Title: Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer,
Abstract: In many machine learning applications, there are multiple decision-makers
involved, both automated and human. The interaction between these agents often
goes unaddressed in algorithmic development. In this work, we explore a simple
version of this interaction with a two-stage framework containing an automated
model and an external decision-maker. The model can choose to say "Pass", and
pass the decision downstream, as explored in rejection learning. We extend this
concept by proposing "learning to defer", which generalizes rejection learning
by considering the effect of other agents in the decision-making process. We
propose a learning algorithm which accounts for potential biases held by
external decision-makers in a system. Experiments demonstrate that learning to
defer can make systems not only more accurate but also less biased. Even when
working with inconsistent or biased users, we show that deferring models still
greatly improve the accuracy and/or fairness of the entire system.