FAI: Advancing Optimization for Threshold-Agnostic Fair AI Systems

人工智能（AI）和机器学习技术正被用于高风险的决策系统，如贷款决策、就业筛选和刑事司法判决。这些人工智能系统面临的一个新挑战是避免它们可能引入的不公平，这可能导致对受保护阶层的歧视性决定。大多数人工智能系统都使用某种阈值来做出决策。该项目旨在通过制定决策的阈值不可知指标来改进具有公平性意识的人工智能技术。特别是，研究团队将改进公平约束人工智能模型的训练程序，使模型适应不同的环境，适用于不同的应用，并受到新兴的公平约束。该项目的成功将产生一种可转移的方法，通过消除决策者手中的不同影响和增强人工智能系统的公平性，来提高社会各个方面的公平性。研究人员将与人工智能从业者一起，将该项目中的技术整合到现实世界的系统中，如教育分析。该项目还将有助于培训未来的人工智能和机器学习专业人员，并通过培训高中生和代表性不足的本科生来扩大这一活动。该项目专注于推进阈值不可知论公平人工智能系统的优化。研究活动包括：(i)开发可扩展的随机优化算法，用于优化一系列基于秩的阈值不可知论目标；（ii）开发新的阈值不可知公平测度，包括受试者工作特征曲线（ROC）公平、ROC曲线下面积（AUC）公平等，并研究它们与现有公平测度的关系；（iii）开发有效的随机方法，用于过程中公平感知学习方法，以直接优化受新的阈值不可知论公平性保证约束的阈值不可知论目标；（iv）研究有效的端到端深度学习框架，该框架不仅能自动学习特征表示，还能满足公平性约束。这些算法将在多个任务上进行评估，包括图像识别、推荐、时空危害预测和预测学生的表现。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。

Artificial intelligence (AI) and machine learning technologies are being used in high-stakes decision-making systems like lending decision, employment screening, and criminal justice sentencing. A new challenge arising with these AI systems is avoiding the unfairness they might introduce and that can lead to discriminatory decisions for protected classes. Most AI systems use some kinds of thresholds to make decisions. This project aims to improve fairness-aware AI technologies by formulating threshold-agnostic metrics for decision making. In particular, the research team will improve the training procedures of fairness-constrained AI models to make the model adaptive to different contexts, applicable to different applications, and subject to emerging fairness constraints. The success of this project will yield a transferable approach to improve fairness in various aspects of society by eliminating the disparate impacts and enhancing the fairness of AI systems in the hands of the decision makers. Together with AI practitioners, the researchers will integrate the techniques in this project into real-world systems such as education analytics. This project will also contribute to training future professionals in AI and machine learning and broaden this activity by including training high school students and under-represented undergraduates. This project focuses on advancing optimization for threshold-agnostic fair AI systems. The research activities include: (i) developing scalable stochastic optimization algorithms for optimizing a broad family of rank-based threshold-agnostic objectives; (ii) developing novel threshold-agnostic fairness measures including Receiver Operating Characteristic curve (ROC) fairness, Area under the ROC Curve (AUC) fairness, etc. and studying the relationship between them and the existing fairness measures; (iii) developing efficient stochastic methods for in-processing fairness-aware learning methods to directly optimize threshold-agnostic objectives subject to new threshold-agnostic fairness-ensuring constraints; and, (iv) investigating effective end-to-end deep learning framework that not only automatically learns the feature representations, but also satisfies the fairness constraints. The algorithms will be evaluated on multiple tasks, including image recognition, recommendation, spatial-temporal hazard prediction, and predicting students’ performance.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.