Interpretability for Machine Learning

机器学习的可解释性

• 批准号: CRC-2019-00383
• 负责人: Matwin, Stan
• 金额: $ 14.57万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=695509
• 关键词: Interpretability; Machine; Learning

Recent important breakthroughs of modern Machine Learning (ML) and Deep Learning methods are accompanied by the concerns about transparency and fairness of ML systems. In this context interpretability, understood as the ability to explain and present ML systems to humans, becomes an important requirement for modern ML systems. This research program will tackle several important aspects of interpretability, including novel use of relational knowledge representation techniques in which I have significant accomplishments. The programs principal axes are the involvement of graduate students, as well as collaborative applications of Machine Learning in Dalhousie Universitys priority research areas of Health and Oceans.

现代机器学习（ML）和深度学习方法的最新重要突破伴随着对ML系统透明度和公平性的关注。在这种情况下，解释性被理解为向人类解释和呈现ML系统的能力，成为现代ML系统的重要要求。该研究计划将解决可解释性的几个重要方面，包括对我拥有重要成就的关系知识表示技术的新颖使用。这些计划的主要轴是研究生的参与，以及在达尔豪西大学（Dalhousie Universitys）的优先研究健康和海洋研究领域的机器学习的协作应用。