  Neural network architectures have been extensively employed in the fair
representation learning setting, where the objective is to learn a new
representation for a given vector which is independent of sensitive
information. Various representation debiasing techniques have been proposed in
the literature. However, as neural networks are inherently opaque, these
methods are hard to comprehend, which limits their usefulness. We propose a new
framework for fair representation learning that is centered around the learning
of "correction vectors", which have the same dimensionality as the given data
vectors. Correction vectors may be computed either explicitly via architectural
constraints or implicitly by training an invertible model based on Normalizing
Flows. We show experimentally that several fair representation learning models
constrained in such a way do not exhibit losses in ranking or classification
performance. Furthermore, we demonstrate that state-of-the-art results can be
achieved by the invertible model. Finally, we discuss the law standing of our
methodology in light of recent legislation in the European Union.
