CAREER: Advancing Fair Data Mining via New Robust and Explainable Algorithms and Human-Centered Approaches

预测歧视在影响人类生活的人工智能(AI)应用中广泛存在。自动决策可能复制、夸大社会不平等，甚至实施新形式的歧视并使之合法化。数据挖掘和机器学习模型的公平性和公平性正成为许多社区日益关注的问题，但数据中敏感信息的约束和模型的复杂性给构建公平的人工智能框架带来了关键挑战。该项目专注于开展基础研究活动，以促进数据挖掘和机器学习的公平性，并在以人为中心和以健康为重点的现实世界问题中实现高效的人机交互。该项目将产生算法和软件，促进在高风险应用领域对公平人工智能技术的更广泛研究，例如提高医疗保健多样性。该项目的影响正在减轻人类构建、采用公平模型并与其互动的努力。此外，该项目将鼓励未被充分代表的学生从事尖端计算研究，并为多学科领域的研究生和本科生教育做出贡献。该项目的研究目标是创建公平和可解释的人工智能和人在环控制范式：设计一系列具有高表达能力、忠实解释和严谨理论基础的公平、可解释和健壮的数据挖掘算法。从数据公平的角度来看，调查人员将设计有效的算法，以实现公平预测，同时能够保护敏感信息。从算法的角度，研究人员将设计新的可解释和健壮的模型，并在泛化能力和帕累托效率方面提供严格的理论保证。从人机交互的角度来看，该项目将促进人在回路中的干预，并整合人类反馈，以修复不正确或有偏见的预测。这项研究工作结合了严格的理论分析和新出现的应用问题，适用于解决社会在建立负责任的数据科学方面面临的重大挑战。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Predictive discrimination is widespread in artificial intelligence (AI) applications that affect human life. Automated decisions can replicate, exaggerate social inequities, and even implement and legitimize new forms of discrimination. The fairness and equity of data mining and machine learning models are becoming a growing concern in many communities, but the constraints of sensitive information in data and the complexity of models bring critical challenges to building fair AI frameworks. This project focuses on undertaking fundamental research activities to advance fairness in data mining and machine learning, and to enable efficient human-machine interaction in human-centered and wellness-focused real-world problems. This project will result in algorithms and software that facilitate broader research of fair AI technologies in high-stake application areas, such as improving healthcare diversity. The project's impacts are easing humans' effort to build, adopt, and interact with fair models. Furthermore, this project will encourage underrepresented students into cutting edge computational research and contribute to graduate and undergraduate education in multidisciplinary areas.The research objective of this project is to create fair and explainable AI and human-in-the-loop control paradigm: designing a family of fair, explainable, and robust data mining algorithms with high expressive ability, faithful explanations, and rigorous theoretical foundations. From a data equity perspective, the investigator will design effective algorithms to achieve fair predictions while being able to protect sensitive information. From an algorithm perspective, the investigator will design novel explainable and robust models with rigorous theoretical guarantees on generalization ability and Pareto efficiency. From a human-machine interaction perspective, the project will promote human-in-the-loop interventions and integrate human feedback to repair incorrect or biased predictions. This research effort combines rigorous theoretical analysis with emerging application problems, and is applicable to addressing the grand challenges that society faces in building responsible data science.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.