  The design of revenue-maximizing auctions with strong incentive guarantees is
a core concern of economic theory. Computational auctions enable online
advertising, sourcing, spectrum allocation, and myriad financial markets.
Analytic progress in this space is notoriously difficult; since Myerson's 1981
work characterizing single-item optimal auctions, there has been limited
progress outside of restricted settings. A recent paper by D\"utting et al.
circumvents analytic difficulties by applying deep learning techniques to,
instead, approximate optimal auctions. In parallel, new research from Ilvento
et al. and other groups has developed notions of fairness in the context of
auction design. Inspired by these advances, in this paper, we extend techniques
for approximating auctions using deep learning to address concerns of fairness
while maintaining high revenue and strong incentive guarantees.
