标题： TaylorPODA：一种基于泰勒展开的改进黑盒模型事后归因的方法 显示英文标题

标题： TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models

摘要： 现有事后模型无关方法通过局部归因模型输出到其输入特征，生成对不透明模型的外部解释。 然而，它们通常缺乏一种明确且系统的框架来量化单个特征的贡献。 在Deng等人（2024）提出的统一现有局部归因方法的泰勒展开框架基础上，我们提出了一组严格的公理——“精度”、“联邦”和“零差异”——以规范泰勒项特定的归因。 在这些公理的指导下，我们引入了TaylorPODA（泰勒展开派生的重要性顺序适应归因），其中包含一个额外的“适应”属性。 该属性使模型能够与任务特定的目标对齐，特别是在缺乏真实解释的事后设置中。 实证评估表明，TaylorPODA在与基线方法的比较中表现出了竞争力，提供了有理论依据且易于可视化的解释。 这项工作通过提供具有更强理论基础的解释，朝着不透明模型的可信部署迈出了一步。 显示英文摘要

摘要： Existing post-hoc model-agnostic methods generate external explanations for opaque models, primarily by locally attributing the model output to its input features. However, they often lack an explicit and systematic framework for quantifying the contribution of individual features. Building on the Taylor expansion framework introduced by Deng et al. (2024) to unify existing local attribution methods, we propose a rigorous set of postulates -- "precision", "federation", and "zero-discrepancy" -- to govern Taylor term-specific attribution. Guided by these postulates, we introduce TaylorPODA (Taylor expansion-derived imPortance-Order aDapted Attribution), which incorporates an additional "adaptation" property. This property enables alignment with task-specific goals, especially in post-hoc settings lacking ground-truth explanations. Empirical evaluations demonstrate that TaylorPODA achieves competitive results against baseline methods, providing principled and visualization-friendly explanations. This work represents a step toward the trustworthy deployment of opaque models by offering explanations with stronger theoretical grounding.