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.

翻译：随着计算高效人工智能技术的发展及其在日常生活中的广泛应用，人们迫切需要透过更详细的解释，理解黑箱式人工智能技术（如主流机器学习和深度学习技术）中隐藏的计算细节。可解释人工智能（xAI）的起源正是源于这些挑战，近年通过将可解释性全面融入传统人工智能系统而获得研究者更多关注。这促使我们针对创新、风险缓解、伦理问题及用户逻辑价值等维度，构建适用于现实场景的xAI应用框架。本章深入分析了包括LIME（局部可解释模型无关解释）和SHAP（沙普利加性解释）在内的多种xAI框架与方法，并以黑箱式随机森林分类器作为人工智能模型，结合LIME和SHAP对公开的糖尿病症状数据集进行更优解释。在糖尿病疾病预测方面，所获结果在透明度、有效性和可信度上展现出显著价值。