  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.
