Modern applications are no longer isolated or static, but deeply interconnected. They traverse multiple layers, spanning from cloud to edge, and interact with a growing. The size and complexity of their building parts, i.e., data, logic, and infrastructure, are unprecedented. This evolution requires learning mechanisms for automating and coordinating the functioning of such wide systems. Still, these learned models have to be not only dynamic and precise, but — very importantly — trustworthy and transparent. It is essential to develop tools and strategies to understand how decisions were made and build a solid trust in the automated components that manage such systems to achieve this objective. This keynote will look into these aspects, highlighting methods for explainability for modern distributed systems, spanning from network traffic to distributed applications on cloud infrastructure, looking at where the understanding of their functioning, plus the trust, play an essential role.