2021 Croucher Summer Course in Information Theory, The Chinese University of Hong Kong
We explore an interesting topic in the field of Artificial Intelligence (AI) via the lens of information theory: fair machine learning. Fairness is one of the crucial aspects required for enabling trustworthy AI. In this series of lectures, we study how tools of information theory serve to develop fair classifiers that aim to achieve the irrelevancy of a prediction to sensitive attributes such as race, sex, age and religion. We also investigate an intimate connection to one prominent unsupervised learning framework: generative adversarial networks.