  Motivated by the need for fair algorithmic decision making in the age of
automation and artificially-intelligent technology, this technical report
provides a theoretical insight into adversarial training for fairness in deep
learning. We build upon previous work in adversarial fairness, show the
persistent tradeoff between fair predictions and model performance, and explore
further mechanisms that help in offsetting this tradeoff.
