  In many real world settings binary classification decisions are made based on
limited data in near real-time, e.g. when assessing a loan application. We
focus on a class of these problems that share a common feature: the true label
is only observed when a data point is assigned a positive label by the
principal, e.g. we only find out whether an applicant defaults if we accepted
their loan application. As a consequence, the false rejections become
self-reinforcing and cause the labelled training set, that is being
continuously updated by the model decisions, to accumulate bias. Prior work
mitigates this effect by injecting optimism into the model, however this comes
at the cost of increased false acceptance rate. We introduce adversarial
optimism (AdOpt) to directly address bias in the training set using adversarial
domain adaptation. The goal of AdOpt is to learn an unbiased but informative
representation of past data, by reducing the distributional shift between the
set of accepted data points and all data points seen thus far. AdOpt
significantly exceeds state-of-the-art performance on a set of challenging
benchmark problems. Our experiments also provide initial evidence that the
introduction of adversarial domain adaptation improves fairness in this
setting.
