  The design of optimal auctions is a problem of interest in economics, game
theory and computer science. Despite decades of effort, strategyproof,
revenue-maximizing auction designs are still not known outside of restricted
settings. However, recent methods using deep learning have shown some success
in approximating optimal auctions, recovering several known solutions and
outperforming strong baselines when optimal auctions are not known. In addition
to maximizing revenue, auction mechanisms may also seek to encourage socially
desirable constraints such as allocation fairness or diversity. However, these
philosophical notions neither have standardization nor do they have widely
accepted formal definitions. In this paper, we propose PreferenceNet, an
extension of existing neural-network-based auction mechanisms to encode
constraints using (potentially human-provided) exemplars of desirable
allocations. In addition, we introduce a new metric to evaluate an auction
allocations' adherence to such socially desirable constraints and demonstrate
that our proposed method is competitive with current state-of-the-art
neural-network based auction designs. We validate our approach through human
subject research and show that we are able to effectively capture real human
preferences. Our code is available at
https://github.com/neeharperi/PreferenceNet
