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In recent years, XAI researchers have been formalizing proposals anddeveloping new methods to explain black box models, with no general consensusin the community on which method to use to explain these models, with thischoice being almost directly linked to the popularity of a specific method.Methods such as Ciu, Dalex, Eli5, Lofo, Shap and Skater emerged with theproposal to explain black box models through global rankings of featurerelevance, which based on different methodologies, generate global explanationsthat indicate how the model's inputs explain its predictions. In this context,41 datasets, 4 tree-ensemble algorithms (Light Gradient Boosting, CatBoost,Random Forest, and Gradient Boosting), and 6 XAI methods were used to supportthe launch of a new XAI method, called eXirt, based on Item Response Theory -IRT and aimed at tree-ensemble black box models that use tabular data referringto binary classification problems. In the first set of analyses, the 164 globalfeature relevance ranks of the eXirt were compared with 984 ranks of the otherXAI methods present in the literature, seeking to highlight their similaritiesand differences. In a second analysis, exclusive explanations of the eXirtbased on Explanation-by-example were presented that help in understanding themodel trust. Thus, it was verified that eXirt is able to generate globalexplanations of tree-ensemble models and also local explanations of instancesof models through IRT, showing how this consolidated theory can be used inmachine learning in order to obtain explainable and reliable models.

Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective