A governed local AI build-and-memory system that trains small brains, compares them, protects the better one, archives the worse one, and preserves the evidence of why. v1.0.0/governed-v2.2.0+

A beginner-friendly AI Governance & Risk Toolkit — risk register, governance templates, and audit-ready workflows for early-stage AI teams.

Forkit Core is an open source passport layer for AI models and agents with GitHub CI validation, local verification, and Hugging Face-compatible export.

A practical framework for turning data analysis into decision policies you can defend. Covers risk modeling, thresholding, exception handling, policy cards, monitoring, and update triggers, using real patterns like abstention rules, reorder points, and fairness-aware benchmarking. Built for “ship it” data science.

Automated validation toolkit for tabular ML models in finance and regulated domains.

Audit-ready explainability artifacts (reason codes, model cards, drift checks) for scikit-learn investment & credit-risk models.

Four Tests Standard (4TS) - Vendor-neutral specification for verifiable AI governance

Data Trust Engineering (DTE) is a vendor-neutral, engineering-first approach to building trusted, Data, Analytics and AI-ready data systems. This repo hosts the Manifesto, Patterns, and the Trust Dashboard MVP.

EU AI Act governance prototype that turns real medical AI evidence and ISO/IEC 42001 governance scaffolding into reviewable classification, evidence checks, human oversight, and incident-handling paths.

Depth-tracked regulatory audit primitives for privacy-preserving AI audits with signed envelopes and TenSEAL CKKS support.

This repository defines a reproducible Layer-0 functional compliance specification for Large Language Models.

Compliance-as-code for AI systems: evaluate AI apps against EU AI Act, NIST AI RMF, and OPA/Rego policies.

Protected v1.0.0 baseline for ARC-Neuron LLMBuilder — local-first LLM lifecycle tooling for benchmark receipts, candidate promotion, lineage, and governed model improvement.

Supporting materials for “Building Governable ML Models with R,” presented at posit::conf 2025

Regime-based evaluation framework for financial NLP stability. Implements chronological cross-validation, semantic drift quantification via Jensen-Shannon divergence, and multi-faceted robustness profiling. Replicates Sun et al.'s (2025) methodology with modular, auditable Python codebase.

Customizable AI Acceptable Use Policy and governance framework for US enterprises. MIT licensed. Covers compliance, HR, infosec, and legal.

Industrial computer vision workflow for welding defect inspection using YOLO, OpenCV preprocessing, dataset QA, threshold governance, and edge-readiness analysis.

Model governance for insurance pricing — PRA SS1/23 validation reports, model risk management, risk tier scoring

Functional foundation model surface standards for SocioProphet: model, adapter, dataset, eval, guardrail, tool, agent, routing, promotion, and SourceOS carry contracts.

Governed model and service routing for SocioProphet: local vs hosted, small vs large, cost, latency, quality, privacy, fallback, and eval-confidence policy.