Measure What You Need and Set Reassessment Triggers
Understand how to measure AI system performance effectively by linking evidence to intended use and context. Learn to set reassessment triggers to keep evidence current and manage risks proactively, using the NIST AI Risk Management Framework in practical scenarios.
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A slide that says “92% accurate” looks like a decision, so it often gets treated like one. The mistake is not caring about evidence. The mistake is treating a single performance number as if it transfers cleanly into a specific mission workflow, with specific constraints, reviewers, and consequences.
The NIST AI RMF function called Measure is where that mistake shows up. Measure is not about admiring a score. Measure is about deciding what evidence is needed for this intended use in this context, so a reviewer can see why reliance is justified. A generic number can still be useful, but only after it is tied to the way the output will actually be used and checked.
A supervisor who wants to proceed on “92%” is usually trying to reduce delay. The hidden cost is that the team may not notice the 8% until it lands on the wrong case, in the wrong queue, under the wrong time pressure. The earlier Map work made the intended use boundary and constraints explicit, which means the evidence can now be chosen to match them instead of floating above them.
Turn risk questions into evidence you can actually collect
You do not need measurement jargon to do Measure well. You need a clean link between a risk question and an evidence artifact that ...