Human-in-the-Loop
Involving human judgment within AI processes (training, validation, decision review) to improve accuracy and accountability.
A hybrid approach where humans augment AI workflows - providing labels, verifying low-confidence predictions, or overriding model decisions. HITL ensures that edge-case, high-risk, or novel situations receive expert attention. Effective governance sets thresholds for human intervention, tracks human-AI performance comparisons, and prevents human biases from undermining automation gains.
A medical-diagnosis AI flags scans with model-confidence below 80% for radiologist review (HITL). Over six months, 95% of low-confidence cases were correctly classified by humans, and those labels feed back into model retraining - improving confidence calibration and reducing overall error rates.
“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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What problem does Enzai solve?
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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How is Enzai different from other governance tools?
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