Course of study, specific field and term:

• Certificate in Artificial Intelligence (compulsory), Artificial Intelligence, 2nd semester

Classes and lectures:

• l CS5075-V: Trustworthy AI (lecture, 3 SWS) l CS5075-Ü: Trustworthy AI (exercise, 1 SWS)

Workload:

• 100 Hours private studies
• 60 Hours in-classroom work
• 20 Hours exam preparation

Contents of teaching:

• Guiding principles of Trustworthy AI: lawful, ethical and robust AI l Trustworthy Computing Basics: Security, Privacy, Dependability, Safety, Transparency, Explainability, Traceability, Accountability
• De-anonymization methods using machine learning models
• Mathematical notions for privacy-preserving machine learning methods
• Privacy-preserving machine learning methods
• Analyse maschinell gelernter Modellen (Robustness Check, Explainability
• Verifikation maschinell gelernter Modellen ((Statistical Testing), Model Checking)
• Black-Box methods for extracting machine learning models (for economical reasons, for analysis, and for verification)
• Attacks for manipulating machine learning models (adversarial examples, backdoors)
• Hardening of machine learning methods against manipulation methods
• Robust machine learning methods against manipulation attacks
• Secure and privacy-preserving distributed learning methods (Privacy-Preserving Federated Learning)

Qualification-goals/Competencies:

• All current techniques taught in the module and described above can be named and defined by the students and their functional proofs can be explained on the basis of applications.
• The formal foundations from the course can be precisely explained
• Students are able to identify advantages and disadvantages of planning and acting approaches
• Understanding about potential vulnerabilities of machine learning methods w.r.t. privacy-violations and manipulation possibilities
• Understanding of hardening methods compared to deanonymization and manipulation methods
• Students can analyze complex security requirements

Grading through:

• Oral examination

Responsible for this module:

• Prof. Dr. Esfandiar Mohammadi

Teacher:

• Institute for IT Security
• Institute of Software Technology and Programming Languages
• Prof. Dr. Thomas Eisenbarth
• Prof. Dr. Martin Leucker
• Prof. Dr. Esfandiar Mohammadi

Literature:

• C. Dwork, A. Roth: The Algorithmic Foundations of Differential Privacy - Now Publishers Inc, 2014
• Andrej Bogdanov: Lecture notes by Andrej Bogdanov from Chinese University of Hong Kong
• Current conference and journal articles on the topics of the event will be announced at the beginning of the event in the case of the seminar and at the discussion of the topic in the case of the lecture.

Language:

• offered only in English

Notes

Prerequisites for attending the module:
- None

Prerequisites for the exam:
- None

Last updated:

10.9.2020