Grey Box Model
A model whose internal logic is partially transparent (some components interpretable, others opaque), balancing performance and explainability.
Hybrid architectures that blend interpretable elements (e.g., decision trees) with opaque components (e.g., neural embeddings). Grey-box models aim for a middle ground - maintaining high accuracy while offering partial visibility. Governance processes include identifying interpretable segments for audit, documenting opaque parts with external explainers, and restricting opaque behaviors in high-stakes contexts.
A credit-assessment system uses a grey-box: a rule-based engine handles eligibility checks (fully transparent), and a neural network predicts risk scores. Loan officers review the rule-based outcome directly, and view SHAP explanations for the neural score - ensuring that at least part of the decision logic is 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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