可解释AI通过BAPC——校正前后参数比较
Explainable AI by BAPC -- Before and After correction Parameter Comparison
AI总结：
本文提出一种基于校正前后参数比较（BAPC）的局部替代模型方法，通过分析AI模型对线性回归基础模型残差的校正，以基础模型参数变化解释AI预测，并给出保真度与精度的理论准则。
摘要：
本文引入了一种局部替代模型，用于校正一个更简单的“基础”模型，这是一种用于生成AI预测解释的解析方法。本文在基础模型为线性回归的背景下研究了该方法。AI模型近似线性模型的残差误差，解释以可解释基础模型参数的变化来表述。本文提出了关于替代模型精度损失、AI模型精度与替代模型保真度之间精确关系的准则。结果表明，假设观测数据中存在某一最大噪声量，这些准则会在待解释实例的邻域中产生在最大精度和保真度方面具有理想大小的邻域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
