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.

黑盒模型、可解释人工智能方法（XAI 方法）、eXirt、扰动等是本研究的关键词，通过实验和生成解释以评估 XAI 方法的稳定性和可靠性。结果表明，eXirt 能够识别出最可靠的模型，同时发现当前 XAI 方法对扰动敏感，但存在一个特定方法例外。