Explainability vs. Interpretability
While both aim to make AI decisions understandable, explainability focuses on the reasoning behind decisions, whereas interpretability relates to the transparency of the model's internal mechanics.
Interpretability: clarity about how internal model components (weights, features) map to outcomes - common in simple models (linear regression). Explainability: post hoc generation of human-friendly justifications (why a decision was made) for any model, even black boxes. Governance requires choosing the right balance: interpretable models where possible, and explainability tools where not.
A bank chooses a logistic-regression model for credit scoring because of its interpretability (coefficients directly show feature impact). For its image-based fraud detector (a neural net), it uses explainability (saliency maps) because the model itself isn’t inherently interpretable.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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