Artificial Intelligence (AI) is often an integral part of modern decision support systems (DSSs). The best-performing predictive models used in AI-based DSSs lack transparency. Explainable Artificial Intelligence (XAI) aims to create AI systems that can explain their rationale to human users. Local explanations in XAI can provide information about the causes of individual predictions in terms of feature importance. However, a critical drawback of existing local explanation methods is their inability to quantify the uncertainty associated with a feature's importance. This paper introduces an extension of a feature importance explanation method, Calibrated Explanations (CE), previously only supporting classification, with support for standard regression and probabilistic regression, i.e., the probability that the target is above an arbitrary threshold. The extension for regression keeps all the benefits of CE, such as calibration of the prediction from the underlying model with confidence intervals, uncertainty quantification of feature importance, and allows both factual and counterfactual explanations. CE for standard regression provides fast, reliable, stable, and robust explanations. CE for probabilistic regression provides an entirely new way of creating probabilistic explanations from any ordinary regression model and with a dynamic selection of thresholds. The performance of CE for probabilistic regression regarding stability and speed is comparable to LIME. The method is model agnostic with easily understood conditional rules. An implementation in Python is freely available on GitHub and for installation using pip making the results in this paper easily replicable.

翻译：人工智能（AI）通常是现代决策支持系统（DSSs）不可或缺的组成部分。基于AI的决策支持系统中所使用的高性能预测模型缺乏透明度。可解释人工智能（XAI）旨在创建能够向人类用户解释其推理逻辑的AI系统。XAI中的局部解释能从特征重要性角度提供关于单个预测成因的信息。然而，现有局部解释方法的一个关键缺陷是它们无法量化与特征重要性相关的不确定性。本文提出了一种特征重要性解释方法——校准解释（CE）的扩展方案，该方法先前仅支持分类任务，现扩展支持标准回归与概率回归（即目标值超过任意阈值的概率）。回归任务的扩展保留了CE的所有优势，例如：包含置信区间的底层模型预测校准、特征重要性的不确定性量化，以及支持事实解释和反事实解释。适用于标准回归的CE可提供快速、可靠、稳定且鲁棒的局部解释。适用于概率回归的CE则提供了一种全新方法，能从任何普通回归模型中生成概率解释，并支持阈值的动态选择。就稳定性和速度而言，概率回归CE的性能与LIME相当。该方法具有模型无关性，并辅以易于理解的条件规则。Python实现代码已在GitHub上开源，并支持通过pip安装，使得本文结果易于复现。