Abstract

The most popular methods in AI-machine learning paradigm are mainly black boxes. This is
why explanation of AI decisions is of emergency. Although dedicated explanation tools have
been massively developed, the evaluation of their quality remains an open research question. In
this paper, we the generalize the methodologies of evaluation of post-hoc explainers of CNNs’
decisions in visual classification tasks with reference and no-reference based metrics. We apply
them on our previously developed explainers (FEM, MLFEM), and popular Grad-CAM. The
reference-based metrics are Pearson correlation coefficient and Similarity computed between
the explanation map and its ground truth represented by a Gaze Fixation Density Map obtained
with a psycho-visual experiment. As a no-reference metric we use stability metric, proposed by
Alvarez-Melis and Jaakkola. We study its behaviour, consensus with reference-based metrics
and show that in case of several kind of degradation on input images, this metric is in agreement
with reference-based ones. Therefore it can be used for evaluation of the quality of explainers
when the ground truth is not available.

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