On the stability, correctness and plausibility of visual explanation
  methods based on feature importance
Romain Xu-Darme Jenny Benois-Pineau Romain Giot Georges Quénot Zakaria Chihani Marie-Christine Rousset Alexey Zhukov
In the field of Explainable AI, multiples evaluation metrics have been
proposed in order to assess the quality of explanation methods w.r.t. a set of
desired properties. In this work, we study the articulation between the
stability, correctness and plausibility of explanations based on feature
importance for image classifiers. We show that the existing metrics for
evaluating these properties do not always agree, raising the issue of what
constitutes a good evaluation metric for explanations. Finally, in the
particular case of stability and correctness, we show the possible limitations
of some evaluation metrics and propose new ones that take into account the
local behaviour of the model under test.