Black Box Model
An AI system whose internal workings are not transparent or interpretable, making it difficult to understand how decisions are made.
High-performance but opaque models (e.g., deep neural networks, ensemble methods) that deliver accurate results without clear insight into their logic. Black-box models pose governance challenges: it’s hard to trace errors, justify decisions to stakeholders, or ensure compliance. Organizations often pair them with external explainers or restrict them to low-risk use cases.
A hospital’s diagnostic AI uses a deep ensemble network that accurately identifies tumors on scans but cannot explain its reasoning. To govern its use, radiologists only deploy it as a second opinion - always reviewing the output alongside human interpretation - and regulators require the vendor to supply an external explainer tool for audit trails.
“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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Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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