NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-MS-4.1: Measurement approaches for identifying AI risks are connected to deployment contexts and informed through consultation with domain experts and other end users, and approaches are documented
Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and other end users. Approaches are documented. The measurement design is informed by people who understand the deployment context, because the risks that matter there are often not visible to those running the evaluation.
Maintained by Gerard Blokdyk·Verified against the published standard ·Control text last updated
What else in your programme already covers this
This control maps to 2 controls across 2 other frameworks. If you already hold one of them, the evidence you collected for it is the starting point here rather than new work.
You are reading one control. How much of NIST AI Risk Management Framework (AI RMF 1.0) have you already done?
NIST AI Risk Management Framework (AI RMF 1.0) AIRMF-MS-4.1 is one control. If you already hold one of the frameworks below, a reviewed crosswalk already says how much of NIST AI Risk Management Framework (AI RMF 1.0) your existing evidence covers. Hold EU AI Act and 48 of 72 NIST AI Risk Management Framework (AI RMF 1.0) controls already carry evidence.
Each report names every control your existing framework evidences, every one it does not, the reasoning behind each claim, and the claims that were argued against and rejected. 10 were rejected on the EU AI Act pair alone.