AI Practitioner

1. Introduction to Explainable Artificial Intelligence (XAI) Explainable Artificial Intelligence (XAI) refers to techniques that make the decision-making process of AI models interpretable and understandable to humans. Despite their high performance, image classification models based on Convolutional Neural Networks (CNNs) have often been criticized for operating as opaque "black boxes." To address this challenge, Class Activation Mapping (CAM) techniques have been developed. CAM enables visual interpretation of which specific regions of an input image influenced a model’s classification decision . These techniques are widely used for model interpretability , especially in critical fields like medical imaging, autonomous driving, and security , where trust and explainability are crucial. Methods such as CAM, Grad-CAM, and Score-CAM visually highlight the regions in an image that most contributed to the model’s prediction, helping explain what features the CNN has focused o...