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

职业：通过新的稳健且可解释的算法和以人为本的方法推进公平数据挖掘

• 批准号: 2146091
• 负责人: Xiaoqian Wang
• 金额: $ 57.64万
• 依托单位: Purdue University
• 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: 2022-08-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146091&HistoricalAwards=false
• 关键词: CAREER; Advancing; Fair; Data; Mining

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.

在影响人类生活的人工智能（AI）应用中，预测性歧视是广泛的。自动化的决定可以复制，夸大社会不平等，甚至实施并使新形式的歧视合法化。在许多社区中，数据挖掘和机器学习模型的公平性和公平性正越来越关注，但是数据中敏感信息的限制和模型的复杂性为建立公平的AI框架带来了关键的挑战。该项目着重于开展基本的研究活动，以提高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中心的现实世界中实现有效的人机相互作用。该项目将产生算法和软件，以促进对高风险应用领域的公平AI技术的更广泛研究，例如改善医疗保健多样性。该项目的影响正在减轻人类在建立，采用和与公平模型互动的努力。此外，该项目将鼓励代表性不足的学生进行最先进的计算研究，并为多学科领域的研究生和本科教育做出贡献。该项目的研究目标是创建公平，可解释的AI和人类在环境控制范式：设计公平，可解释的，可解释的，可解释的和强大的数据矿物的家族，并具有高表达能力，忠实地解释了高表达能力，并忠于高表达能力，并忠于忠实地说明效果，并忠于高表达能力。从数据公平的角度来看，研究人员将设计有效的算法以实现公平的预测，同时能够保护敏感信息。从算法的角度来看，研究人员将设计新颖的可解释且健壮的模型，并具有严格的理论保证，可确保概括能力和帕累托效率。从人机相互作用的角度来看，该项目将促进人类的干预措施，并整合人类的反馈以修复错误或偏见的预测。这项研究工作将严格的理论分析与新兴的应用问题相结合，适用于社会在建立负责任的数据科学方面面临的巨大挑战。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响来通过评估来进行支持。

项目成果

Self-Supervised Fair Representation Learning without Demographics

• 发表时间: 2022
• 作者: Junyi Chai;Xiaoqian Wang

Fairness without Demographics through Knowledge Distillation

• 发表时间: 2022
• 作者: Junyi Chai;T. Jang;Xiaoqian Wang

Difficulty-based Sampling for Debiased Contrastive Representation Learning doi

用于无偏差对比表示学习的基于难度的采样 doi

• 发表时间: 2023
• 期刊: IEEE Conference on Computer Vision and Pattern Recognition
• 作者: Jang, Taeuk;Wang, Xiaoqian
• 通讯作者: Wang, Xiaoqian