  Strategies based on Explainable Artificial Intelligence - XAI have promoted
better human interpretability of the results of black box machine learning
models. The XAI measures being currently used (Ciu, Dalex, Eli5, Lofo, Shap,
and Skater) provide various forms of explanations, including global rankings of
relevance of attributes. Current research points to the need for further
studies on how these explanations meet the Interpretability Expectations of
human experts and how they can be used to make the model even more transparent
while taking into account specific complexities of the model and dataset being
analyzed, as well as important human factors of sensitive real-world
contexts/problems. Intending to shed light on the explanations generated by XAI
measures and their interpretabilities, this research addresses a real-world
classification problem related to homicide prediction, duly endorsed by the
scientific community, replicated its proposed black box model and used 6
different XAI measures to generate explanations and 6 different human experts
to generate what this research referred to as Interpretability Expectations -
IE. The results were computed by means of comparative analysis and
identification of relationships among all the attribute ranks produced, and
~49% concordance was found among attributes indicated by means of XAI measures
and human experts, ~41% exclusively by XAI measures and ~10% exclusively by
human experts. The results allow for answering: "Do the different XAI measures
generate similar explanations for the proposed problem?", "Are the
interpretability expectations generated among different human experts
similar?", "Do the explanations generated by XAI measures meet the
interpretability expectations of human experts?" and "Can Interpretability
Explanations and Expectations work together?", all of which concerning the
context of homicide prediction.

Machine Learning;Artificial Intelligence