The fields of deep learning, safety-critical systems, distributed system monitoring, machine learning, financial forecasting, multimodal analysis, computer vision, and human motion analysis are witnessing significant advancements in explainability and interpretability. A common theme among these domains is the development of innovative techniques to provide insights into the decision-making processes of complex models. Noteworthy papers include those on interpretable deep learning frameworks for breast cancer detection, out-of-distribution detection techniques, and explainable AI methods for financial forecasting and decision-making. Additionally, researchers are exploring the use of tensor networks, Shapley values, and counterfactual explanations to improve the transparency and understanding of machine learning models. The integration of multimodal data and the development of more effective fusion strategies are also trending in industrial computer vision and time series forecasting. Overall, these developments highlight the potential of explainable and interpretable models to increase trust, reliability, and performance across various applications.