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

职业：探索和利用以数据为中心的建模以实现机器学习的公平性

• 批准号: 2239257
• 负责人: Na Zou
• 金额: $ 54.77万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239257&HistoricalAwards=false
• 关键词: CAREER; Exploring; Exploiting; Data; Centric

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.

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公平和透明的决策过程。尽管学习方法在现实世界的应用中取得了成功，但它们常常存在偏见，并对某些人群表现出歧视，尤其是在高风险的应用中，这可能对社会和个人造成重大损害，现有的工作重点是“以模型为中心”的计算方法。在构建模型的同时忽视数据的重要性为了应对缺乏高质量数据和缺乏对公平性各个方面的全面理解所带来的挑战，该项目将以模型为中心，进行“以数据为中心”的建模，系统地设计公平性所需的数据。这项多学科研究的成功成果将产生有效且高效的算法，增强学习模型的通用性和可信度，并提高在健康信息学和灾难恢复方面现实世界系统中部署的算法的公平性。该项目的教育计划将发挥不可或缺的作用参与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，并吸引和留住 STEM 领域未来劳动力的多元化成员。该项目的研究目标是开发一个计算框架，以应对公平性受损的数据挑战。通过探索和利用数据和先验知识的以数据为中心的公平性缓解解决方案，该项目研究了一个新的研究方向，系统地探索以数据为中心的公平性缓解框架。研究目标包括：（1）在公平性方面探索和提取实例、特征和代表性示例子集的数据特征，允许公平性定义和度量在实际应用中可能有所不同；（2）扩展和完善先验知识通过实例增强、特征集扩展和测量重新定义视角来指导减少歧视过程；(3) 利用可解释和交互式数据和先验知识作为进一步改进公平性建模的关键要素；(4) 证明真实的有效性； - 世界应用，包括医疗信息学和灾难教育目标是：（1）通过将研究成果和案例研究融入当前和新课程，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，增强公众对 RAI 的兴趣和认识。在社交媒体平台上传播信息；(3) 吸引和留住女性并反映代表性不足的少数群体，以确保未来 STEM 劳动力的多元化。该奖项是 NSF 的法定使命，通过使用基金会的智力优势和更广泛的评估，被认为值得支持。影响审查标准。