Description

This course aims to familiarize students with the recent advances in the emerging field of eXplainable Artificial Intelligence (XAI).It mentions in detail different classes of interpretable models and post hoc explanations (e.g., rule-based and prototype-based models, feature attributions, counterfactual explanations, mechanistic interpretability), and explore the connections between explainability and fairness, robustness, and privacy. This course will also cover latest research on understanding large language models (e.g., GPT- 3) and diffusion models (e.g., DALLE 2), and highlight the unique opportunities and challenges that arise when interpreting the behavior of such large generative models.

Program

Artificial Intelligence 132 CRs

Objectives

•  To familiarize students with the concepts and advances in explainable artificial intelligence (XAI).  To explore different interpretable models and post hoc explanations in AI.  To understand the relationships between explainability, fairness, robustness, and privacy in AI systems.  To analyze and interpret the behavior of large generative models like GPT and DALLE 2.