Explainable Artificial Intelligence for neural networks and its evaluation
June 12, 2026
This course will be given in the context of the Enhance Athens week at TU Delft between November 14 and 21, 2026.
Lecturers
Dr. Marco Zullich (TU Delft, the Netherlands)
Abstract
Transparency in the context of an AI system is a fundamental property which is remarked by the EU AI Act and its foundational document, the Ethical Guidelines for Trustworthy AI: indeed, modern-day AI models, such as neural networks, are often so complex that their predictive dynamics are unintelligible to humans. One of the ways for enhancing transparency is by providing explanations, human-understandable tokens of information that approximate the functioning of said models. The branch behind the study of techniques for generating explanations is called Explainable AI (XAI). Other regulations, such as the GDPR, introduce a “right for explanation” for users whose data are processed automatically by other entities, further fueling the necessity for reliable XAI tools. However, the reliability of these methods has often been questioned, and the formal evaluation of XAI quality remains an open challenge, hindering the widespread applicability of XAI to real-world applications.
This intensive 5-day course provides an accelerated, interactive introduction to XAI in the specific case of neural networks. The first half of the course will be aimed at providing the basis of XAI, while the second half will dive into the topics of explanations quality. The lectures will be blending frontal lectures and active learning, with activities based on concept mapping and collaborative peer analysis. The final day will be dedicated to assessment – a multiple-choice exam for Bachelor’s and Master’s students and an evaluative group discussion for PhD candidates.
Course program
The course is composed of two main modules:
- Frontal lectures (12 hours): these are accessible in-person at TU Delft and online via live stream. The lectures will cover the foundations of XAI and its evaluation.
- Examination (1-3 hours): written exam (for MSc students) or group discussion (for Ph.D. candidates)
Schedule
Dates will be added soon. Lectures and exam will all happen in the period 14-21/11/2026
| Date | Time (CET) | Room | Topic |
|---|---|---|---|
| TBD | TBD | TBD | 1. Introduction to XAI |
| TBD | TBD | TBD | 2. Main XAI methods: feature attribution, counterfactuals, concepts |
| TBD | TBD | TBD | 3. Evaluation of XAI part I |
| TBD | TBD | TBD | 4. Evaluation of XAI part II & Stakeholders perspective on XAI |
| TBD | TBD | TBD | Final examination |
Enrollment
Enroll via this form: https://forms.cloud.microsoft/e/dPA5H1NFwf.
Note: completing the form above will not grant automatic access to the course. An email confirmation will be sent following manual verification of prerequisite knowledge.