EAGER:AI-DCL:Capture, Explain and Negotiate the Inherent Trade-offs in Machine Learning Algorithms

EAGER：AI-DCL：捕获、解释和协商机器学习算法中固有的权衡

• 批准号: 1927166
• 负责人: Haiyi Zhu
• 金额: $ 29.57万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927166&HistoricalAwards=false
• 关键词: EAGER; AI; DCL; Capture; Explain

An outstanding issue with machine learning based decision-making algorithms is the inherent trade-offs between different system criteria. There is an emerging body of literature demonstrating trade-offs between fairness and accuracy, and between different fairness notions. By improving fairness, overall accuracy might decrease. Furthermore, different fairness notions are not compatible with each other: well-established results show that common statistical fairness notions are often mutually exclusive. Accurate understanding of such trade-offs is critical for stakeholders and practitioners to appropriately use these machine learning methods. The focus of this project is to take an interdisciplinary approach to study, explain, and address the inherent trade-offs between different system criteria in machine learning-based decision-making.The researchers will develop methods to capture trade-offs between different system criteria in machine learning algorithms. They will develop visualizations and interactive interfaces to explain the trade-offs between the models to the stakeholders. Finally, the project team will explore social and technical innovations that let stakeholders navigate and negotiate the fundamental trade-offs between different system criteria. The project will disseminate the new knowledge in part through a MOOC offering.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.

基于机器学习的决策算法的一个突出问题是不同系统标准之间的固有权衡。有一个新兴的文学机构证明公平和准确性之间的权衡，以及不同的公平概念。通过提高公平性，整体准确性可能会下降。此外，不同的公平概念是不兼容的：公认的结果表明，共同的统计公平概念往往是相互排斥的。准确理解这些权衡对于利益相关者和从业者正确使用这些机器学习方法至关重要。该项目的重点是采取跨学科的方法来研究，解释和解决基于机器学习的决策中不同系统标准之间的内在权衡。研究人员将开发方法来捕获机器学习算法中不同系统标准之间的权衡。他们将开发可视化和交互式界面，向利益相关者解释模型之间的权衡。最后，项目团队将探索社会和技术创新，让利益相关者导航和谈判不同系统标准之间的基本权衡。 该项目将通过MOOC课程传播新知识。该奖项反映了NSF的法定使命，并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，被认为值得支持。