KB article
Your Model Can Calculate, But Can It Explain?
Explainability requires more than calculations; it requires drivers and context.
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TL;DR
- Calculations answer “what,” not “why.”
- Explainability requires drivers, lineage, and caveats.
The problem
- Models are optimized for totals and KPIs but not explanations.
- AI can’t justify changes without supporting measures.
Why it matters
- Without explanations, users distrust results.
- AI answers without context can be misleading.
Symptoms
- Users ask “why did this change?” and get vague answers.
- AI responses omit drivers or cite irrelevant factors.
Root causes
- No driver measures or decomposition logic.
- Missing metadata for assumptions.
What good looks like
- KPI measures paired with driver measures.
- Explainability is part of the model design.
How to fix
- Add driver measures for top KPIs.
- Create standard explanation templates.
- Embed caveats in metadata.
Pitfalls
- Assuming AI can infer drivers from raw data.
- Ignoring outliers and null semantics.
Checklist
- Top KPIs have driver measures.
- Explanation templates exist.
- Caveats documented in metadata.
Framework placement
Primary ARF layer: Analytical Explainability. Diagnostic bridge: semantic-reliability, execution-reliability, change-reliability.