Validity and Reliability as Trustworthiness Foundation

The NIST AI RMF explicitly positions “Valid & Reliable” as the necessary foundation upon which all other trustworthiness characteristics depend.

The architectural logic: an AI system cannot be meaningfully safe, fair, or explainable if it doesn’t first perform as intended under expected conditions. Security, privacy, accountability, and fairness are all contingent on the system doing what it’s supposed to do.

This hierarchy has practical implications for prioritization. Organizations chasing fairness metrics or explainability features before establishing baseline validity are building on sand.

The pattern appears elsewhere in engineering: you can’t optimize what you can’t measure, and you can’t measure what doesn’t work reliably.

Related: 05-atom—trustworthy-ai-characteristics, 05-atom—ai-risk-measurement-challenges