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

该项目将推动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利用，并确保未来信息系统的决策过程公平透明。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，特别是在高风险应用中，这可能会对社会和个人造成重大伤害。现有的工作集中在“以模型为中心”的计算方法，建立模型，而忽略了数据质量的重要性。为了应对缺乏高质量数据和缺乏对公平各方面的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，系统地设计公平决策过程所需的数据。这项多学科研究的成功结果将导致有效和高效的算法，提高学习模型的通用性和可信度，并提高健康信息学和灾难恢复能力中实际系统中部署的算法的公平性。该项目的教育计划将在培训下一代美国劳动力方面发挥不可或缺的作用，这些劳动力具有关键的负责任人工智能（RAI）技术，并吸引和留住未来STEM劳动力的多样化成员。该项目的研究目标是开发一个计算框架，通过以数据为中心的公平性缓解解决方案来解决公平性方面的数据挑战，这些解决方案可以探索和利用数据和先验知识。补充现有的研究集中在模型为中心或数据驱动的方法，该项目调查了一个新的研究方向，系统地探讨了以数据为中心的公平缓解框架。具体而言，研究目标包括：（1）探索和提取公平性方面的实例，特征和示例的代表性子集的数据特征，允许公平性定义和度量在现实世界的应用中可能会有所不同;（2）扩展和细化先验知识，以通过实例增强，特征集扩展和测量重新定义来指导歧视减轻过程;（3）利用可解释和交互式数据以及先验知识作为进一步改进公平性建模的关键要素;（4）证明在现实世界应用中的有效性，包括医疗信息学和灾难恢复能力。教育目标是：（1）透过将研究成果及个案分析融入现有及新课程，将负责任人工智能（RAI）纳入课程设计;（2）透过组织数据挑战及在社交媒体平台上广播信息，提高公众对RAI的兴趣及认识;以及（3）吸引和留住女性和代表性不足的少数民族，以确保未来的STEM劳动力多样化。该奖项反映了NSF的法定使命，并已被视为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，

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.