Text

Course syllabus - Trustworthy Artificial Intelligence

Scope

2.5 credits

Course code

DVA507

Valid from

Autumn semester 2026

Education level

Second cycle

Progressive Specialisation

A1N (Second cycle, has only first-cycle course/s as entry requirements)

Main area(s)

Computer Science

Organisation

Department of Computer Science & Engineering

Ratified

2024-01-18

Revised

2025-11-03

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2025-09-11
    • Latest update: 2025-09-11

    Miscellaneous

    Reference articles will be provided in Canvas.

Objectives

This course introduces concepts and relevant problems where trustworthiness in developing AI systems is crucial. The purpose is to enable the students to critically evaluate existing AI methods, techniques and algorithms to increase explainability, transparency, fairness and trust in AI systems.

Learning outcomes

After completing the course, the student shall be able to:

  1. describe and understand the fundamentals of trustworthy AI,
  2. demonstrate the ability to apply XAI methods and algorithms and
  3. describe and understand the challenges and measures of fair ML.

Course content

  • Introduction to trustworthiness in AI: fundamental concept, requirements, challenges, and limitations.
  • Explainable AI: definition, requirements and methods.
  • Algorithmic Fairness: Data bias, Ethics, Fair ML.

Specific requirements

90 credits of which at least 60 credits in Computer Science or equivalent, including 15 credits in programming as well as 2,5 credits in basic probability theory and 2,5 credits in linear algebra, or equivalent. In addition Swedish course 3 or Swedish level 3 and English course 6 or English level 2 are required. For courses given entirely in English exemption is made from the requirement in Swedish course 3 or Swedish level 3.

Examination

Assignment (INL1) 0.5 credits, examines the learning outcome 1, marks Fail (U) or Pass (G).

Exercise (OVN1), 2.0 credits, examines the learning outcomes 2-3, marks Fail (U) or Pass (G).

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

Two-grade scale