The field of artificial intelligence is moving towards developing more interpretable and transparent models. This is evident in the increasing focus on techniques that provide insights into model decisions and behaviors. Recent developments have led to the creation of frameworks and methods that enhance model interpretability, such as multi-modal explainability and concept-based explanation. These approaches aim to provide a deeper understanding of how models process complex data and make decisions. Noteworthy papers in this area include: Multi-Modal Interpretability for Enhanced Localization in Vision-Language Models, which introduces a novel framework for enhancing model interpretability. ConceptFlow: Hierarchical and Fine-grained Concept-Based Explanation for Convolutional Neural Networks, which proposes a concept-based interpretability framework that simulates the internal thinking path of a model. Smaller is Better: Enhancing Transparency in Vehicle AI Systems via Pruning, which demonstrates that pruning can significantly enhance the comprehensibility and faithfulness of explanations in AI models. These papers highlight the importance of developing more interpretable and transparent AI models, and demonstrate the potential of various techniques for achieving this goal.