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Responsible AI

MLNavigator focuses on evidence, reproducibility, and operational clarity. Verifiable provenance helps organizations govern AI systems, but it does not make the outputs correct by default.

Evidence Over Hype

We avoid claims that cannot be audited. Where determinism is not achievable, variance is documented and bounded.

Human Review

High-consequence decisions require human accountability. The product is designed to support review workflows, not replace them.

Data Boundary Discipline

Offline-first deployment reduces inadvertent data exposure to third parties and supports controlled facilities.

Verification Scope

Receipts prove what ran and what it produced. They do not prove that the output is correct, safe, or complete.

See: Verification Scope.