An Experimental Investigation into the Evaluation of Explainability
  Methods
Sédrick Stassin Alexandre Englebert Géraldin Nanfack Julien Albert Nassim Versbraegen Gilles Peiffer Miriam Doh Nicolas Riche Benoît Frenay Christophe De Vleeschouwer
EXplainable Artificial Intelligence (XAI) aims to help users to grasp the
reasoning behind the predictions of an Artificial Intelligence (AI) system.
Many XAI approaches have emerged in recent years. Consequently, a subfield
related to the evaluation of XAI methods has gained considerable attention,
with the aim to determine which methods provide the best explanation using
various approaches and criteria. However, the literature lacks a comparison of
the evaluation metrics themselves, that one can use to evaluate XAI methods.
This work aims to fill this gap by comparing 14 different metrics when applied
to nine state-of-the-art XAI methods and three dummy methods (e.g., random
saliency maps) used as references. Experimental results show which of these
metrics produces highly correlated results, indicating potential redundancy. We
also demonstrate the significant impact of varying the baseline hyperparameter
on the evaluation metric values. Finally, we use dummy methods to assess the
reliability of metrics in terms of ranking, pointing out their limitations.