Established by: Faculty Board of Science and Technology, 2025-03-25
Contents
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.
Module 1, theory, 4.0 credits.
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.
Various AI, machine learning and XAI methods will be used. The intention is to make the students proficient with how those methods can be applied in real-world settings encountered in industry and society in general. This is why lectures are accompanied by exercises where students practice applying some of the methods treated during lectures.
The course mainly uses the Python and R programming languages for the lectures and examples provided. Students can freely choose which language they prefer to use for the exercises.
A key component of the module is the Learning Diary, where students will critically reflect on lecture content, exercises, and key readings. This assessment encourages deeper engagement with the material, allowing students to articulate their understanding, analyze different XAI techniques, and evaluate their practical implications.
Key topics covered are:
Introduction to Explainable AI: Importance, definitions, and challenges
Explainability in deep learning and neural networks
Human-centered XAI and usability aspects
Fairness, bias, and ethical considerations in XAI
Case studies and industry applications
Module 2, practice, 3.5 credits.
This module focuses on the practical implementation of Explainable Artificial Intelligence through a group project, performed in groups of 1-4 students. Project topics and data sets will be provided by the course personnel, but student-proposed topics are encouraged. Each group presents their progress, plans and open questions to course personnel and fellow students in intermediate "mentoring sessions" and in one final presentation session. Through this mentoring approach, students will take an active role in developing an XAI solution, critically assessing its usability, and adapting explanations to different stakeholders.
The purpose of mentoring sessions is to provide constructive feedback and guidance to the students in their learning project. Rather than traditional lectures, students will engage in self-directed learning with support from mentors, who will guide discussions, provide feedback, and help refine project outcomes. The final deliverable is a project report, in which students will document their methodology, justify their design choices, evaluate the effectiveness of their explanations, and reflect on the broader implications of their work.
Expected learning outcomes
Knowledge and understanding After completing the course, the student should be able to:
(FSR 1) Explain the need for and challenges of Explainable AI (XAI) in AI applications.
(FSR 2) Differentiate between global, local, model-specific, model-agnostic, interpretable, etc. XAI methods and techniques.
(FSR 3) Recognize the importance of tailoring explanations to different user groups and contexts.
Competence and skills After completing the course, the student should be able to:
(FSR 4) Apply various XAI methods to real-world machine learning models.
(FSR 5) Evaluate the effectiveness of explainability techniques using appropriate metrics.
Judgement and approach After completing the course, the student should be able to:
(FSR 6) Develop a user-centered mindset when designing AI explanations.
(FSR 7) Appreciate the importance of fairness, transparency, and trust in AI systems.
Required Knowledge
At least 90 ECTS. At least 7.5 ECTS artificial intelligence (5DV243 or similar). Either 7.5 ECTS machine learning (5DV238 or similar) or 7.5 ECTS data processing and visualisation (5DV217 or similar). Proficiency in English equivalent to the level required for basic eligibility for higher studies.
Form of instruction
The course consists of lectures, practical exercises performed individually, and a project performed in groups of up to four students. In addition to scheduled activities, the course also requires individual work with the material.
Examination modes
The assessment of Module 1 (FSR 1-7) consists of a written learning diary, which includes written lab reports. The grades given in this module are Fail (U), Pass (G) or Pass with distinction (VG).
The assessment of Module 2 (FSR 4-7) consists of a written project group report. The assessment is designed such that an individual assessment can be performed. The grades given in this module are Fail (U), Pass (G) or Pass with distinction (VG).
On the whole course, one of the grades Fail (U), Pass (G) or Pass with distinction (VG) is given. If both modules are graded VG, then the course as a whole will also be graded VG.
Adapted examination The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.
Other regulations
If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.
