Generated fluency is not verified evidence: What distinction | AIGP
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Generated fluency is not verified evidence: What distinction must training establish?

AIGP Understanding How to Govern AI Deployment and Use Hard

Training must distinguish fluent generated content from verified policy evidence before consequential claim decisions.

The question

An insurer trains operators to review AI-generated claim summaries. The system retrieves policy text, sometimes invents exclusions, and may trigger payment recommendations. Operators see the interface but receive no instruction on source verification, abstention, or escalation. What distinction must training establish?

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  1. Confidence scores can supplement source checking.
    Scores may be one input, but they do not establish that retrieved policy text was faithfully represented.
  2. Historical claim averages can identify unreliable summaries.
    Historical comparisons may reveal anomalies but cannot verify policy citations or detect every invented exclusion.
  3. Generated fluency is not verified evidence. ✓
    Operators must understand that a plausible summary can contain invented exclusions and requires evidence verification before consequential action.
  4. Interface demonstrations can teach approval and escalation workflows.
    Workflow demonstrations are useful, but they do not establish the central limitation that fluent generated text may be false.
The trap
Look for training that teaches the system limitation, not merely interface use or confidence-score interpretation.

How to remember it

Training must distinguish fluent generated content from verified policy evidence before consequential claim decisions.

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