A hybrid database system using PostgreSQL and Neo4j to analyze, track, and visualize AI ethics policies and compliance risks.
- Overview
- Features
- System Architecture
- Tech Stack
- Project Roadmap
- Getting Started
- API Documentation
- Dashboard
- Development Timeline
- Contributing
- License
The AI Ethics Compliance Tracker is a comprehensive system designed to help organizations monitor, analyze, and ensure compliance with AI ethics policies. By leveraging both relational (PostgreSQL) and graph (Neo4j) databases, the system provides powerful insights into policy relationships, risk assessment, and compliance tracking.
Note: This project is currently in the planning and early development stage. The repository structure and documentation will evolve as the project progresses.
Planning Stage - Features to be implemented:
- Policy Scraping & Ingestion: Automated collection of AI ethics policies from major tech companies
- Dual Database Architecture:
- PostgreSQL for structured data and efficient queries
- Neo4j for relationship mapping and graph-based analysis
- NLP-Powered Risk Assessment: Fine-tuned BERT model for analyzing policy text and identifying risks
- Interactive Dashboard: Streamlit-based visualization of compliance metrics and risk heatmaps
- RESTful API: FastAPI endpoints for integration with existing systems
- Graph Visualization: Interactive network graphs showing relationships between companies, policies, and risks
- Automated Reporting: Scheduled compliance reports and risk alerts
- OAuth2 Security: Secure API access with proper authentication
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Data Sources │───▶│ ETL Pipeline │───▶│ PostgreSQL DB │
│ (AI Policies) │ │ (Python) │ │ (Relational) │
└─────────────────┘ └─────────────────┘ └────────┬────────┘
│
▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Dashboard │◀───│ FastAPI │◀───│ Neo4j DB │
│ (Streamlit) │ │ Backend │ │ (Graph) │
└─────────────────┘ └─────────────────┘ └─────────────────┘
Backend
- Python 3.9+
- PostgreSQL: Primary relational database
- Neo4j: Graph database for relationship modeling
- FastAPI: API framework
- Hugging Face Transformers: NLP model for policy analysis
- SQLAlchemy & psycopg2: PostgreSQL interactions
- Neo4j Python Driver: Graph database connection
Frontend/Visualization
- Streamlit: Interactive dashboard
- Plotly & NetworkX: Data visualization
- D3.js: Advanced graph visualization
DevOps
- Docker & Docker Compose: Containerization
- GitHub Actions: CI/CD pipeline
- GCP/AWS: Cloud deployment
- Redis: API response caching
- Prometheus & Grafana: Monitoring
- Project planning and repository setup
- PostgreSQL schema design and setup
- Web scraping module for AI ethics policies
- Initial data ingestion pipeline
- Neo4j graph model design
- Docker containerization
- Cloud deployment setup
- CI/CD pipeline with GitHub Actions
- Database synchronization between PostgreSQL and Neo4j
- Text preprocessing pipeline
- BERT model fine-tuning for ethics policy analysis
- Risk scoring algorithm
- Batch processing system for policy analysis
- FastAPI endpoints implementation
- Authentication and security
- Streamlit dashboard development
- Interactive graph visualization
- Load testing and performance optimization
- Comprehensive documentation
- Demo video and presentation materials
- Python 3.9+
- Docker and Docker Compose
- Git
# Clone the repository
git clone https://github.com/yourusername/ai-ethics-compliance-tracker.git
cd ai-ethics-compliance-tracker
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt# Start the databases with Docker Compose
docker-compose up -d
# Run the ETL pipeline
python etl/scraper.py
# Start the API
uvicorn app.api.main:app --reload
# Start the dashboard
streamlit run app/dashboard/main.pyOnce the project reaches the API development phase, the API documentation will be available at:
http://localhost:8000/docs
| Endpoint | Method | Description |
|---|---|---|
/companies |
GET | List all companies with ethics policies |
/companies/{id}/policies |
GET | Get policies for a specific company |
/risks |
GET | List all identified compliance risks |
/predict |
POST | Analyze a new policy text for risks |
/graph/company-risks |
GET | Get graph data for company-risk relationships |
The Streamlit dashboard (under development) will provide:
- Company compliance scorecards
- Risk heatmaps across industries
- Policy comparison tools
- Interactive graph visualization of risk relationships
- Trend analysis of AI ethics focus areas
This project follows a structured 7-day development plan:
- Day 1: PostgreSQL Setup & Data Ingestion
- Day 2: Neo4j Graph Database Setup
- Day 3: DevOps & Cloud Deployment
- Day 4: NLP & Risk Scoring
- Day 5: API & Dashboard Development
- Day 6: Testing & Optimization
- Day 7: Documentation & Final Polish
Current Status: Planning Phase
As this project is in the early planning stages, contributions are not yet being accepted. However, once the project progresses, we welcome contributions through pull requests.
When the contribution process opens:
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Note: This project is currently in planning phase. Repository structure and functionality will be updated as development progresses.