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.

通过解释性更详细的说明，理解在黑匣子人工智能技术中隐藏的计算细节对于我们日常生活中计算效率高的人工智能技术及其各种应用至关重要。该论文分析了几种解释性人工智能框架和方法，包括LIME（局部可解释的模型无关解释）和SHAP（Shapley附加解释），并使用随机森林分类器作为黑匣子人工智能在公开的糖尿病症状数据集上进行更好的解释和透明度进行糖尿病疾病预测。

基于随机森林分类器、LIME和SHAP的可解释人工智能在医疗应用中的应用