This article explores the critical intersection of knowledge graphs and data lineage in the context of modern AI and machine learning. It examines how combining these two technologies can provide the transparency and traceability required to build trustworthy AI systems. By mapping the origins, transformations, and movements of data, organizations can ensure better data quality, regulatory compliance, and improved model interpretability.
