It is well-known that real-world changes constituting distribution shift adversely affect model performance. How to characterize those changes in an interpretable manner is poorly understood. Existing techniques to address this problem take the form of shift explanations that elucidate how to map samples from the original distribution toward the shifted one by reducing the disparity between these two distributions. However, these methods can introduce group irregularities, leading to explanations that are less feasible and robust. To address these issues, we propose Group-aware Shift Explanations (GSE), a method that produces interpretable explanations by leveraging worst-group optimization to rectify group irregularities. We demonstrate how GSE not only maintains group structures, such as demographic and hierarchical subpopulations, but also enhances feasibility and robustness in the resulting explanations in a wide range of tabular, language, and image settings.

翻译：众所周知，构成分布偏移的现实变化会严重影响模型性能。如何以可解释的方式描述这些变化尚未得到充分理解。现有应对该问题的技术采用偏移解释的形式，通过减少原始分布与偏移分布之间的差异，阐明如何将样本从原始分布映射至偏移分布。然而，这些方法可能引入群体不规则性，导致解释的可行性和鲁棒性降低。为解决这些问题，我们提出群体感知偏移解释（GSE），该方法利用最差群体优化来修正群体不规则性，从而生成可解释的解释。我们证明，在表格、语言和图像等多种场景中，GSE不仅能够维持群体结构（如人口统计及层级子群体），还能显著提升所得解释的可行性与鲁棒性。