Explainable Artificial Intelligence 7.5 credits

About the course

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
Interpretable vs. black-box models
Model-agnostic explanation methods (e.g., LIME, SHAP, CIU)
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.

Show syllabus

Apply

Autumn 2025
- Explainable Artificial Intelligence
  
  Second admissions round for EU/EEA citizens
  
  Autumn 2025 / Umeå / English / On site
  
  Submit a late application
  
  Show more Show less
  
  Starts
  3 November 2025
  
  Ends
  18 January 2026
  
  Number of credits
  7.5 credits
  
  Type of studies
  On site
  
  Study pace
  50%
  
  Teaching hours
  Daytime
  
  Study location
  Umeå
  
  Language
  English
  
  Application code
  UMU-57248
  
  Eligibility 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.
  
  Selection
  Academic credits
  
  Application
  Application deadline was 15 April 2025. Please note: This second application round is intended only for EU/EEA/Swiss citizens. Submit a late application at Universityadmissions.se.
  
  Application and tuition fees
  As a citizen of a country outside the European Union (EU), the European Economic Area (EEA) or Switzerland, you are required to pay application and tuition fees for studies at Umeå University.
  
  Application fee: SEK 900
  
  Tuition fee, first instalment: SEK 19,038
  
  Total fee: SEK 19,038
  
  Details about tuition, fees and funding

Apply online via universityadmissions.se
You apply to our programmes and courses via universityadmissions.se – the official website for higher education applications in Sweden. There, you can track your application, check that your documents have been registered, and log in to find our your admission results.
 
Late applications
Admissions to most programmes and courses typically close after the final application deadline. However, some programmes and courses may still accept late applications if seats are available. These are marked “Open for late application” on universityadmissions.se. Please note that late applications are not guaranteed to be reviewed.

More about application and admission

Explore your future at Umeå University

Join a vibrant academic community where high-quality education meets groundbreaking research in science, technology, humanities, and the arts. At Umeå University, you will learn from passionate, expert teachers and benefit from a close connection between research, education, collaboration, and innovation.

World's most satisfied international students

#1 globally in the main categories of Living, Support, and Overall Satisfaction.
A university with health at its core

Umeå University is certified as a Healthy Campus, with many initiatives that promote health and well-being.

Questions about the course?

Subject

Describe your enquiry or ask a question

First name

Last name

Email address

Please be aware that the University is a public authority and that what you write here can be included in an official document. Therefore, be careful if you are writing about sensitive or personal matters in this contact form. If you have such an enquiry, please call us instead. All data will be treated in accordance with the General Data Protection Regulation.