Interpretability
The degree to which a human can understand the internal mechanics or decision rationale of an AI model.
Refers to the inherent transparency of a model’s structure - e.g., linear models or decision trees where feature impacts map directly to outputs. Interpretability governance encourages interpretable models for high-risk use cases, documents model logic clearly, and restricts opaque models to lower-risk domains or pairs them with post hoc explanation methods.
A credit-scoring team opts for a decision-tree model for initial loan approvals because each split can be directly interpreted (“income > $50K”). They publish the tree logic to stakeholders - ensuring full interpretability and facilitating regulatory reviews.
“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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