Global Explanation

Global explanation in machine learning aims to provide a comprehensive understanding of a model's overall behavior, rather than just explaining individual predictions. Current research focuses on developing methods that generate global explanations across various model architectures, including graph neural networks and deep learning models for image and point cloud data, often employing techniques like counterfactual reasoning, Shapley values, and activation maximization. These advancements are crucial for increasing trust and transparency in AI systems, particularly in high-stakes applications like healthcare and autonomous driving, by enabling better model debugging, bias detection, and ultimately, more reliable decision-making.