Post-hoc explanation techniques such as the well-established partial dependence plot (PDP), which investigates feature dependencies, are used in explainable artificial intelligence (XAI) to understand black-box machine learning models. While many real-world applications require dynamic models that constantly adapt over time and react to changes in the underlying distribution, XAI, so far, has primarily considered static learning environments, where models are trained in a batch mode and remain unchanged. We thus propose a novel model-agnostic XAI framework called incremental PDP (iPDP) that extends on the PDP to extract time-dependent feature effects in non-stationary learning environments. We formally analyze iPDP and show that it approximates a time-dependent variant of the PDP that properly reacts to real and virtual concept drift. The time-sensitivity of iPDP is controlled by a single smoothing parameter, which directly corresponds to the variance and the approximation error of iPDP in a static learning environment. We illustrate the efficacy of iPDP by showcasing an example application for drift detection and conducting multiple experiments on real-world and synthetic data sets and streams.

翻译：在可解释人工智能（XAI）中，诸如成熟的部分依赖图（PDP）等事后解释技术被用于分析特征依赖性，以理解黑箱机器学习模型。尽管许多实际应用需要能够随时间持续适应并对底层分布变化做出反应的动态模型，但XAI目前主要关注静态学习环境——即模型以批量方式训练后保持不变。为此，我们提出了一种新颖的模型无关XAI框架——增量式部分依赖图（iPDP），该框架在PDP基础上扩展，能够提取非平稳学习环境中随时间变化的特征效应。我们从理论上分析了iPDP，证明其能近似一种时间相关的PDP变体，可正确响应真实概念漂移与虚拟概念漂移。iPDP的时间敏感性由单个平滑参数控制，该参数直接对应于静态学习环境下iPDP的方差与近似误差。通过展示漂移检测的示例应用，以及在真实数据集、合成数据集与数据流上开展的多项实验，我们验证了iPDP的有效性。