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Ethics of Artificial Intelligence 7,5 Credits

Course Contents

Artificial Intelligence is increasingly playing an integral role in our daily lives. AI-based technology is used more and more in areas such as criminal justice, healthcare, transportation, education, etc. and such technologies will probably have a significant impact on the development of humanity in the near future. As designers and developers of such technologies, it is mandatory that we consider and reflect on the legal and moral repercussions of AI, and even on the fundamental principles of ethical life.

Thus, this course gives an introduction to the ethics of AI, discussing ethical concerns that arise from the use and development of AI. We organize the course content by themes, following Müller (2020)[1] division in objects (AI-systems as tools made and used by humans) and subjects (AI systems as subjects).

The theme AI-systems as objects includes learning and discussing issues such as:
- Privacy and manipulation
- Opacity (black-box machine learning) and bias
- Human-robot interaction
- Employment
- The effects of autonomy

AI systems as subjects include topics such as:
- Ethics for the AI systems themselves in machine ethics
- Artificial moral agency

We explore tools, methods and policies to address some of these aspects, e.g., addressing opacity through interpretable machine learning and explainable AI solutions.

Additionally, this course introduces and reviews Fairness, Accountability, and Transparency (FAT) aspects in Machine Learning (ML), in particular:
- Potentially discriminatory effects of using AI/ML
- The dangers of inadvertently encoding bias into automated decisions
- Solutions that account for fairness in algorithm development

Finally, we will discuss the problem of a possible future AI superintelligence leading to a singularity.


The applicant must hold the minimum of a bachelor’s degree (i.e the equivalent of 180 ECTS credits at an accredited university) with at least 90 credits in Computer Engineering, Electrical Engineering (with relevant courses in computer engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering. The bachelor’s degree should comprise a minimum of 15 credits in mathematics. Proof of English proficiency is required.

Level of Education: Master
Course code/Ladok code: TAIR22
The course is conducted at: School of Engineering

Previous and ongoing course occasions

Type of course
Study type
Spring 2023: Jan 16 - Mar 26
Rate of Study
Course coordinator
Maria Hedblom
Maria Hedblom
Tuition fees do NOT apply for EU/EEA citizens or exchange students
Application code
Last modified 2022-11-25 07:19:20