Governed NLP system for regulatory document classification with audit-ready, policy-enforced AI.
This project demonstrates how to combine ML, governance, and auditability in a practical regulatory AI system.
# Regulatory AI Classification System
**Description:**
This project implements a **governed NLP system** for classifying regulatory documents. It combines interpretable machine learning with **policy-enforced, audit-ready AI**, ensuring safe and compliant use in regulated environments.
## Key Features
- **Synthetic & versioned datasets** for reproducible experiments
- **Interpretable NLP classification model** (TF-IDF + Logistic Regression)
- **Explainability & risk analysis** for each prediction
- **Governance & model card**: links dataset versions to model versions, defines approved use cases, and enforces risk controls
- **Policy-enforced inference**: automatically blocks prohibited use, triggers human review for low-confidence predictions, and logs all actions
- **Audit-ready logging**: every decision is recorded for regulatory traceability
- **Executive demo UI**: simple, non-technical interface to demonstrate safe AI usage
## Project Structure
Reg-pj/
├── data/ # Synthetic dataset
├── models/ # Trained ML model
├── governance/ # Model card, approvals, policies
├── logs/ # Inference audit logs
├── src/ # Python source code (data prep, training, inference)
└── demo/ # Streamlit demo UI for executive presentation
## How to Run
1. **Create and activate a virtual environment**
From Command Prompt:
python -m venv venv
venv\Scripts\activate # Windows
streamlit run demo/app.py