  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 which is centered around the
learning of "correction vectors", which have the same dimensionality as the
given data vectors. The corrections are then simply summed up to the original
features, and can therefore be analyzed as an explicit penalty or bonus to each
feature. We show experimentally that a fair representation learning problem
constrained in such a way does not impact performance.
