  With the advances in computationally efficient artificial Intelligence (AI)
techniques and their numerous applications in our everyday life, there is a
pressing need to understand the computational details hidden in black box AI
techniques such as most popular machine learning and deep learning techniques;
through more detailed explanations. The origin of explainable AI (xAI) is
coined from these challenges and recently gained more attention by the
researchers by adding explainability comprehensively in traditional AI systems.
This leads to develop an appropriate framework for successful applications of
xAI in real life scenarios with respect to innovations, risk mitigation,
ethical issues and logical values to the users. In this book chapter, an
in-depth analysis of several xAI frameworks and methods including LIME (Local
Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive
exPlanations) are provided. Random Forest Classifier as black box AI is used on
a publicly available Diabetes symptoms dataset with LIME and SHAP for better
interpretations. The results obtained are interesting in terms of transparency,
valid and trustworthiness in diabetes disease prediction.
