Ethics for Data Science - Interactive curriculae of TU Ilmenau

The interactive curriculae provide information on the degree programmes offered by the TU Ilmenau.

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You can find all details on planned lectures and classes in the course catalogue.

Please note that this page is no longer updated. All modules and study plans from PO version 2021 onwards (Bachelor and Master study programs) are now available on the Campus Portal.

module properties Ethics for Data Science in degree program Master Media and Communication Science 2021
module number	201135
examination number	250019
department	Department of Economic Sciences and Media
ID of group	2559 (Kommunikationswissenschaft mit Schwerpunkt Computational Communication Science)
module leader	Prof. Dr. Emese Domahidi
term	winter term only
language	Englisch
credit points	5
on-campus program (h)	22
self-study (h)	128
obligation	elective module
exam	examination performance with multiple performances
details of the certificate	Part 1 (60%): Students will be required to analyse case studies using theories explained in class. Students will present their ideas in the form of an oral exam at the end of the semester (exam number: 2500629) Part 2 (40%): Students have to participate actively in class , write their findings, and take online quizzes (exam number: 2500630)
link to Moodle course	Course:" title="link to Moodle course" target="_blank">https://moodle.tu-ilmenau.de/course/view.php?id=3907">Course:
teacher	Prof. Dr. Domahidi, Emese
signup details for alternative examinations	This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered. The lecturer and/or the examination office will inform you about the details and time periods. If necessary, be sure to ask the lecturer.
maximum number of participants
previous knowledge and experience	Interest in ethics an data science Research skills No coding or data science skills needed
learning outcome	At the end of the course, students will be able to: Identify ethical issues surrounding the application of artificial intelligence and data science Formulate, justify, and explain ethical decisions Analyse ethical blind spots in artificial intelligence applications and data science through the use of case studies Define boundaries for future researchers and practice
content	In this lecture students will be introduced to the field of ethics and learn why ethics are important in society and especially in the digital space. They will also learn different ethical perspectives used to make decisions. Next the field of big data will be explored and defined to draw the scope of the study. The students will then be introduced to frameworks that can help to shape their understanding of ethical grey areas in areas such as autonomous driving, privacy, data ownership, surveillance, social media research, machine learning bias, AI powered disinformation, facial profiling and many more. Learning is done based on real life case studies. At the end of the semester, students are expected to find other case studies and analyse them in a term paper and propose ethical solutions.
media of instruction and technical requirements for education and examination in case of online participation	Moodle. techical requirements: camera for video transmission (720p/HD), microphone, Internet connection (suitable for HD audio and video transmission: 4 Mbps), terminal device that meets the technical requirements of the required software.
literature / references	Will be announced each semester
evaluation of teaching