无偏决策降低遗憾：面向银行贷款问题的对抗域适应
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem
AI总结：
针对贷款审批中真实标签仅在接受后可见导致训练集偏差的问题，提出 AdOpt 对抗域适应方法以学习无偏表示，并在基准上显著优于现有方法且改善公平性。
摘要：
在许多现实场景中，二元分类决策需要基于有限数据近乎实时地作出，例如评估贷款申请时。我们关注其中一类具有共同特征的问题：只有当某个数据点被委托人赋予正标签时，才能观察到其真实标签；例如，只有在我们接受了申请人的贷款申请后，才会知道该申请人是否违约。因此，错误拒绝会形成自我强化，并导致由模型决策持续更新的有标签训练集不断积累偏差。以往工作通过向模型中注入乐观情绪来缓解这一效应，但这会以提高错误接受率为代价。我们提出对抗乐观方法（AdOpt），利用对抗域适应直接解决训练集中的偏差。AdOpt 的目标是通过减小已接受数据点集合与迄今为止所见全部数据点之间的分布偏移，学习过去数据的一种无偏但富有信息量的表示。AdOpt 在一系列具有挑战性的基准问题上显著超越了最先进性能。我们的实验还提供了初步证据，表明引入对抗域适应能够改善该场景下的公平性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
