Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem
This addresses bias and fairness issues in real-time decision-making systems like bank loans, though it appears incremental as it builds on prior optimism-based methods.
The paper tackles the problem of bias accumulation in binary classification tasks where true labels are only observed for positive decisions, such as loan defaults, by introducing adversarial domain adaptation to learn unbiased representations. The method, AdOpt, significantly outperforms state-of-the-art on benchmark problems and shows improved fairness.
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.