From Indicator to Claim: Validating the Behavioral Constructs in AI Assurance Frameworks

AI standards, frameworks, and guardrails increasingly ask organizations to show that a claim holds, and many of those claims concern human behavior: that oversight is meaningful, that disclosures are understood, that users rely on a system appropriately. This white paper argues that such behavioral claims are only as strong as the validity argument behind their indicators. Adapting the measurement framework of Wallach et al. (2025), it proposes a method for reading a requirement in a standard, and the evidence offered under it, as a chain from concept to definition, instrument, measurement, and inference. It applies the method to the NIST AI Risk Management Framework and the EU AI Act, catalogs known threats to behavioral evidence, and proposes evidence tiers that scale with the consequence of being wrong. The method is unvalidated and its transfer to AI settings is a hypothesis. Whether current assurance practice falls short is an open empirical question, and the paper recommends piloting the review protocol before it is relied on.