Module Number

ML-4520
Module Title

Ethics and Philosophy of Machine Learning
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Workload:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation and written report

Content

The fact that machine learning algorithms will play a central role in the process
of scientific discovery challenges our traditional understanding of the scientific
process and raises fundamental questions about concepts of scientific discovery
and the role of the scientists. On the other hand machine learning has impact on
different aspects of our society which raises ethical concerns. In this course, we
will discuss the impact, changes, and challenges to the scientific methodology
as well as society in general by machine learning from the perspective of both
the philosophy and ethics.

Objectives

Students can reflect current research questions in the area of philosophy and
ethics of machine learning. Students will be able to acquire knowledge about
current findings through comprehensive literature search. They will know the
importance of current topics in the course-relevant areas, and will be aware
that there are still many open questions. Students will not only have improved
their study and reading skills, but will also have enhanced their capability of
working independently. The teaching method in this seminar aims at boosting
the students’ confidence (oral presentation), and at enhancing their communication
skills and enabling them to accept criticism (discussion session following
their presentation).

Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Seminar
S
o
2
3.0
tp, op
30
g
100
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Ethics Lab, Genin, Grothe
Literature

Will be handed out in the course

Last offered Sommersemester 2022
Planned for Wintersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV