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Retrain when evidence shows changed risk or performance: What distinguishes a trigger-based approach from this

AIGP Understanding How to Govern AI Development Easy

Trigger-based retraining responds to evidence of changed performance, data, context, or risk; an automatic schedule responds only to time.

The question

An internal employee-assistance model is retrained every quarter, although monitoring shows stable performance and no material data or context change. What distinguishes a trigger-based approach from this schedule?

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  1. Retrain only after users report a confirmed harmful outcome.
    Waiting for confirmed harm is too narrow because monitoring can reveal declining performance or emerging risks earlier.
  2. Retrain after every monitored change, regardless of measured impact.
    A detected change does not automatically justify retraining; its effect and significance should be evaluated first.
  3. Retrain whenever the quarterly calendar reaches its scheduled date.
    A calendar schedule can support maintenance, but it does not establish that new evidence requires retraining.
  4. Retrain when evidence shows changed risk or performance. ✓
    This uses monitoring evidence to trigger retraining when conditions change, rather than treating elapsed time as sufficient justification.
The trap
Look for whether the decision depends on observed evidence or merely on elapsed time.

How to remember it

Trigger-based retraining responds to evidence of changed performance, data, context, or risk; an automatic schedule responds only to time.

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