In uses of pre-trained machine learning models, it is a known issue that the target population in which the model is being deployed may not have been reflected in the source population with which the model was trained. This can result in a biased model when deployed, leading to a reduction in model performance. One risk is that, as the population changes, certain demographic groups will be under-served or otherwise disadvantaged by the model, even as they become more represented in the target population. The field of domain adaptation proposes techniques for a situation where label data for the target population does not exist, but some information about the target distribution does exist. In this paper we contribute to the domain adaptation literature by introducing domain-adaptive decision trees (DADT). We focus on decision trees given their growing popularity due to their interpretability and performance relative to other more complex models. With DADT we aim to improve the accuracy of models trained in a source domain (or training data) that differs from the target domain (or test data). We propose an in-processing step that adjusts the information gain split criterion with outside information corresponding to the distribution of the target population. We demonstrate DADT on real data and find that it improves accuracy over a standard decision tree when testing in a shifted target population. We also study the change in fairness under demographic parity and equal opportunity. Results show an improvement in fairness with the use of DADT.

翻译：在使用预训练机器学习模型时，一个已知问题是模型部署的目标人群可能未反映在训练模型的源人群中。这可能导致模型部署后产生偏差，从而降低模型性能。风险之一是，随着人群变化，即使某些人口统计群体在目标人群中的代表性增加，这些群体也可能被模型忽视或处于不利地位。领域自适应领域提出了一些技术，适用于目标人群标签数据不存在但存在部分目标分布信息的情况。本文通过引入领域自适应决策树（DADT）为领域自适应研究做出贡献。由于决策树因其可解释性及相对于其他复杂模型的性能而日益流行，我们聚焦于该模型。通过DADT，我们旨在提高在源域（或训练数据）与目标域（或测试数据）不同时训练的模型准确性。我们提出一种处理步骤，利用与目标人群分布相对应的外部信息调整信息增益分裂准则。我们在真实数据上演示了DADT，发现在分布偏移的目标人群测试中，其准确性优于标准决策树。同时，我们研究了在人口统计均等和机会均等条件下公平性的变化。结果表明，使用DADT提升了公平性。