Model Observability Needs Data-Trust Scoring for Reliable AI

Model Monitoring Has a Data Blind Spot Modern AI systems rarely fail with a clean, obvious error. They degrade through subtle changes: a feature arrives late, a label loses context, or a retrieval pipeline begins returning stale evidence. Conventional model observability catches downstream symptoms such as latency, drift, outliers, and declining evaluation scores. It often cannot answer the more important question: should the data behind this prediction be trusted? That gap matters because…