupervised learning that guarantees to learn fair representations or generative models for multiple downstream tasks. The team will address the practical problems that arise due to uncongenial data in real-world sequential decision-making systems, including distribution shifts between training and test, imbalanced data, and missing sensitive attributes. This proposal contains a comprehensive plan to incorporate its research into education at high school, undergraduate, and graduate levels, as well as plans for within- and cross-disciplinary dissemination of research results, outreach, and other synergistic activities.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.