  Black box models are increasingly being used in the daily lives of human
beings living in society. Along with this increase, there has been the
emergence of Explainable Artificial Intelligence (XAI) methods aimed at
generating additional explanations regarding how the model makes certain
predictions. In this sense, methods such as Dalex, Eli5, eXirt, Lofo and Shap
emerged as different proposals and methodologies for generating explanations of
black box models in an agnostic way. Along with the emergence of these methods,
questions arise such as "How Reliable and Stable are XAI Methods?". With the
aim of shedding light on this main question, this research creates a pipeline
that performs experiments using the diabetes dataset and four different machine
learning models (LGBM, MLP, DT and KNN), creating different levels of
perturbations of the test data and finally generates explanations from the
eXirt method regarding the confidence of the models and also feature relevances
ranks from all XAI methods mentioned, in order to measure their stability in
the face of perturbations. As a result, it was found that eXirt was able to
identify the most reliable models among all those used. It was also found that
current XAI methods are sensitive to perturbations, with the exception of one
specific method.
