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

Truthful Meta-Explanations for Local Interpretability of Machine Learning Models