This paper provides new theory to support to the eXplainable AI (XAI) method Contextual Importance and Utility (CIU). CIU arithmetic is based on the concepts of Multi-Attribute Utility Theory, which gives CIU a solid theoretical foundation. The novel concept of contextual influence is also defined, which makes it possible to compare CIU directly with so-called additive feature attribution (AFA) methods for model-agnostic outcome explanation. One key takeaway is that the "influence" concept used by AFA methods is inadequate for outcome explanation purposes even for simple models to explain. Experiments with simple models show that explanations using contextual importance (CI) and contextual utility (CU) produce explanations where influence-based methods fail. It is also shown that CI and CU guarantees explanation faithfulness towards the explained model.

本文提出了一个新理论，支持可解释AI（XAI）方法的情境重要性和效用（CIU）。CIU算法基于多属性效用理论的概念，为CIU提供了坚实的理论基础。同时，也定义了新颖的情境影响概念，使CIU能够直接与模型无关的加法特征归因方法进行比较。实验表明，使用CI和CU的解释可以产生CIU方法解释失败的影响方法解释。此外，还表明CI和CU保证向解释模型的说明忠实。

情境重要性与效用：理论基础