Why AI Cannot Explain Decisions Without Graphs

Enterprise AI explainability is presented as primarily a data infrastructure challenge, since regulators and auditors need to understand the business entities, evidence, and relationships behind AI-assisted decisions rather than only a model’s internal calculations. The material argues that SHAP, LIME, attention visualizations, and RAG citations can improve insight into features, model focus, or source documents, but do not provide complete relationship-based reasoning chains for complex business decisions. Graph databases address this gap by explicitly storing connected entities such as customers, accounts, suppliers, transactions, products, and patients, enabling AI recommendations to be traced through queryable relationship paths. This approach is positioned as particularly relevant to banking, healthcare, and insurance, where frameworks including the EU AI Act, SR 11-7, HIPAA, and Basel IV emphasize auditability, oversight, and documented decision processes. TigerGraph’s GraphRAG is described as combining graph and vector retrieval to provide real-time, relationship-aware context and durable audit trails for enterprise AI systems.

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