Fairness and privacy in machine learning

Objectif

With the ubiquitous deployment of machine learning algorithms in nearly every area of our lives, the problem of unethical or discriminatory algorithm-based decisions becomes more and more prevalent. To partially address these concerns, new sub-fields of machine learning has emerged: fairness and privacy. The goal of the course is to introduce the audience to recent developments of fairness and privacy aware algorithms. The emphasise will be made on those methods which are supported by statistical guarantees and that can be implemented in practice. In the first part, we will study classification and regression problems under the so called demographic parity constraint—a popular way to define fairness of an algorithm. In the second part we will mainly deal with differential privacy.