docs/MODEL_GOVERNANCE.md

Model Governance

DataPilot AI is an exploratory copilot. Its critic gate evaluates predictive performance and validation consistency; it does not replace domain approval.

Automatic gate

• Primary classification metric: balanced accuracy.
• Primary regression metric: R².
• Holdout score compared with a configurable minimum.
• Holdout-to-cross-validation divergence above 0.20 is flagged.
• A failed gate routes once back to modeling by default.
• When the retry budget is exhausted, the result is retained only with an explicit limitation.

Human gate before deployment

• Confirm the target is meaningful and available at prediction time.
• Remove direct and proxy leakage.
• Evaluate out-of-time and segment performance.
• Review fairness and disparate impact.
• Verify privacy, consent, retention and lawful use.
• Establish drift, quality and performance alerts.
• Define rollback and retraining ownership.

Reproducibility

Every completed run exports:

• serialized fitted pipeline
• metrics and evidence JSON
• model card
• standalone HTML analysis report
• reproduction metadata containing seed, split, target, task and selected model