A local surrogate for an AI-model correcting a simpler 'base' model is introduced representing an analytical method to yield explanations of AI-predictions. The approach is studied here in the context of the base model being linear regression. The AI-model approximates the residual error of the linear model and the explanations are formulated in terms of the change of the interpretable base model's parameters. Criteria are formulated for the precise relation between lost accuracy of the surrogate, the accuracy of the AI-model, and the surrogate fidelity. It is shown that, assuming a certain maximal amount of noise in the observed data, these criteria induce neighborhoods of the instances to be explained which have an ideal size in terms of maximal accuracy and fidelity.

翻译：提出一种针对AI模型的本地表征方法，该方法通过修正更简单的"基础"模型来表征AI预测的可解释性分析。本文以线性回归作为基础模型的情景展开研究：AI模型对线性模型的残差误差进行逼近，解释结果通过可解释基础模型参数的变化来表述。本文建立了描述表征模型精度损失、AI模型精度与表征保真度之间精确关系的准则。研究表明，在观测数据存在特定最大噪声量的假设下，这些准则可推导出待解释实例的邻域，该邻域在最大精度与保真度方面具有理想尺寸。