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.

翻译：在许多现实场景中，基于有限数据的近乎实时的二元分类决策被做出，例如评估贷款申请时。我们聚焦于一类共享共同特征的此类问题：真实标签仅在数据点被主体赋予正标签时才能被观测到，例如，只有当我们接受贷款申请后，才能发现申请人是否违约。因此，错误拒绝具有自我强化特性，导致由模型决策持续更新的标注训练集累积偏差。先前工作通过向模型注入乐观性来缓解这一效应，但这以增加错误接受率为代价。我们提出对抗乐观性（AdOpt）方法，利用对抗域适应直接应对训练集中的偏差。AdOpt的目标是通过减小已接受数据点与迄今所见所有数据点之间的分布偏移，学习过去数据的无偏但富有信息性的表征。在一组具有挑战性的基准问题上，AdOpt显著超越了现有最优性能。我们的实验还初步表明，在此场景中引入对抗域适应能够提升公平性。