  Automated Machine Learning-based systems' integration into a wide range of
tasks has expanded as a result of their performance and speed. Although there
are numerous advantages to employing ML-based systems, if they are not
interpretable, they should not be used in critical, high-risk applications
where human lives are at risk. To address this issue, researchers and
businesses have been focusing on finding ways to improve the interpretability
of complex ML systems, and several such methods have been developed. Indeed,
there are so many developed techniques that it is difficult for practitioners
to choose the best among them for their applications, even when using
evaluation metrics. As a result, the demand for a selection tool, a
meta-explanation technique based on a high-quality evaluation metric, is
apparent. In this paper, we present a local meta-explanation technique which
builds on top of the truthfulness metric, which is a faithfulness-based metric.
We demonstrate the effectiveness of both the technique and the metric by
concretely defining all the concepts and through experimentation.
