1. Bachelor of Science (at least 180 higher education credits) in computer science or related subjects such as mathematics, informatics, telecommunications, electrical engineering, physics.
2. Knowledge equivalent to English 6 at Swedish upper secondary level.
3. At least 15 credits in programming.
4. At least 7.5 credits in mathematics.

The course is part of the programme Computer Science: Applied Data Science, master’s programme, and can be included in the master's degree in computer science (120 credits).

The course contains the following elements:

The course focuses on how modern information technology and data processing gives rise to new kinds of ethical dilemmas. The course uses case studies to convey principles and guidelines for managing:

• Data quality
• Data integrity and responsible management of sensitive data (e.g. GDPR, consent)
• Data security (including data ownership, block chain techniques, threats and risk analysis)
• Data-driven and evidence-based decision making
• Neutrality, transparency, reliability, accountability and personal self-determination in a data-driven society.
• Interpretability in machine learning models
• Susceptibility to bias and discrimination as a potential effect of information systems replacing manual processing
• Ethical perspective on data science

Knowledge and understanding
For a passing grade the student shall be able to:

• Summarise current reasoning on ethical aspects of data-driven activities
• Describe conditions for the results of data processing being interpreted correctly
• Discuss ethical aspects of data processing and their significance for decision-making and societal development

Competence and abilities
For a passing grade the student shall be able to:

• Identify pitfalls and weaknesses in today's data-driven systems and provide solutions to mitigate them.
• Point out possible contributions from the use of data science to policymakers and stakeholders in the public and private sector
• Verbally presenting a work in the field of data science

Evaluation abilities and approach
For a passing grade the student shall be able to:

• Analyse ethical problems concerning data processing and argue from an ethical perspective to suggest improvements to data-driven systems and operations.
• Critically analyse and justify ethical positions in relation to data science processes

Lectures and seminars.

The students’ performance is assessed partly from written assignments (6.5 credits, assessed as A–E), and partly from oral presentations (1 credit, assessed as UG)

An A-E pass requires that all parts have been completed and passed.

The final grade is based on written assignments.

• Tavani, Herman T. (2013). Ethics and Technology Controversies, Questions and Strategies for Ethical Computing, Fourth Edition. John Wiley & Sons Inc.

A collection of scientific articles will be added to the above mentioned literature.

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format.

The syllabus is a translation of a Swedish source text.