Perturbation Based

Perturbation-based methods are a crucial class of explainable AI techniques used to understand the decision-making processes of complex models, particularly in image classification, natural language processing, and graph neural networks. Current research focuses on improving the accuracy and efficiency of these methods, exploring variations in perturbation strategies (e.g., smooth masks, concept permutation, feature subspace analysis) and addressing limitations like vulnerability to adversarial attacks and the reliability of explanations for temporal data. These advancements are vital for building trust in AI systems across diverse applications, enabling better model debugging, bias detection, and ultimately, more responsible AI deployment.