Explainable Artificial Intelligence

About this programme

This course explores the principles, methods, and applications of Explainable Artificial Intelligence (XAI). As AI systems become more complex and widely used in critical domains such as healthcare, finance, and autonomous systems, understanding their decision-making processes is crucial for transparency, fairness, and trust. Students will learn about various XAI approaches, including model-specific and model-agnostic techniques, interpretable machine learning models, and post-hoc explanation methods. The course also covers human-centered design for AI explanations and real-world case studies where explainability is essential. Through hands-on exercises, projects, and discussions, participants will gain practical experience in implementing XAI techniques, evaluating explainability metrics, and assessing the validity, reliability, and usability of XAI explanations. A particular emphasis will be placed on identifying the intended audience and tailoring explanations to different user groups. The course will also explore how explanations may need to be adapted based on the specific context of use. This module provides a theoretical foundation for Explainable Artificial Intelligence, focusing on its principles, methods, and applications. Through lectures and exercises, students will explore different approaches to explainability, including interpretable models, post-hoc explanation techniques, and human-centered AI design. The module also addresses ethical considerations, regulatory frameworks, and the role of explainability in various application domains.