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

TL;DR

This paper introduces AdOpt, an adversarial domain adaptation method that reduces bias in real-time binary decision-making, such as loan approvals, by learning unbiased data representations, thereby improving fairness and performance.

Contribution

The paper proposes a novel adversarial optimism approach using domain adaptation to mitigate bias in sequential decision problems with limited feedback.

Findings

AdOpt outperforms existing methods on benchmark problems.

AdOpt reduces distributional shift between accepted and all data.

Initial evidence suggests improved fairness in decision-making.

Abstract

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.