Explainable AI is necessary, but it’s not enough
Executive Take
A model can be fully explainable and still make a wrong, ungovernable decision if its inputs were never verified. Leaders need controls that check data and policy before the model runs, not audits after the fact.
Executive Summary
The piece argues explainable AI (SHAP, LIME, EU AI Act disclosures) only explains model reasoning, not the data or policy fed into it. It uses a fraud-claim example where an audit finds backdated documents and stale valuations that no explainability tool caught. It proposes "Explainable Decision Systems," a framework requiring provenance-checked knowledge and time-matched policy before a model runs.
Why It Matters
AI and technology leaders are being told explainability satisfies regulators, but this piece shows it doesn't catch bad inputs. That gap creates real audit and compliance exposure for any enterprise running AI on claims, loans, or clinical decisions.
Bizquad Perspective
Most governance budgets go into model interpretability tools, but the real risk sits upstream in unverified documents and timestamps that no model audit ever checks.