  As more decisions in our daily life become automated, the need to have
machine learning algorithms that make fair decisions increases. In fair
representation learning we are tasked with finding a suitable representation of
the data in which a sensitive variable is censored. Recent work aims to learn
fair representations through adversarial learning. This paper builds upon this
work by introducing a novel algorithm which uses dampening and stacking to
learn adversarial fair representations. Results show that that our algorithm
improves upon earlier work in both censoring and reconstruction.
