  Strategies based on Explainable Artificial Intelligence - XAI have emerged in
computing to promote a better understanding of predictions made by black box
models. Most XAI measures used today explain these types of models, generating
attribute rankings aimed at explaining the model, that is, the analysis of
Attribute Importance of Model. There is no consensus on which XAI measure
generates an overall explainability rank. For this reason, several proposals
for tools have emerged (Ciu, Dalex, Eli5, Lofo, Shap and Skater). An
experimental benchmark of explainable AI techniques capable of producing global
explainability ranks based on tabular data related to different problems and
ensemble models are presented herein. Seeking to answer questions such as "Are
the explanations generated by the different measures the same, similar or
different?" and "How does data complexity play along model explainability?" The
results from the construction of 82 computational models and 592 ranks shed
some light on the other side of the problem of explainability: dataset
complexity!
