CAREER: Exploring and Exploiting Data-Centric Modeling for Fairness in Machine Learning

该项目将在处理数据挑战方面取得进展，以促进机器学习的公平性，促进机器学习算法在高风险应用中的广泛使用，并确保未来信息系统的决策过程公平和透明。虽然机器学习方法在现实世界的应用中取得了成功，但它们往往存在偏见，并对某些人口统计数据表现出歧视，特别是在高风险的应用程序中，这可能对社会和个人造成重大损害。现有的工作集中在“以模型为中心”的计算方法上，这些方法构建模型，而忽略了数据质量的重要性。为了应对缺乏高质量数据和对公平的所有方面缺乏全面理解所带来的挑战，该项目将把以模型为中心的建模与以数据为中心的建模相结合，后者系统地设计公平决策过程所需的数据。这一多学科研究的成功结果将导致有效和高效的算法，以增强学习模型的泛化和可信性，并提高在卫生信息学和灾难恢复方面部署在真实系统中的算法的公平性。该项目的教育项目将在使用关键的负责任人工智能(RAI)技术培训下一代美国劳动力方面发挥不可或缺的作用，并吸引和留住STEM未来劳动力的不同成员。该项目的研究目标是开发一个计算框架，通过探索和利用数据和先验知识的以数据为中心的公平缓解解决方案来应对公平方面的数据挑战。作为对现有研究的补充，本项目探索了一种新的研究方向，系统地探索了一种以数据为中心的公平性缓解框架。具体地说，研究目标包括：(1)探索和提取关于实例、特征和具有代表性的例子子集的公平方面的数据特征，允许公平定义和度量在真实世界的应用中有所不同；(2)扩展和提炼先验知识，以通过实例扩充、特征集扩展和测量重定义来指导区分-缓解过程；(3)利用可解释和交互的数据和先验知识作为进一步改进公平性建模的关键要素；以及(4)展示对包括医疗保健信息学和灾难恢复能力在内的真实世界应用的有效性。教育目标是：(1)通过将研究结果和案例研究整合到现有课程和新课程中，将负责任的人工智能(RAI)纳入课程设计；(2)通过组织数据挑战和在社交媒体平台上传播信息，提高公众对RAI的兴趣和认识；以及(3)吸引和留住女性和代表性不足的少数群体，以确保未来STEM工作队伍多样化。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。

This project will lead to advances in dealing with data challenges to facilitate fairness in machine learning, promote broad utilization of machine-learning algorithms in high-stake applications, and ensure a fair and transparent decision-making process for future information systems. While machine-learning methods have achieved success in real-world applications, they often suffer from biases and show discrimination towards certain demographics especially in high-stakes applications, which risks significant harm to both society and individuals. Existing work focuses on “model-centric” computational approaches that build models while overlooking the importance of data quality. To tackle the challenges raised by the lack of high quality data and the lack of a comprehensive understanding of fairness in all its respects, this project will integrate model-centric with “data-centric” modeling, which systematically engineers the data needed for a fair decision-making process. The successful outcome of this multidisciplinary research will lead to effective and efficient algorithms that enhance the generalizability and trustworthiness of learned models, and improve the fairness of algorithms deployed in real-world systems in health informatics and disaster resilience. The education programs of this project will play an integral part in training the next generation of the U.S. workforce with critical Responsible Artificial Intelligence (RAI) technologies and attract and retain diverse members of the future workforce in STEM. The research goal of this project is to develop a computational framework for tackling data challenges in fairness through data-centric fairness mitigation solutions that explore and exploit data and prior knowledge. Complementing existing studies focusing on model-centric or data-driven approaches, this project investigates a novel research direction that systematically explores a data-centric fairness mitigation framework. Specifically, the research objectives include: (1) to explore and extract data characteristics on instances, features and a representative subset of examples in terms of fairness, allowing that fairness definitions and metrics may vary across real-world applications; (2) to expand and refine prior knowledge to guide the discrimination-mitigation process via instance augmentation, feature set expansion, and measurement redefinition perspectives; (3) to leverage interpretable and interactive data and prior knowledge as a key element for further improving fairness modeling; and (4) to demonstrate effectiveness on real-world applications including healthcare informatics and disaster resilience. The educational objectives are: (1) to incorporate responsible artificial intelligence (RAI) into curriculum design via integrating research findings and case studies into current and new courses; (2) to enhance public interest in and awareness of RAI by organizing data challenges and broadcasting information on social media platforms; and (3) to attract and retain women and underrepresented minorities to ensure a diverse future STEM workforce.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.