Artificial Intelligence (AI) has become an integral part of decision support systems (DSSs) in various domains, but the lack of transparency in the predictive models used in AI-based DSSs can lead to misuse or disuse. 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, but they suffer from drawbacks such as instability. To address these issues, we propose a new feature importance explanation method, Calibrated Explanations (CE), which is based on Venn-Abers and calibrates the underlying model while generating feature importance explanations. CE provides fast, reliable, stable, and robust explanations, along with uncertainty quantification of the probability estimates and feature importance weights. Furthermore, the method is model agnostic with easily understood conditional rules and can also generate counterfactual explanations with uncertainty quantification.

翻译：人工智能（AI）已成为各领域决策支持系统（DSSs）的重要组成部分，但基于AI的决策支持系统中所用预测模型缺乏透明度，可能导致误用或弃用。可解释人工智能（XAI）旨在创建能向人类用户解释其决策依据的AI系统。XAI中的局部解释可从特征重要性角度提供关于个体预测成因的信息，但存在不稳定性等缺陷。为解决这些问题，我们提出一种新的特征重要性解释方法——校准解释（CE），该方法基于Venn-Abers方法，在生成特征重要性解释的同时对底层模型进行校准。CE能提供快速、可靠、稳定且鲁棒的解释，同时包含概率估计与特征重要性权重的不确定性量化。此外，该方法具有模型无关性，附带易于理解的条件规则，并能生成具有不确定性量化的反事实解释。