论文标题
REX：将时间信息纳入模型不合时宜的本地解释技术中的框架
ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques
论文作者
论文摘要
现有的本地模型不足的解释技术对于考虑可变长度的输入的机器学习模型无效，因为它们不考虑这些模型中嵌入的时间信息。为了解决此限制，我们建议\ textsc {rex}，这是将时间信息纳入这些技术的一般框架。我们的关键见解是，这些技术通常通过采样模型输入和输出来学习模型替代，并且我们可以仅通过更改采样过程和替代特征来以统一的方式合并时间信息。我们对三种流行的解释技术进行实例化：锚点，石灰和内核。为了评估\ textsc {rex}的有效性，我们将方法应用于三个不同任务中的六个模型。我们的评估结果表明，我们的方法1）显着提高了解释的忠诚度，使模型不合时宜的技术在其目标模型上优于最先进的模型特定技术，而2）帮助最终用户更好地理解模型的行为。
Existing local model-agnostic explanation techniques are ineffective for machine learning models that consider inputs of variable lengths, as they do not consider temporal information embedded in these models. To address this limitation, we propose \textsc{ReX}, a general framework for incorporating temporal information in these techniques. Our key insight is that these techniques typically learn a model surrogate by sampling model inputs and outputs, and we can incorporate temporal information in a uniform way by only changing the sampling process and the surrogate features. We instantiate our approach on three popular explanation techniques: Anchors, LIME, and Kernel SHAP. To evaluate the effectiveness of \textsc{ReX}, we apply our approach to six models in three different tasks. Our evaluation results demonstrate that our approach 1) significantly improves the fidelity of explanations, making model-agnostic techniques outperform a state-of-the-art model-specific technique on its target model, and 2) helps end users better understand the models' behaviors.
