How Reliable and Stable are Explanations of XAI Methods?

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.

Global Explanation of Tree-Ensembles Models Based on Item Response Theory

Explainable Artificial Intelligence - XAI is aimed at studying and developing techniques to explain black box models, that is, models that provide limited self-explanation of their predictions. In recent years, XAI researchers have been formalizing proposals and developing new measures to explain how these models make specific predictions. In previous studies, evidence has been found on how model (dataset and algorithm) complexity affects global explanations generated by XAI measures Ciu, Dalex, Eli5, Lofo, Shap and Skater, suggesting that there is room for the development of a new XAI measure that builds on the complexity of the model. Thus, this research proposes a measure called Explainable based on Item Response Theory - eXirt, which is capable of explaining tree-ensemble models by using the properties of Item Response Theory (IRT). For this purpose, a benchmark was created using 40 different datasets and 2 different algorithms (Random Forest and Gradient Boosting), thus generating 6 different explainability ranks using known XAI measures along with 1 data purity rank and 1 rank of the measure eXirt, amounting to 8 global ranks for each model, i.e., 640 ranks altogether. The results show that eXirt displayed different ranks than those of the other measures, which demonstrates that the advocated methodology generates global explanations of tree-ensemble models that have not yet been explored, either for the more difficult models to explain or even the easier ones.