Adding Explanations to Machine Learning Models

Abstract
This talk provides a two-part exploration of Explainable AI (XAI). The first part is a tutorial covering the motivation and key use cases for XAI, followed by an overview of fundamental methods in the field. In the second part, we cover advanced topics, including techniques for making clusters more interpretable and methods for identifying impactful concepts to improve model performance.

About the Speaker
Michal is a research scientist at Bosch Center for AI.
Previously, she was a postdoctoral fellow at the Qualcomm Institute of the University of California San Diego and a postdoc at Tel-Aviv University. She received her Ph.D. from the Hebrew University and an MSc from Tel-Aviv University. During her Ph.D., Michal interned at the Machine Learning for Healthcare and Life Sciences group of IBM Research and the Foundations of Machine Learning group of Google.
Michal has been selected as a 2021 EECS MIT Rising Star, the recipient of the Anita Borg scholarship from Google and the Hoffman scholarship from the Hebrew University.