Description & Objectives

This course explores Explainable Artificial Intelligence (XAI), a crucial subfield of machine learning dedicated to enhancing the transparency of complex models. While modern AI systems—particularly Deep Neural Networks (DNNs) and Foundation Models—achieve state-of-the-art performance, their black-box nature makes it challenging to understand the reasoning behind their predictions. This lack of interpretability raises concerns about trust, accountability, and the ability to extract meaningful insights from these models.

The course examines two key perspectives in XAI:

1. The argument for using inherently interpretable models in high-stakes domains such as healthcare and finance.
2. The development of post hoc explanation techniques that provide insight into complex models after training.

Students will engage with a variety of state-of-the-art XAI methods across multiple modalities, including computer vision, audio processing, and natural language processing (NLP). Topics covered include attribution techniques, sensitivity analysis, Concept Bottleneck Models, Concept Activation Vectors (CAVs), and Counterfactual Explanations. Through hands-on exercises, students will gain practical experience applying XAI techniques, equipping them to enhance transparency and interpretability across diverse AI applications.