报告题目：Explainable Artificial Intelligence in Banking and Finance
报 告 人：Professor Aijun Zhang，Department of Statistics and Actuarial Science, The University of Hong Kong
报告时间：2019年12月26日，10:00—11:00
报告地点：数学楼一楼报告厅
报告摘要：
The recent developments of deep learning in artificial intelligence (AI) have brought great successes in computer vision and natural language processing. They are based on the deep neural networks that are too complex to interpret. Such black box AI models have limited applications in banking and finance. In this talk, a holistic view of interpretable machine learning will be presented, including post-hoc explainability and intrinsic modeling approaches from global and local interpretability perspectives. We suggest to enhance the intrinsic interpretability through a variety of model constraints. In particular, we propose a constructive approach to developing explainable neural networks through sparse, orthogonal and smooth constraints. We derive the necessary and sufficient identifiability conditions for the proposed model. The multiple parameters are simultaneously estimated by a modified mini-batch gradient descent method based on backpropagation algorithm and the Cayley transformation. By simulation and real case studies, the proposed explainable neural networks are shown to achieve the superior balance between prediction accuracy and model interpretability.
报告人简介：
张爱军博士现任香港大学统计及精算学系助理教授，主要从事大数据分析、大规模机器学习、可解释人工智能等领域的基础研究及其在银行金融领域的实践应用。他曾担任香港大学“数据科学”硕士学位课程副主任和香港大学“应用人工智能”文理学士学位课程创始主任。加入香港大学前，张爱军博士于2014至2016年在香港浸会大学深圳研究院担任教育大数据中心主任，在任期间领导研究团队成功开发新一代微慕课软件和基于微课慕课的大规模教育数据挖掘平台；于2008至2013年任职美国银行全球风险管理部，主要从事大规模信贷资产风险管理，参与发明美国专利“风险与奖励评估机制”和多项量化风险模型研究。张爱军博士于1998年考入清华大学，转学香港浸会大学于2002年和2004年分别获数学理学士学位和统计学哲学硕士学位，再赴美国密歇根大学于2009年获统计学哲学博士学位。他目前担任中国数学会均匀设计分会常务委员和广东省高校统计学专业教学指导委员会委员。
