The field of AI-driven sports analytics and networking is moving towards increased explainability and transparency. Researchers are developing innovative methods to provide insights into the decision-making processes of AI agents, enabling more trustworthy and reliable systems. This trend is evident in the development of frameworks that integrate explainability into reinforcement learning, such as those using object-centric representations and transparent multi-agent reasoning processes. Additionally, there is a growing focus on creating interpretable and correctable representations of complex systems, such as collaborative physical activities and sports tactics. Noteworthy papers in this area include: MAGIC-MASK, which proposes a mathematically grounded framework for explainability in multi-agent reinforcement learning. Expandable Decision-Making States, which introduces a semantically enriched state representation for multi-agent deep reinforcement learning in soccer tactical analysis. TriAlignXA, which presents an explainable trilemma alignment framework for trustworthy agri-product grading.