  As machine learning algorithms grow in popularity and diversify to many
industries, ethical and legal concerns regarding their fairness have become
increasingly relevant. We explore the problem of algorithmic fairness, taking
an information-theoretic view. The maximal correlation framework is introduced
for expressing fairness constraints and shown to be capable of being used to
derive regularizers that enforce independence and separation-based fairness
criteria, which admit optimization algorithms for both discrete and continuous
variables which are more computationally efficient than existing algorithms. We
show that these algorithms provide smooth performance-fairness tradeoff curves
and perform competitively with state-of-the-art methods on both discrete
datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes).
