TRUSTWORTHY & EXPLAINABLE AI: CSE 4/510

In this course, we will discuss adversarial learning, analyze explainability as well as the security vulnerability and privacy related issues of different machine learning(ML)/Artificial Intelligence(AI) models, popularly used by the research community. While AI is growingly being employed as an automated decision making tool in several usecase settings like business, education, healthcare, law enforcement, etc., before adopting any such system, it is important for the end users to have a clear understanding of the questions like `why the system works?' than treating it as an omnipotent BlackBox without having any explanation on its trustworthiness. We will review several state-of-the-art research papers to learn about the recent advances in this emerging domain of Trustworthy and Explainable AI, discuss several representative explainable models, learn about different categories of attacks along with a set of certified defenses introduced to evaluate robustness, and finally explore the connections between explainability and trustworthiness in terms of its applications in several domain specific problem settings.