分与不分：分类中区别对待的影响
To Split or Not to Split: The Impact of Disparate Treatment in Classification
摘要：
当机器学习模型基于敏感属性（例如，年龄、性别）。在预测精度至关重要的领域，拟合表现出不同处理的模型可能是可以接受的。为了评估不同治疗的效果，我们比较了分裂分类器的性能（即，在每个组上分别训练和部署的分类器）与组盲分类器（即，不使用敏感属性的分类器）。我们介绍的好处，分裂量化的性能改善分裂分类器。直接从它的定义计算分裂的好处可能是棘手的，因为它涉及到解决无限维函数空间上的优化问题。在不同的性能指标下，我们（i）证明了一个等价的表达式，可以有效地计算通过解决小规模凸规划的分裂的好处;（ii）提供尖锐的上限和下限的分裂的好处，揭示精确的条件下，一个组盲分类器将总是遭受从分裂分类器的非平凡的性能差距。在有限样本制度，分裂不一定是有益的，我们提供了数据相关的界限来理解这种影响。最后，我们验证了我们的理论结果，通过数值实验合成和真实世界的数据集。
英文摘要：
Disparate treatment occurs when a machine learning model yields different decisions for individuals based on a sensitive attribute (e.g., age, sex). In domains where prediction accuracy is paramount, it could potentially be acceptable to fit a model which exhibits disparate treatment. To evaluate the effect of disparate treatment, we compare the performance of split classifiers (i.e., classifiers trained and deployed separately on each group) with group-blind classifiers (i.e., classifiers which do not use a sensitive attribute). We introduce the benefit-of-splitting for quantifying the performance improvement by splitting classifiers. Computing the benefit-of-splitting directly from its definition could be intractable since it involves solving optimization problems over an infinite-dimensional functional space. Under different performance measures, we (i) prove an equivalent expression for the benefit-of-splitting which can be efficiently computed by solving small-scale convex programs; (ii) provide sharp upper and lower bounds for the benefit-of-splitting which reveal precise conditions where a group-blind classifier will always suffer from a non-trivial performance gap from the split classifiers. In the finite sample regime, splitting is not necessarily beneficial and we provide data-dependent bounds to understand this effect. Finally, we validate our theoretical results through numerical experiments on both synthetic and real-world datasets.
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。
