FAI: Advancing Optimization for Threshold-Agnostic Fair AI Systems

FAI：推进与阈值无关的公平人工智能系统的优化

• 批准号: 2246757
• 负责人: Tianbao Yang
• 金额: $ 50万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246757&HistoricalAwards=false
• 关键词: FAI; Advancing; Optimization; Threshold; Agnostic

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.

人工智能（AI）和机器学习技术已用于高风险决策系统，例如贷款决策，就业筛查和刑事司法宣判。这些AI系统带来的新挑战是避免他们可能引入的不公平性，这可能导致对受保护阶级的歧视性决策。大多数AI系统都使用某种阈值来做出决策。该项目旨在通过为决策制定阈值 - 不可吻合的指标来提高公平感知的AI技术。特别是，研究团队将改进公平约束的AI模型的培训程序，以使该模型适应不同的环境，适用于不同的应用程序，并受到新兴公平限制。该项目的成功将通过消除不同的影响并增强了决策者手中的AI系统的公平性来提高社会各个方面的公平性，以提高社会各个方面的公平性。研究人员将与AI从业人员一起，将该项目的技术整合到教育分析等现实世界中。该项目还将有助于培训AI和机器学习的未来专业人员，并通过培训高中生和代表性不足的本科生来扩大这项活动。该项目着重于推进阈值 - 不合稳定公平AI系统的优化。研究活动包括：（i）开发可扩展的随机优化算法，以优化一个基于等级的阈值 - 不合Snostic目标的广泛家庭； （ii）开发新颖的阈值不足的公平测量值，包括接收器操作特征曲线（ROC）公平，ROC曲线下的面积（AUC）公平等等，以及研究它们与现有公平测量之间的关系； （iii）开发有效的随机方法，用于进行处理公平感知的学习方法，以直接优化受到新的阈值 - 无义公平 - 信号公平的约束的阈值 - 无义对象； ，（iv）调查有效的端到端深度学习框架，这些框架不仅会自动学习特征表示，而且还满足了公平的约束。该算法将在多个任务上进行评估，包括图像识别，建议，时空危害预测以及预测学生的表现。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响标准来评估NSF的法定任务。

项目成果

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints

• DOI: 10.48550/arxiv.2212.12603
• 发表时间: 2022-12
• 作者: Yao Yao-Yao;Qihang Lin;Tianbao Yang