Validate local outcomes before restart: What restart evidence | AIGP
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Validate local outcomes before restart: What restart evidence is decisive?

AIGP Understanding How to Govern AI Deployment and Use Medium

Restart requires current local outcome validation because prior errors and changed inputs make vendor retraining evidence insufficient.

The question

A public-benefit model was suspended after unexplained local errors. The vendor reports retraining, but the input mix has changed and no local outcome validation exists. What restart evidence is decisive?

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  1. Ask caseworkers whether predictions seem more understandable.
    Perceived understandability is relevant but cannot substitute for outcome validation addressing the known local-error and distribution concerns.
  2. Compare the revised model with its original benchmark.
    An original benchmark may show general change, but it cannot establish acceptable local outcomes after the population shifted.
  3. Validate local outcomes before restart.
    Changed inputs and prior local errors require outcome evidence in the deployment context before resuming consequential public-benefit use.
  4. Confirm vendor retraining is complete.
    Completion of retraining does not show that local errors improved under the changed input distribution or that safeguards are operational.
The trap
Supplier claims and benchmark comparisons do not replace validation against current local conditions.

How to remember it

Restart requires current local outcome validation because prior errors and changed inputs make vendor retraining evidence insufficient.

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