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Artificial Intelligence and Digital Skills


Admission requirements

Students must be registered for the minor Artificial Intelligence and Society. Students of all faculties can register for the minor.


The best way to get familiar with a technology is to work with it rather than to talk about it. For this reason, this course provides a hands-on approach towards AI, aimed at equipping students with digital skills needed to understand and work with AI technologies. This course lets students experience with coding and developing simple algorithms. The concept of automated decision-making is discussed by looking into a simple decision-making tool to better understand how they represent the decision space and transform decision processes. During a practicum students experience how the design and development of new technologies benefits from working in multidisciplinary teams. Typical considerations such as different forms of bias and ways of addressing them are discussed. Students will learn in interaction from each other how different perspectives can be cast on technology development.

Course objectives

Objectives of the course

This project-oriented course aims to let students develop practical insights in how the use of decision tools impacts decision processes, and in typical considerations such as bias. By combining different disciplinary backgrounds in each of the project teams, the course aims to foster interdisciplinary communication and understanding of technical and societal aspects of AI.

Achievement levels
The following achievement levels apply with regard to the course:

  • Students with no background in digital skills learn simple coding and reading (pseudo)code

  • Students with digital skills practice translating technical concepts to general audiences

  • Students can explain their disciplinary perspective on technical and societal aspects of AI to students from other disciplines

  • Students can combine different disciplinary perspectives on technical and societal aspects of AI

  • Students get a practical understanding of simple algorithmic decision tools and their societal implications


Check MyTimetable.

Mode of instruction


  • Number of (2 hour) lectures: 2

  • Names of lecturers: F. Dechesne, Mathijs Westera, Jelena Prokic, Gineke Wiggers, others to be confirmed

  • Required preparation by students: see Brightspace

Other methods of instruction

  • Description: Lab sessions and final presentations

  • Number of (2 hour) instructions: 8

  • Names of instructors: Gineke Wiggers (FdR), Friso Selten (FGGA), TBA (FWN), dr. Francien Dechesne (FdR), dr. Jelena Prokic (FGW),

  • Required preparation by students: see Brightspace

Assessment method

Examination form(s)

  • Homework assignments (20%)

  • Group project and presentation (80%)

Submission procedures

Areas to be tested within the exam
The material relevant to the examination elements consists of the required reading (literature) and practical exercises for the course, the course information guide and the subjects taught in the lectures, the seminars and all other instructions which are part of the course.

Reading list

Obligatory course materials See Brightspace.


Check the website under “course and exam enrollment” for information on how to register for the course.

Contact information

  • Coordinator: Dr. Francien Dechesne

  • Work address: Kamerlingh Onnes Building, Steenschuur 25

  • Contact information:

  • Telephone number: +31 71 527 7608

  • Email:


  • Institute: Metajuridica

  • Department: eLaw

  • Email:


This course is a central element of the new interdisciplinary minor on AI and Society. At the time of writing of this course description, class composition is yet unknown. Given that the central goal of the course to let students leverage their different disciplinary backgrounds in teams, the exact set-up of the course will be developed in the Fall tailored to the composition of the class.

In case of (corona)restrictions imposed by the government, this course description is subject to change.