Pause approval: What does the evidence support? | AIGP
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Pause approval: What does the evidence support?

AIGP Understanding How to Govern AI Deployment and Use Medium

The vendor benchmark is scoped evidence; materially lower local results require investigation before approval.

The question

For an internal pre-deployment decision, an energy company’s vendor reports 96% fault-identification accuracy, but local turbine validation shows 81%. The intended use produces maintenance recommendations that technicians review before work orders. What does the evidence support?

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  1. Treat technician review as the primary corrective control.
    Technician review may reduce consequences, but it does not explain the performance gap or establish that the system is suitable locally.
  2. Pause approval, investigate local results, and document limits and safeguards.
    The conflicting results require investigation of actual-use suitability, external dependencies, limitations, and whether technician review can manage residual risk.
  3. Use the vendor benchmark as the deployment performance estimate.
    The benchmark may use different equipment, data, or conditions and therefore does not establish performance for this local maintenance context.
  4. Approve conditionally on the vendor result while monitoring local outcomes after launch.
    Monitoring may be useful later, but approving consequential use while disregarding materially lower local validation leaves the central suitability question unresolved.
The trap
Match evidence to the actual equipment, users, conditions, and consequences of deployment.

How to remember it

The vendor benchmark is scoped evidence; materially lower local results require investigation before approval.

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