To detect performance decline and trigger maintenance | AIGP
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To detect performance decline and trigger maintenance: What is the primary purpose of this continuous

AIGP Understanding How to Govern AI Deployment and Use Easy

Continuous monitoring exists to detect performance decline and behavioral drift over time so maintenance, updates or retraining can be triggered.

The question

A governance policy requires ongoing observation of a deployed model's accuracy and behavior on a regular schedule after launch. What is the primary purpose of this continuous monitoring?

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  1. To detect performance decline and trigger maintenance or retraining ✓
    Continuous monitoring watches for accuracy and behavior degradation over time so maintenance, updates or retraining can be triggered.
  2. To negotiate the licensing terms before the model is deployed
    A real pre-deployment task, but contract negotiation happens before launch and is not ongoing monitoring.
  3. To assess the potential harms of the system prior to go-live
    A valid activity, yet pre-deployment harm evaluation is an impact assessment, not continuous post-launch monitoring.
  4. To draft the external communication plan for future customer incidents
    A useful artifact, but preparing outbound messaging is distinct from observing live model performance.
The trap
Conflating ongoing post-launch monitoring with one-time pre-deployment activities.

How to remember it

Continuous monitoring exists to detect performance decline and behavioral drift over time so maintenance, updates or retraining can be triggered.

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