Analytical Explainability
A Practical Explainability Checklist for Power BI
A checklist to ensure AI explanations are reliable and auditable.
TL;DR
- • Explainability requires data, drivers, and context.
- • Use a checklist to enforce consistency.
The problem (layman)
- • Teams forget key explainability elements.
- • AI explanations vary by report.
Why it matters
- • A checklist reduces errors and omissions.
- • It standardizes AI outputs.
Symptoms
- • Missing drivers or caveats.
- • Inconsistent structure across KPIs.
Root causes
- • No standard explainability process.
- • Missing governance for narratives.
What good looks like
- • Each KPI includes drivers, context, and caveats.
- • Explanations are consistent across reports.
How to fix (steps)
- • Adopt the checklist in model reviews.
- • Add metadata fields required by the checklist.
- • Use automated tests where possible.
Pitfalls
- • Treating the checklist as optional.
- • Ignoring feedback loops.
Checklist
- • Lineage documented.
- • Drivers defined.
- • Caveats included.
- • Segment context provided.