Explainability Metrics: Consistency, Coverage, and Confidence

TL;DR

• Explainability can be measured.
• Track consistency, coverage, and confidence.

The problem

• Teams improve explanations without knowing if they’re better.
• No metrics exist for explanation quality.

Why it matters

• What isn’t measured is hard to improve.
• Explainability metrics guide roadmap decisions.

Symptoms

• Repeated user complaints about unclear explanations.
• Unpredictable quality across KPIs.

Root causes

• No standard explainability KPIs.
• Explanations generated without validation.

What good looks like

• Consistency: same question yields same explanation.
• Coverage: % of KPIs with driver measures.
• Confidence: explanation includes caveats and evidence.

How to fix

• Define explainability KPIs and targets.
• Run regular evaluation tests.
• Tie improvement efforts to these metrics.

Pitfalls

• Tracking only output length instead of substance.
• Ignoring qualitative feedback.

Checklist

• Explainability KPIs defined.
• Evaluation tests run regularly.
• Metrics reviewed with stakeholders.

Framework placement

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