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Ethics for Nerds E4N


study semester
standard study semester
occasional / summer semester
1 semester
teaching language


Prof. Dr.-Ing. Holger Hermanns
Prof. Dr.-Ing. Holger Hermanns
Kevin Baum
Sarah Sterz

Assessment & Grades

entrance requirements

We expect basic knowledge of propositional and first-order logic, an open mind, and interest to look at computer science in ways you probably are not used to.

assessment / exams

The details of exam admission and grading are announced at the beginning of each iteration. Typically, participant are graded based on

  • an exam or a re-exam (the better mark counts),
  • a short essay where the participant has to argue for or against a moral claim in a topic from computer science.

To get the exam admission, participants usually have to get 50% of the points on weekly exercise sheets.


Will be determined based on exam performance, essay performance, and possibly exercise outcomes. The exact modalities will be announced at the beginning of the module.


course type /weekly hours
  2 h lectures
+ 2 h tutorial
= 4 h (weekly)

(may be adjusted before the start of each iteration of the course)

total workload
   60 h of classes
+ 120 h private study
= 180 h (= 6 ECTS)

Aims / Competences to be developed

Many computer scientists will be confronted with morally difficult situations at some point in their career – be it in research, in business, or in industry. This module equips participants with the crucial assets enabling them to recognize such situations and to devise ways to arrive at a justified moral judgment regarding the question what one is permitted to do and what one should better not do. For that, participants will be made familiar with moral theories from philosophy, as well as different Codes of Ethics for computer scientists. Since one can quickly get lost when talking about ethics and morals, it is especially important to talk and argue clearly and precisely. In order to do prepare for that, the module offers substantial training regarding formal and informal argumentation skills enabling participants to argue beyond the level of everyday discussions at bars and parties. In the end, succesful participants are able to assess a morally controversial topic from computer science on their own and give a convincing argument for their respective assessments.

The module is intended to always be as clear, precise, and analytic as possible. What you won't find here is the meaningless bla-bla, needlessly poetic language, and vague and wordy profundity that some people tend to associate with philosophy.


This course covers:

  • an introduction to the methods of philosophy, argumentation theory, and the basics of normative as well as applied ethics;
  • relevant moral codices issued by professional associations like the ACM, the IEEE, and more;
  • starting points to evaluate practices and technologies already in use or not that far away, including for instance: filter bubbles and echo chambers, ML-algorithms as predictive tools, GPS-tracking, CCTV and other tools from surveillance, fitness trackers, big data analysis, autonomous vehicles, lethal autonomous weapons systems and so on;
  • an outlook on more futuristic topics like machine ethics, roboethics, and superintelligences;
  • and more.

The content of the course is updated regularly to always be up-to-date and cover the currently most relevant topics, technologies, policies, and developments.

Literature & Reading

Will be announced before the start of the course on the course page.

Additional Information


This module is part of the following study programmes:

Cybersecurity MSc: Vertiefungsvorlesungen Cybersecurity
study semester: 1-3 / standard study semester: 4
Cybersicherheit BSc: Vertiefungsvorlesungen der Cybersicherheit
study semester: 5-6 / standard study semester: 6
Computer Science BSc (English): Vertiefungsvorlesungen
study semester: 5-6 / standard study semester: 6
Cybersecurity BSc (English): Komplementäre Themen der Cybersicherheit
study semester: 4-5 / standard study semester: 6
Data Science and Artificial Intelligence MSc: Vertiefungsvorlesungen DSAI
study semester: 1-3 / standard study semester: 4