A human checkpoint should be designed as a real decision, not a ceremonial approval button.
Choose the decision that needs review
Start with the consequences of a wrong result and the point where an intervention can prevent them. Reviewing a draft before it is sent is different from inspecting a log after the message has reached a customer. The appropriate checkpoint depends on the workflow. A team should be able to explain what the reviewer is expected to decide and what the system will do with that decision.
Give the reviewer useful evidence
Show the proposed action, the material source information and any unresolved uncertainty. Do not force a reviewer to reconstruct the entire task from a long transcript. In a hypothetical claims-processing tool, a summary should link back to the relevant records and identify which parts are model interpretation. This helps the reviewer challenge the result rather than merely judge whether it sounds plausible.
A review step that matters
Timing
Intervene before the consequential action.
Evidence
Show sources and uncertainty.
Authority
Allow rejection, correction and stopping.
Learning
Use decisions to improve the workflow.
Avoid approval fatigue
If every minor action receives the same interruption, users may learn to approve without reading. Reserve the most demanding review for decisions where it is useful, while keeping lower-risk work observable. Track how often reviewers change or reject outputs and why. A near-perfect approval rate can mean excellent performance, but it can also mean the checkpoint is not receiving meaningful attention.
Close the learning loop
Use review outcomes to improve the product’s task boundaries, evidence presentation and evaluation cases. Make it possible to pause or reverse a workflow where appropriate. The human should have a practical way to disagree with the system, not just responsibility for its mistakes. Review is working when it catches consequential problems and produces better future decisions without making the entire product unusably slow.
Sources
- NIST AI Risk Management Framework — source and reference material ↗ NIST AI Risk Management Framework · Primary source
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