• CD101A Imperative Programming (7.5 credits)
• CD102A Object-Oriented Programming (7.5 credits)
• CM152A Mathematical Statistics (7.5 credits)
• CD153A Sustainable Development and Ethics in Computer Science and Engineering (7.5 credits)
• 4.5 credits in the course CD162A Data Security (7.5 credits)
• In addition to the formal entry requirements, the student is expected to have knowledge from the course CM660E Mathematical Statistics for Data Science (7.5 credits)

The course is part of the degree requirements for a Master of Science in Engineering in Computer Science and Engineering (specialisation Applied Data Science)

The aim of the course is for the student to develop an understanding of the societal implications (risks and opportunities) of increasing data-driven digitalisation, and to be able to communicate knowledge of methods and tools in the field of data science, with relevance for decision-making and sustainable societal development.

The course focuses on how modern data-driven systems give 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 and sustainability perspectives on data science

Knowledge and understanding

Upon completion of the course, the student shall be able to:

1. summarise current reasoning on ethical aspects of data-driven activities,

2. describe conditions for the results of data processing being interpreted correctly, and

3. analyse and discuss ethical aspects of data processing and their significance for decision-making and societal development.

Competence and skills

Upon completion of the course, the student shall be able to:

4. use relevant methods to identify pitfalls and weaknesses in today’s data-driven systems and provide research-based solutions to mitigate them,

5. point out possible contributions from the use of data science for policymakers and stakeholders in the public and private sector, taking into account society's goals for economically, socially and ecologically sustainable development, and

6. verbally presenting a work in the field of data science.

Judgement and approach

Upon completion of the course, the student shall be able to:

7. analyse ethical problems concerning data processing and argue from an ethical perspective to suggest improvements to data-driven systems and operations, and

8. critically analyse and justify ethical positions related and other societal aspects in relation to data science processes.

Lectures, seminars and self-study.

The following are required to pass the course

• passing grade on written assignments (6.5 credits, UA) (Intended learning outcomes 1–5, 7, 8)
• oral presentations (1 credit, Pass/Fail) (Intended learning outcomes 3, 6)

For all assessments, the materials must be presented in a manner that makes it possible to discern individual performance.

The final grade corresponds to the grade of the written assignments.

• Tavani, H. T. (2016) Ethics and Technology Controversies, Questions and Strategies for Ethical Computing (5th edition), Wiley.
• Utöver ovanstående litteratur tillkommer en samling vetenskapliga artiklar.

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).

If a course is no longer offered, or has undergone significant changes, the students must be offered two opportunities for re-examination based on the syllabus that applied at the time of registration, for a period of one year after the changes have been implemented. The syllabus is a translation of a Swedish source text.

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.