Analytical Explainability
Assumptions and Caveats: Making Answers Trustworthy
Explicit assumptions and caveats keep AI answers honest and reliable.
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Metric yields answer plus assumptions and caveats.
TL;DR
- • Every metric has assumptions; document them.
- • Caveats reduce over‑confidence.
The problem (layman)
- • 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 (steps)
- • 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.