Level of Study
Place of Instruction
Objectives and Content
AI ethics is the common reference to a collection of sub-fields in AI developed to respond to the issues of how to manage the moral, personal and societal impact of replacing people tasks and roles with AI powered computing. AI Ethics comprises of four main research sub-disciplines: fair-accountable-transparent AI (FAccT), explainable AI (XAI),responsible AI, and machine ethics (also called artificial morality).
This course gives an introduction to AI Ethics and a general overview of the state of the art in AI ethics through immersing the students in the research process of an AI ethics topic.
- Identify the basic problems studied in explainable AI (XAI), Fairness, Accountability and Transparency (FAccT), Responsible AI and machine ethics.
- Understand the premises of the core moral theories.
- Interpret, explain and extend the need for, and challenges of, AI Ethics.
- Experience the entire process of research in machine ethics from the inception of an idea, analysis of research work, refining a research question, planing and executing group work and reporting on the work in the form of a scientific report.
- Appraise the ethical aspects of AI problems.
- Match a specific AI Ethics challenge to its most relevant discipline.
- Reading and explaining scientific articles.
- Research project management. Scientific reporting
Recommended Previous Knowledge
Solid background in discrete mathematics and logic.
Credit Reduction due to Course Overlap
Access to the Course
Master in Information Science. Other master students may apply for admission.
Teaching and learning methods
Compulsory Assignments and Attendance
Mandatory participation: Attendance at 80 % of course sessions is mandatory.
Obligatory group and individual assignments throughout the course.
Compulsory requirements are only valid the semester they are approved.
Update: In the spring semester 2021, the requirement to attend 80% of course sessions will not apply due to the corona situation. However, it is highly recommended that students attend as much as possible.
Forms of Assessment
- Participation in class discussions 20%
- Oral individual presentation 30%
- Group Project 50%
Examination Support Material
The grading system has a descending scale from A to E for passes and F for fail.
Assessment in teaching semester
The reading list will be ready before 1 June for the autumn semester and 1 Decemeber for the spring semester.
All courses are evaluated according to UiB's system for quality assurance of education.
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course coordinator and administrative contact person can be found on Mitt UiB.
Department of Information Science and Media Studies at the Faculty of Social Sciences has the administrative responsibility for the course and the study programme.
Type of assessment: Group Project, Oral presentation and Discussions
- Withdrawal deadline
Exam part: Group Project
- Submission deadline
- 11.06.2021, 14:00
- Examination system
- Digital exam
Exam part: Oral individual presentation
- Exam period
Exam part: Participation in class discussions