Fairness and Privacy in Machine Learning

This course gives a thorough technical introduction to algorithmic privacy and fairness. The course work is centered around individual assignments and group project work.

  1. Introduction: algorithmic privacy, fairness and reproducibility.
  2. Privacy and anonymity
  3. Differential privacy
  4. Approximate differential privacy
  5. Privacy amplification
  6. Group fairness: Equalised odds
  7. Group fairness: Balance and calibration
  8. Individual fairness: Meritocracy
  9. Individual fairness: Smoothness
  10. Reproducibility


Code 42116
Type Course
Site Neuchâtel
Track(s) T4 – Theory and Logic
T6 – Data Science
Semester A2023


Lecturer(s) Christos Dimitrakakis
Language english
Course Page

The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_2793349.html.

Schedules and Rooms

Period Weekly
Schedule Friday, 08:45 - 12:00
Location UniNE, Unimail
Room E213

Additional information


First Lecture
The first lecture will take place on Friday, 22.09.2023 at 08:45 in UniNE, Unimail, room E213.