The paper "Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models" by Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller discusses the importance of interpretability in artificial intelligence (AI) systems, particularly deep learning models. Despite the impressive performance of these models in various tasks, their black-box nature makes it difficult to understand how they arrive at their predictions, which can be a significant drawback in applications such as medical diagnosis and autonomous driving. The authors highlight the need for explainable AI to address issues like verification, improvement, learning, and compliance with legislation. The paper introduces two methods for explaining deep learning model predictions: Sensitivity Analysis (SA) and Layer-wise Relevance Propagation (LRP). SA quantifies the sensitivity of predictions to changes in input variables, while LRP decomposes the prediction into the relevance of each input variable. The evaluation of these methods on image classification, text document classification, and human action recognition tasks demonstrates that LRP provides more informative and interpretable explanations compared to SA. The authors also propose a quality measure for explanations based on perturbation analysis, which assesses the effectiveness of explanation methods by tracking the prediction score after perturbing input variables. The experimental results show that LRP heatmaps, which visualize the importance of each pixel or word, are more effective in explaining predictions than SA heatmaps. Finally, the paper concludes by emphasizing the importance of explainability in AI, noting its role in detecting model flaws, verifying predictions, improving models, and gaining new insights. Future work will focus on theoretical foundations, integrating explainability into model structures, and applying these methods to new domains.The paper "Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models" by Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller discusses the importance of interpretability in artificial intelligence (AI) systems, particularly deep learning models. Despite the impressive performance of these models in various tasks, their black-box nature makes it difficult to understand how they arrive at their predictions, which can be a significant drawback in applications such as medical diagnosis and autonomous driving. The authors highlight the need for explainable AI to address issues like verification, improvement, learning, and compliance with legislation. The paper introduces two methods for explaining deep learning model predictions: Sensitivity Analysis (SA) and Layer-wise Relevance Propagation (LRP). SA quantifies the sensitivity of predictions to changes in input variables, while LRP decomposes the prediction into the relevance of each input variable. The evaluation of these methods on image classification, text document classification, and human action recognition tasks demonstrates that LRP provides more informative and interpretable explanations compared to SA. The authors also propose a quality measure for explanations based on perturbation analysis, which assesses the effectiveness of explanation methods by tracking the prediction score after perturbing input variables. The experimental results show that LRP heatmaps, which visualize the importance of each pixel or word, are more effective in explaining predictions than SA heatmaps. Finally, the paper concludes by emphasizing the importance of explainability in AI, noting its role in detecting model flaws, verifying predictions, improving models, and gaining new insights. Future work will focus on theoretical foundations, integrating explainability into model structures, and applying these methods to new domains.