|Type of Lecture:||Lecture|
- 03.05.2023 - All seminar topics have been assigned. 9 participants received their first choice, 4 participants received their second choice, 1 participant was assigned the third choice, 3 participants received none of their choice. The quantity of selected topics is the following (not taking ranking into account):
Topic 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Quantity 4 4 1 0 3 0 0 2 3 4 6 9 3 4 1 6 1
- You will find more information about the algorithm used for assignment here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html
- 02.05.2023 - So far, 16 out of 17 participants have submitted their topic preferences. Please check again if you have really sent your preference to
- 28.04.2023 - The Slides V1 include an update of the time table slide 15, and an update expected results as announced in the lecture. Additionally a 17th topic about gender bias was added.
- Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. International journal of surgery, 88, 105906.
- Vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A.: Reconstructing the giant: On the importance of rigour in documenting the literature search process. In: 17th European Conference on Information Systems, ECIS 2009 (2009).
- Exampel: Tronnier, F., Recker, M., Hamm, P.: Towards Central Bank Digital Currency – A Systematic Literature Review. PACIS 2020 Proc. (2020).
Machine Learning is becoming more and more important in daily life applications such as self-driving cars or communication assistants. To get these applications working, a lot of data is required what is the reason why, many countries already restrict and regulate the handling and usage of personal data by data protection regulations such as the EU GDPR. Besides the handling of private data, also a lot of ethical questions, such as the demand for fair AI emerge.
The biggest challenge at present is opening new markets while at the same time meeting the ethical, privacy and regulatory requirements.
Already, a variety of new technologies that enable privacy preserving machine learning have emerged during the recent years. These techniques aim to protect machine learning models from a variety of attacks that try to reveal data, training features, or the algorithm itself. Also, with regards to fairness, different approaches exist to define rules for a fair AI application that will be analysed and compared within this seminar.
|28.04.23||09:00 bis 18:00||Kick-Off||Slides_V1, Template||IG-Farben-Haus - IG 1.314 (Eisenhower-Raum / nur für Einzeltermine)|
|26.06.23||09:00 bis 18:00||Presentation||Seminarhaus SH - SH 3.104 nur für Einzeltermine)|
|27.06.23||09:00 bis 18:00||Presentation||Seminarhaus SH - SH 3.104 nur für Einzeltermine)|
|28.06.23||09:00 bis 18:00||Presentation||Seminarhaus SH - SH 3.104 nur für Einzeltermine)|
|29.06.23||09:00 bis 18:00||Presentation||Seminarhaus SH - SH 3.104 nur für Einzeltermine)|
|30.06.23||09:00 bis 18:00||Presentation||Seminarhaus SH - SH 3.104 nur für Einzeltermine)|
- Understand privacy issues and possible solutions for Machine Learning applications
- Understand ethical issues and possible solutions for Machine Learning applications
- Understand the aim and need for Machine Learning regulations
Topics are in the area of:
- Analysis of different Machine Learning applications
- Ethical issues in Machine Learning
- Fairness and data Bias
- Privacy Preserving Machine Learning Techniques