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Reassess the model using representative local data: Which missing control most directly addresses this local

AIGP Understanding How to Govern AI Deployment and Use Medium

The observed local drift requires representative reassessment and corrective controls tied to the affected population.

The question

A university research office uses an AI tool to prioritize grant administration inquiries. Oversight, permissions, documentation, supplier controls, monitoring infrastructure, and response plans are established. Monthly measurements show that accuracy has declined specifically for the region’s newly enrolled research population. Which missing control most directly addresses this local performance drift?

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  1. Increase general training using the original evaluation dataset.
    General training on old data may not represent the newly enrolled population or address the observed local distribution change.
  2. Ask staff to rely more heavily on established workflows.
    Existing workflows may provide continuity, but they do not measure, investigate, or correct the population-specific performance decline.
  3. Require additional manual approval for every inquiry classification.
    Universal approval can reduce immediate exposure, but it does not directly diagnose or remediate the documented local drift.
  4. Reassess the model using representative local data and adjust controls accordingly. ✓
    Local reassessment connects changed population data to suitable measurements and supports proportionate corrective action for the observed drift.
The trap
A changed input distribution signals investigation; it is not by itself proof of poorer outcomes without suitable outcome measurements.

How to remember it

The observed local drift requires representative reassessment and corrective controls tied to the affected population.

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