We often see the term explainable in the titles of papers that describe applications based on artificial intelligence (AI). However, the literature in explainable artificial intelligence (XAI) indicates that explanations in XAI are application- and domain-specific, hence requiring evaluation whenever they are employed to explain a model that makes decisions for a specific application problem. Additionally, the literature reveals that the performance of post-hoc methods, particularly feature attribution methods, varies substantially hinting that they do not represent a solution to AI explainability. Therefore, when using XAI methods, the quality and suitability of their information outputs should be evaluated within the specific application. For these reasons, we used a scoping review methodology to investigate papers that apply AI models and adopt methods to generate post-hoc explanations while referring to said models as explainable. This paper investigates whether the term explainable model is adopted by authors under the assumption that incorporating a post-hoc XAI method suffices to characterize a model as explainable. To inspect this problem, our review analyzes whether these papers conducted evaluations. We found that 81% of the application papers that refer to their approaches as an explainable model do not conduct any form of evaluation on the XAI method they used.

翻译：我们常在基于人工智能（AI）的应用论文标题中看到“可解释”一词。然而，可解释人工智能（XAI）领域的研究表明，XAI中的解释具有应用和领域特异性，因此每当将其用于解释特定应用问题决策的模型时，都需要进行评估。此外，文献揭示事后解释方法（尤其是特征归因方法）的性能差异显著，说明它们并非AI可解释性的解决方案。因此，在使用XAI方法时，应在具体应用场景中评估其信息输出的质量和适用性。基于这些原因，我们采用范围综述方法，调查那些应用AI模型并采用事后解释方法，同时将所述模型称为“可解释模型”的论文。本文旨在探究作者是否在“采用事后XAI方法便足以将模型定性为可解释模型”的假设下使用该术语。为审视此问题，本综述分析了这些论文是否进行了评估。我们发现，81% 将自身方法称为可解释模型的应用论文未对其采用的XAI方法进行任何形式的评估。