论文标题
关于“为什么？”和“为什么不呢？”解释
On Relating 'Why?' and 'Why Not?' Explanations
论文作者
论文摘要
机器学习（ML）模型的解释通常会解决“为什么？”问题。这种解释可能与选择特征值对相关，这足以进行预测。最近的工作调查了解决“为什么不呢？”的解释。问题，即找到可以保证更改预测的特征值的变化。鉴于他们的目标，这两种解释ML模型预测的形式似乎主要是无关的。但是，本文证明了另外，并在“为什么？”之间建立了严格的形式关系。和“为什么不呢？”解释。具体而言，本文证明，对于任何给定的实例，“为什么？”解释是最小击中“为什么不呢？”的一组。解释，反之亦然。此外，该论文设计了用于提取和列举两种形式的解释的新型算法。
Explanations of Machine Learning (ML) models often address a 'Why?' question. Such explanations can be related with selecting feature-value pairs which are sufficient for the prediction. Recent work has investigated explanations that address a 'Why Not?' question, i.e. finding a change of feature values that guarantee a change of prediction. Given their goals, these two forms of explaining predictions of ML models appear to be mostly unrelated. However, this paper demonstrates otherwise, and establishes a rigorous formal relationship between 'Why?' and 'Why Not?' explanations. Concretely, the paper proves that, for any given instance, 'Why?' explanations are minimal hitting sets of 'Why Not?' explanations and vice-versa. Furthermore, the paper devises novel algorithms for extracting and enumerating both forms of explanations.
