Regulatory Submission Package for AI-Driven Resource Estimates

This submission package serves as the formal interface between AI-enabled resource estimation workflows and regulatory authorities. Unlike traditional deterministic or geostatistical estimation methods, AI-driven approaches—such as convolutional neural networks for lithology prediction, graph neural networks for grade interpolation, or ensemble ML models for domain-adapted grade modeling—introduce novel sources of uncertainty related to algorithmic bias, training-data representativeness, and black-box inference. Therefore, the package must go beyond standard QA/QC to include model lineage metadata (e.g., training dataset provenance, version-controlled code repositories), sensitivity analyses, cross-validation metrics against independent drill-hole validation sets, and expert geological review documentation confirming that AI outputs align with structural, stratigraphic, and alteration constraints. Regulatory bodies increasingly require demonstrable human-in-the-loop governance: e.g., geologists must annotate decision points where AI outputs were overridden or constrained, and all assumptions underlying feature engineering (e.g., spatial lag selection, variogram-free neighborhood definitions) must be explicitly justified. Furthermore, the package must articulate how AI-generated uncertainty envelopes—often derived from Monte Carlo dropout, quantile regression forests, or Bayesian neural networks—are mapped to compliant classification categories (Inferred, Indicated, Measured) per reporting code thresholds for confidence and risk.