Description

This session offers a comprehensive introduction to Explainable Artificial Intelligence (XAI) — a field focused on making AI model decisions transparent, interpretable, and trustworthy. As AI systems are increasingly deployed in critical domains like healthcare, finance, and law enforcement, understanding how and why these systems make decisions is vital.

This course explores the core concepts, techniques, tools, challenges, and real-world applications of XAI. Participants will gain both theoretical insights and practical skills to implement and evaluate explainability methods in machine learning workflows.