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，一个已知问题是目标部署人群可能未在模型训练的源人群中得到充分体现。这可能导致部署后的模型产生偏差，从而降低模型性能。其中一个风险是，随着人群变化，某些人口统计群体即使在目标人群中占比增加，仍可能被模型服务不足或处于不利地位。领域自适应领域针对目标人群标签数据不存在、但已知部分目标分布信息的情况提出了相应技术。本文通过引入域自适应决策树（DADT）为领域自适应文献做出贡献。鉴于决策树因其可解释性以及相比其他更复杂模型更优的性能而日益普及，我们聚焦于决策树研究。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。我们提出一种处理内步骤，该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。我们在真实数据上验证DADT，发现在偏移的目标人群中测试时，其准确性优于标准决策树。我们还研究了在人口统计平等和机会均等条件下的公平性变化。结果表明，使用DADT可提升公平性。