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In many real world settings binary classification decisions are made based onlimited data in near real-time, e.g. when assessing a loan application. Wefocus on a class of these problems that share a common feature: the true labelis only observed when a data point is assigned a positive label by theprincipal, e.g. we only find out whether an applicant defaults if we acceptedtheir loan application. As a consequence, the false rejections becomeself-reinforcing and cause the labelled training set, that is beingcontinuously updated by the model decisions, to accumulate bias. Prior workmitigates this effect by injecting optimism into the model, however this comesat the cost of increased false acceptance rate. We introduce adversarialoptimism (AdOpt) to directly address bias in the training set using adversarialdomain adaptation. The goal of AdOpt is to learn an unbiased but informativerepresentation of past data, by reducing the distributional shift between theset of accepted data points and all data points seen thus far. AdOptsignificantly exceeds state-of-the-art performance on a set of challengingbenchmark problems. Our experiments also provide initial evidence that theintroduction of adversarial domain adaptation improves fairness in thissetting.

Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem