An Empirical Evaluation of AI Deep Explainable Tools

Success in machine learning has led to a wealth of Artificial Intelligence (AI) systems. A great deal of attention is currently being set on the development of advanced Machine Learning (ML)-based solutions for a variety of automated predictions and classification tasks in a wide array of industries. However, such automated applications may introduce bias in results, making it risky to use these ML models in security-and privacy-sensitive domains. The prediction should be accurate and models have to be interpretable/explainable to understand how they work. In this research, we conduct an empirical evaluation of two major explainer/interpretable methods called LIME and SHAP on two datasets using deep learning models, including Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). The results demonstrated that SHAP performs slightly better than LIME in terms of Identity, Stability, and Separability from two different datasets (Breast Cancer Wisconsin (Diagnostic) and NIH Chest X-Ray) that we used.