Analytical Explainability
Your Model Can Calculate, But Can It Explain?
Explainability requires more than calculations; it requires drivers and context.
Rendering diagram...
A KPI leads to a basic answer, but drivers and context lead to explainable answer.
TL;DR
- • Calculations answer “what,” not “why.”
- • Explainability requires drivers, lineage, and caveats.
The problem (layman)
- • 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 (steps)
- • 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.