A Practical Explainability Checklist for Power BI

TL;DR

• Explainability requires data, drivers, and context.
• Use a checklist to enforce consistency.

The problem

• 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

• 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.

Framework placement

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