Analytical Explainability
Explainability Metrics: Consistency, Coverage, and Confidence
Measure explainability to track progress and reliability over time.
TL;DR
- • Explainability can be measured.
- • Track consistency, coverage, and confidence.
The problem (layman)
- • 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 (steps)
- • 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.