报告时间：2023/08/23  上午10：00-11：00 

报告地点：第一综合楼B316会议室

报告题目：“Post-Hoc Explanation-by-Example” in Theory and in Practice

交流摘要：

The widespread use of machine learning has raised significant concerns over the ability to trust such systems due to the “black box” nature of their inference process. In this talk I will explain our work on explaining such systems using post-hoc explanation-by-example by way of the twin-system framework. In twin-systems, an opaque artificial neural network (ANN) is explained by “twinning” it with a more interpretable case-based reasoning (CBR) system, by mapping the feature weights from the form

报告人简介：

MIT博后Eoin M. Kenny （个人主页 https://eoinkenny.github.io/ ）

Eoin M. Kenny is an explainable AI reseracher (XAI). Previously, he did my Ph.D. at University College Dublin, Ireland. There he worked on post-hoc explanation-by-example with his supervisor Mark Keane. Prior to that, for his undergrad, he completed my Bachelor of Music degree (and Master of Arts in Musicology & Performance) at the University of Maynooth. Currently, he is at MIT researching human-friendly explanations in the Interactive Robotics Group, with a focus on contrastive explanation, and deep reinforcement learning. He has published in many of the top AI/ML conferences and Journals as first author during the past few years, including ICLR, AAAI, IJCAI, and the AI Journal. His notable contributions have been the introduction of semi-factual explanations, the first inherently intrepretable (high performing) Deep RL system, the twin-system framework for post-hoc example-based explanation, and several high quality user evaluations (designed with cognitive psychologists).