  In recent years, significant work has been done to include fairness
constraints in the training objective of machine learning algorithms. Many
state-of the-art algorithms tackle this challenge by learning a fair
representation which captures all the relevant information to predict the
output Y while not containing any information about a sensitive attribute S. In
this paper, we propose an adversarial algorithm to learn unbiased
representations via the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation
coefficient. We leverage recent work which has been done to estimate this
coefficient by learning deep neural network transformations and use it as a
minmax game to penalize the intrinsic bias in a multi dimensional latent
representation. Compared to other dependence measures, the HGR coefficient
captures more information about the non-linear dependencies with the sensitive
variable, making the algorithm more efficient in mitigating bias in the
representation. We empirically evaluate and compare our approach and
demonstrate significant improvements over existing works in the field.

Machine Learning;Artificial Intelligence;Computers and Society;Machine Learning