Assumptions and Caveats: Making Answers Trustworthy

TL;DR

• Every metric has assumptions; document them.
• Caveats reduce over‑confidence.

The problem

• AI answers are presented without caveats.
• Users assume results are absolute truths.

Why it matters

• Caveats prevent misuse and misinterpretation.
• Transparency increases trust.

Symptoms

• AI omits missing data or exclusions.
• Stakeholders assume precision that isn’t there.

Root causes

• No place to store assumptions in the model.
• No standard for caveats in explanations.

What good looks like

• Assumptions stored in metadata.
• AI responses include caveat sections.

How to fix

• Add “assumptions” and “caveats” fields to key measures.
• Include caveats in narrative templates.
• Review caveats during metric changes.

Pitfalls

• Overloading caveats until they’re ignored.
• Leaving caveats out of AI responses.

Checklist

• Assumptions documented for key KPIs.
• Caveats surfaced in AI outputs.
• Review process in place.

Framework placement

Primary ARF layer: Analytical Explainability. Diagnostic bridge: semantic-reliability, execution-reliability, change-reliability.