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# Nordic Privacy AI 🛡️# Nordic Privacy AI 🛡️# AI Governance Module
**AI-Powered GDPR Compliance & Privacy Protection Platform**
A comprehensive solution for AI governance, bias detection, risk assessment, and automated PII cleaning with GDPR compliance. Built for Nordic ecosystems and beyond.**AI-Powered GDPR Compliance & Privacy Protection Platform**A Python package for detecting bias and analyzing risks in machine learning models. Provides comprehensive fairness metrics, privacy risk assessment, and ethical AI evaluation.
[![Python](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://www.python.org/)
[![FastAPI](https://img.shields.io/badge/FastAPI-0.109+-green.svg)](https://fastapi.tiangolo.com/)
[![Next.js](https://img.shields.io/badge/Next.js-14.2+-black.svg)](https://nextjs.org/)A comprehensive solution for AI governance, bias detection, risk assessment, and automated PII cleaning with GDPR compliance. Built for Nordic ecosystems and beyond.## Features
[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
---
[![Python](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://www.python.org/)### 🎯 Bias Detection
## 🚀 Quick Start
[![FastAPI](https://img.shields.io/badge/FastAPI-0.109+-green.svg)](https://fastapi.tiangolo.com/)- **Fairness Metrics**: Disparate Impact, Statistical Parity Difference, Equal Opportunity Difference
### Prerequisites
- Python 3.8+[![Next.js](https://img.shields.io/badge/Next.js-14.2+-black.svg)](https://nextjs.org/)- **Demographic Analysis**: Group-wise performance evaluation
- Node.js 18+
- GPU (optional, for faster processing)[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)- **Violation Detection**: Automatic flagging with severity levels
### Installation
1. **Clone the repository**---### 🛡️ Risk Assessment
```powershell
git clone https://github.com/PlatypusPus/MushroomEmpire.git- **Privacy Risks**: PII detection, GDPR compliance, data exposure analysis
cd MushroomEmpire
```## 🚀 Quick Start- **Ethical Risks**: Fairness, transparency, accountability, social impact
2. **Install Python dependencies**- **Compliance Risks**: Regulatory adherence (GDPR, CCPA, AI Act)
```powershell
pip install -r requirements.txt### Prerequisites- **Data Quality**: Missing data, class imbalance, outlier detection
python -m spacy download en_core_web_sm
```- Python 3.8+
3. **Install frontend dependencies**- Node.js 18+### 🤖 Machine Learning
```powershell
cd frontend- GPU (optional, for faster processing)- Generalized classification model (works with any dataset)
npm install
cd ..- Auto-detection of feature types and protected attributes
```
### Installation- Comprehensive performance metrics
### Running the Application
- Feature importance analysis
1. **Start the FastAPI backend** (Terminal 1)
```powershell1. **Clone the repository**
python start_api.py
``````powershell## Installation
Backend runs at: **http://localhost:8000**
git clone https://github.com/PlatypusPus/MushroomEmpire.git
2. **Start the Next.js frontend** (Terminal 2)
```powershellcd MushroomEmpire```bash
cd frontend
npm run dev```pip install -r requirements.txt
```
Frontend runs at: **http://localhost:3000**```
3. **Access the application**2. **Install Python dependencies**
- Frontend UI: http://localhost:3000
- Try It Page: http://localhost:3000/try```powershellOr install as a package:
- API Documentation: http://localhost:8000/docs
- Health Check: http://localhost:8000/healthpip install -r requirements.txt
---python -m spacy download en_core_web_sm```bash
## 📋 Features```pip install -e .
### 🎯 AI Governance & Bias Detection```
- **Fairness Metrics**: Disparate Impact, Statistical Parity, Equal Opportunity
- **Demographic Analysis**: Group-wise performance evaluation3. **Install frontend dependencies**
- **Violation Detection**: Automatic flagging with severity levels (HIGH/MEDIUM/LOW)
- **Model Performance**: Comprehensive ML metrics (accuracy, precision, recall, F1)```powershell## Quick Start
### 🛡️ Privacy Risk Assessmentcd frontend/nordic-privacy-ai
- **Privacy Risks**: PII detection, GDPR compliance scoring, data exposure analysis
- **Ethical Risks**: Fairness, transparency, accountability evaluationnpm install```python
- **Compliance Risks**: Regulatory adherence (GDPR, CCPA, AI Act)
- **Data Quality**: Missing data, class imbalance, outlier detectioncd ../..from ai_governance import AIGovernanceAnalyzer
### 🧹 Automated Data Cleaning```
- **PII Detection**: Email, phone, SSN, credit cards, IP addresses, and more
- **GPU Acceleration**: CUDA-enabled for 10x faster processing# Initialize analyzer
- **GDPR Compliance**: Automatic anonymization with audit trails
- **Smart Anonymization**: Context-aware masking and pseudonymization### Running the Applicationanalyzer = AIGovernanceAnalyzer()
### 🌐 Modern Web Interface
- **Drag & Drop Upload**: Intuitive CSV file handling
- **Real-time Processing**: Live feedback and progress tracking1. **Start the FastAPI backend** (Terminal 1)# Run complete analysis
- **Interactive Dashboards**: Visualize bias metrics, risk scores, and results
- **Report Downloads**: JSON reports, cleaned CSV, and audit logs```powershellreport = analyzer.analyze(
---python start_api.py data_path='your_data.csv',
## 🏗️ Project Structure``` target_column='target',
```Backend runs at: **http://localhost:8000** protected_attributes=['gender', 'age', 'race']
MushroomEmpire/
├── api/ # FastAPI Backend)
│ ├── main.py # Application entry point
│ ├── routers/2. **Start the Next.js frontend** (Terminal 2)
│ │ ├── analyze.py # POST /api/analyze - AI Governance
│ │ └── clean.py # POST /api/clean - Data Cleaning```powershell# Access results
│ └── utils/ # Helper utilities
│cd frontend/nordic-privacy-aiprint(f"Bias Score: {report['summary']['overall_bias_score']:.3f}")
├── ai_governance/ # Core AI Governance Module
│ ├── __init__.py # AIGovernanceAnalyzer classnpm run devprint(f"Risk Level: {report['summary']['risk_level']}")
│ ├── data_processor.py # Data preprocessing
│ ├── model_trainer.py # ML model training```print(f"Model Accuracy: {report['summary']['model_accuracy']:.3f}")
│ ├── bias_analyzer.py # Bias detection engine
│ ├── risk_analyzer.py # Risk assessment engineFrontend runs at: **http://localhost:3000**
│ └── report_generator.py # JSON report generation
│# Save report
├── data_cleaning/ # Data Cleaning Module
│ ├── __init__.py # DataCleaner class3. **Access the application**analyzer.save_report(report, 'governance_report.json')
│ ├── cleaner.py # PII detection & anonymization
│ └── config.py # PII patterns & GDPR rules - Frontend UI: http://localhost:3000```
│
├── frontend/ # Next.js Frontend - API Documentation: http://localhost:8000/docs
│ ├── app/ # App Router pages
│ │ ├── page.tsx # Landing page - Health Check: http://localhost:8000/health## Module Structure
│ │ └── try/page.tsx # Try it page (workflow UI)
│ ├── components/
│ │ └── try/
│ │ ├── CenterPanel.tsx # File upload & results---```
│ │ ├── Sidebar.tsx # Workflow tabs
│ │ └── ChatbotPanel.tsx # AI assistantai_governance/
│ └── lib/
│ ├── api.ts # TypeScript API client## 📋 Features├── __init__.py # Main API
│ └── indexeddb.ts # Browser caching utilities
│├── data_processor.py # Data preprocessing
├── Datasets/ # Sample datasets
│ └── loan_data.csv # Example: Loan approval dataset### 🎯 AI Governance & Bias Detection├── model_trainer.py # ML model training
│
├── reports/ # Generated reports (auto-created)- **Fairness Metrics**: Disparate Impact, Statistical Parity, Equal Opportunity├── bias_analyzer.py # Bias detection
│ ├── governance_report_*.json
│ ├── cleaned_*.csv- **Demographic Analysis**: Group-wise performance evaluation├── risk_analyzer.py # Risk assessment
│ └── cleaning_audit_*.json
│- **Violation Detection**: Automatic flagging with severity levels (HIGH/MEDIUM/LOW)└── report_generator.py # Report generation
├── start_api.py # Backend startup script
├── setup.py # Package configuration- **Model Performance**: Comprehensive ML metrics (accuracy, precision, recall, F1)```
├── requirements.txt # Python dependencies
└── README.md # This file
```
### 🛡️ Privacy Risk Assessment## API Reference
---
- **Privacy Risks**: PII detection, GDPR compliance scoring, data exposure analysis
## 📡 API Reference
- **Ethical Risks**: Fairness, transparency, accountability evaluation### AIGovernanceAnalyzer
### Base URL
```- **Compliance Risks**: Regulatory adherence (GDPR, CCPA, AI Act)
http://localhost:8000
```- **Data Quality**: Missing data, class imbalance, outlier detectionMain class for running AI governance analysis.
### Endpoints
#### **POST /api/analyze**### 🧹 Automated Data Cleaning```python
Analyze dataset for bias, fairness, and risk assessment.
- **PII Detection**: Email, phone, SSN, credit cards, IP addresses, and moreanalyzer = AIGovernanceAnalyzer()
**Request:**
```bash- **GPU Acceleration**: CUDA-enabled for 10x faster processing
curl -X POST "http://localhost:8000/api/analyze" \
-F "file=@Datasets/loan_data.csv"- **GDPR Compliance**: Automatic anonymization with audit trails# Analyze from DataFrame
```
- **Smart Anonymization**: Context-aware masking and pseudonymizationreport = analyzer.analyze_dataframe(
**Response:**
```json df=dataframe,
{
"status": "success",### 🌐 Modern Web Interface target_column='target',
"filename": "loan_data.csv",
"dataset_info": {- **Drag & Drop Upload**: Intuitive CSV file handling protected_attributes=['gender', 'age']
"rows": 1000,
"columns": 15- **Real-time Processing**: Live feedback and progress tracking)
},
"model_performance": {- **Interactive Dashboards**: Visualize bias metrics, risk scores, and results
"accuracy": 0.85,
"precision": 0.82,- **Report Downloads**: JSON reports, cleaned CSV, and audit logs# Analyze from file
"recall": 0.88,
"f1_score": 0.85report = analyzer.analyze(
},
"bias_metrics": {--- data_path='data.csv',
"overall_bias_score": 0.23,
"violations_detected": [] target_column='target',
},
"risk_assessment": {## 🏗️ Project Structure protected_attributes=['gender', 'age']
"overall_risk_score": 0.35,
"privacy_risks": [],)
"ethical_risks": []
},``````
"recommendations": [
"[HIGH] Privacy: Remove PII columns before deployment",MushroomEmpire/
"[MEDIUM] Fairness: Monitor demographic parity over time"
],├── api/ # FastAPI Backend### Individual Components
"report_file": "/reports/governance_report_20251107_123456.json"
}│ ├── main.py # Application entry point
```
│ ├── routers/```python
#### **POST /api/clean**
Detect and anonymize PII in datasets.│ │ ├── analyze.py # POST /api/analyze - AI Governancefrom ai_governance import (
**Request:**│ │ └── clean.py # POST /api/clean - Data Cleaning DataProcessor,
```bash
curl -X POST "http://localhost:8000/api/clean" \│ └── utils/ # Helper utilities GeneralizedModelTrainer,
-F "file=@Datasets/loan_data.csv"
```│ BiasAnalyzer,
**Response:**├── ai_governance/ # Core AI Governance Module RiskAnalyzer,
```json
{│ ├── __init__.py # AIGovernanceAnalyzer class ReportGenerator
"status": "success",
"dataset_info": {│ ├── data_processor.py # Data preprocessing)
"original_rows": 1000,
"original_columns": 15,│ ├── model_trainer.py # ML model training
"cleaned_rows": 1000,
"cleaned_columns": 13│ ├── bias_analyzer.py # Bias detection engine# Process data
},
"summary": {│ ├── risk_analyzer.py # Risk assessment engineprocessor = DataProcessor(df)
"columns_removed": ["ssn", "email"],
"columns_anonymized": ["phone", "address"],│ └── report_generator.py # JSON report generationprocessor.target_column = 'target'
"total_cells_affected": 2847
},│processor.protected_attributes = ['gender', 'age']
"pii_detections": {
"EMAIL": 1000,├── cleaning.py # Core PII detection & anonymizationprocessor.prepare_data()
"PHONE": 987,
"SSN": 1000├── cleaning_config.py # Configuration for data cleaning
},
"gdpr_compliance": [├── test_cleaning.py # Unit tests for cleaning module# Train model
"Article 5(1)(c) - Data minimization",
"Article 17 - Right to erasure",│trainer = GeneralizedModelTrainer(
"Article 25 - Data protection by design"
],├── frontend/nordic-privacy-ai/ # Next.js Frontend processor.X_train,
"files": {
"cleaned_csv": "/reports/cleaned_20251107_123456.csv",│ ├── app/ # App Router pages processor.X_test,
"audit_report": "/reports/cleaning_audit_20251107_123456.json"
}│ │ ├── page.tsx # Landing page processor.y_train,
}
```│ │ └── try/page.tsx # Try it page (workflow UI) processor.y_test,
#### **GET /health**│ ├── components/ processor.feature_names
Health check endpoint with GPU status.
│ │ └── try/)
**Response:**
```json│ │ ├── CenterPanel.tsx # File upload & resultstrainer.train()
{
"status": "healthy",│ │ ├── Sidebar.tsx # Workflow tabstrainer.evaluate()
"version": "1.0.0",
"gpu_available": true│ │ └── ChatbotPanel.tsx # AI assistant
}
```│ └── lib/# Analyze bias
#### **GET /reports/{filename}**│ ├── api.ts # TypeScript API clientbias_analyzer = BiasAnalyzer(
Download generated reports and cleaned files.
│ └── indexeddb.ts # Browser caching utilities processor.X_test,
---
│ processor.y_test,
## 🔧 Configuration
├── Datasets/ # Sample datasets trainer.y_pred,
### Environment Variables
│ └── loan_data.csv # Example: Loan approval dataset processor.df,
Create `.env` file in `frontend/`:
```env│ processor.protected_attributes,
NEXT_PUBLIC_API_URL=http://localhost:8000
```├── reports/ # Generated reports (auto-created) processor.target_column
### CORS Configuration│ ├── governance_report_*.json)
Edit `api/main.py` to add production domains:│ ├── cleaned_*.csvbias_results = bias_analyzer.analyze()
```python
origins = [│ └── cleaning_audit_*.json
"http://localhost:3000",
"https://your-production-domain.com"│# Assess risks
]
```├── start_api.py # Backend startup scriptrisk_analyzer = RiskAnalyzer(
### GPU Acceleration├── setup.py # Package configuration processor.df,
GPU is automatically detected and used if available. To force CPU mode:├── requirements.txt # Python dependencies trainer.results,
```python
# In data_cleaning/cleaner.py or api endpoints└── README.md # This file bias_results,
DataCleaner(use_gpu=False)
`````` processor.protected_attributes,
--- processor.target_column
## 🧪 Testing---)
### Test the Backendrisk_results = risk_analyzer.analyze()
```powershell
# Test analyze endpoint## 📡 API Reference
curl -X POST "http://localhost:8000/api/analyze" -F "file=@Datasets/loan_data.csv"
# Generate report
# Test clean endpoint
curl -X POST "http://localhost:8000/api/clean" -F "file=@Datasets/loan_data.csv"### Base URLreport_gen = ReportGenerator(
# Check health``` trainer.results,
curl http://localhost:8000/health
```http://localhost:8000 bias_results,
### Run Unit Tests``` risk_results,
```powershell
# Test cleaning module processor.df
python test_cleaning.py
### Endpoints)
# Run all tests (if pytest configured)
pytestreport = report_gen.generate_report()
```
#### **POST /api/analyze**```
---
Analyze dataset for bias, fairness, and risk assessment.
## 📊 Usage Examples
## Report Structure
### Python SDK Usage
**Request:**
```python
from ai_governance import AIGovernanceAnalyzer```bashThe module generates comprehensive JSON reports:
# Initialize analyzercurl -X POST "http://localhost:8000/api/analyze" \
analyzer = AIGovernanceAnalyzer()
-F "file=@Datasets/loan_data.csv"```json
# Analyze dataset
report = analyzer.analyze(```{
data_path='Datasets/loan_data.csv',
target_column='loan_approved', "metadata": {
protected_attributes=['gender', 'age', 'race']
)**Response:** "report_id": "unique_id",
# Print results```json "generated_at": "timestamp",
print(f"Bias Score: {report['summary']['overall_bias_score']:.3f}")
print(f"Risk Level: {report['summary']['risk_level']}"){ "dataset_info": {}
print(f"Model Accuracy: {report['summary']['model_accuracy']:.3f}")
"status": "success", },
# Save report
analyzer.save_report(report, 'my_report.json') "filename": "loan_data.csv", "summary": {
```
"dataset_info": { "overall_bias_score": 0.0-1.0,
### Data Cleaning Usage
"rows": 1000, "overall_risk_score": 0.0-1.0,
```python
from data_cleaning import DataCleaner "columns": 15 "risk_level": "LOW|MEDIUM|HIGH",
# Initialize cleaner with GPU }, "model_accuracy": 0.0-1.0,
cleaner = DataCleaner(use_gpu=True)
"model_performance": { "fairness_violations_count": 0
# Load and clean data
df = cleaner.load_data('Datasets/loan_data.csv') "accuracy": 0.85, },
cleaned_df, audit = cleaner.anonymize_pii(df)
"precision": 0.82, "model_performance": {},
# Save results
cleaner.save_cleaned_data(cleaned_df, 'cleaned_output.csv') "recall": 0.88, "bias_analysis": {},
cleaner.save_audit_report(audit, 'audit_report.json')
``` "f1_score": 0.85 "risk_assessment": {},
### Frontend Integration }, "key_findings": [],
```typescript "bias_metrics": { "recommendations": []
import { analyzeDataset, cleanDataset } from '@/lib/api';
"overall_bias_score": 0.23,}
// Analyze uploaded file
const handleAnalyze = async (file: File) => { "violations_detected": []```
const result = await analyzeDataset(file);
console.log('Bias Score:', result.bias_metrics.overall_bias_score); },
console.log('Download:', result.report_file);
}; "risk_assessment": {## Metrics Interpretation
// Clean uploaded file "overall_risk_score": 0.35,
const handleClean = async (file: File) => {
const result = await cleanDataset(file); "privacy_risks": [],### Bias Score (0-1, lower is better)
console.log('Cells anonymized:', result.summary.total_cells_affected);
console.log('Download cleaned:', result.files.cleaned_csv); "ethical_risks": []- **0.0 - 0.3**: Low bias ✅
};
``` },- **0.3 - 0.5**: Moderate bias ⚠️
--- "recommendations": [- **0.5 - 1.0**: High bias ❌
## 📈 Metrics Interpretation "[HIGH] Privacy: Remove PII columns before deployment",
### Bias Score (0-1, lower is better) "[MEDIUM] Fairness: Monitor demographic parity over time"### Risk Score (0-1, lower is better)
- **0.0 - 0.3**: ✅ Low bias - Good fairness
- **0.3 - 0.5**: ⚠️ Moderate bias - Monitoring recommended ],- **0.0 - 0.4**: LOW risk ✅
- **0.5 - 1.0**: ❌ High bias - Immediate action required
"report_file": "/reports/governance_report_20251107_123456.json"- **0.4 - 0.7**: MEDIUM risk ⚠️
### Risk Score (0-1, lower is better)
- **0.0 - 0.4**: ✅ LOW risk}- **0.7 - 1.0**: HIGH risk ❌
- **0.4 - 0.7**: ⚠️ MEDIUM risk
- **0.7 - 1.0**: ❌ HIGH risk```
### Fairness Metrics### Fairness Metrics
- **Disparate Impact**: Fair range 0.8 - 1.25
- **Statistical Parity**: Fair threshold < 0.1#### **POST /api/clean**- **Disparate Impact**: Fair range 0.8 - 1.25
- **Equal Opportunity**: Fair threshold < 0.1
Detect and anonymize PII in datasets.- **Statistical Parity**: Fair threshold < 0.1
---
- **Equal Opportunity**: Fair threshold < 0.1
## 🛠️ Technology Stack
**Request:**
### Backend
- **FastAPI** - Modern Python web framework```bash## Requirements
- **scikit-learn** - Machine learning
- **spaCy** - NLP for PII detectioncurl -X POST "http://localhost:8000/api/clean" \
- **PyTorch** - GPU acceleration (optional)
- **pandas** - Data processing -F "file=@Datasets/loan_data.csv"- Python 3.8+
### Frontend```- pandas >= 2.0.0
- **Next.js 14** - React framework with App Router
- **TypeScript** - Type safety- numpy >= 1.24.0
- **Tailwind CSS** - Styling
- **IndexedDB** - Browser storage**Response:**- scikit-learn >= 1.3.0
---```json
## 🤝 Contributing{See `requirements.txt` for complete list.
Contributions are welcome! Please follow these steps: "status": "success",
1. Fork the repository "dataset_info": {## Integration Examples
2. Create a feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`) "original_rows": 1000,
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request "original_columns": 15,### FastAPI Backend
--- "cleaned_rows": 1000,
## 📝 License "cleaned_columns": 13```python
This project is licensed under the MIT License - see the LICENSE file for details. },from fastapi import FastAPI, UploadFile
--- "summary": {from ai_governance import AIGovernanceAnalyzer
## 🎓 Citation "columns_removed": ["ssn", "email"],
If you use this project in your research or work, please cite: "columns_anonymized": ["phone", "address"],app = FastAPI()
```bibtex "total_cells_affected": 2847analyzer = AIGovernanceAnalyzer()
@software{nordic_privacy_ai,
title = {Nordic Privacy AI - GDPR Compliance & AI Governance Platform}, },
author = {PlatypusPus},
year = {2025}, "pii_detections": {@app.post("/analyze")
url = {https://github.com/PlatypusPus/MushroomEmpire}
} "EMAIL": 1000,async def analyze(file: UploadFile, target: str, protected: list):
```
"PHONE": 987, df = pd.read_csv(file.file)
---
"SSN": 1000 report = analyzer.analyze_dataframe(df, target, protected)
## 📧 Support
}, return report
- **Issues**: [GitHub Issues](https://github.com/PlatypusPus/MushroomEmpire/issues)
- **Discussions**: [GitHub Discussions](https://github.com/PlatypusPus/MushroomEmpire/discussions) "gdpr_compliance": [```
--- "Article 5(1)(c) - Data minimization",
## 🙏 Acknowledgments "Article 17 - Right to erasure",### Flask Backend
- Built for Nordic ecosystems (BankID, MitID, Suomi.fi) "Article 25 - Data protection by design"
- Inspired by GDPR, CCPA, and EU AI Act requirements
- Developed during a hackathon prototype ],```python
--- "files": {from flask import Flask, request, jsonify
**Made with ❤️ by the Nordic Privacy AI Team** "cleaned_csv": "/reports/cleaned_20251107_123456.csv",from ai_governance import AIGovernanceAnalyzer
"audit_report": "/reports/cleaning_audit_20251107_123456.json"
}app = Flask(__name__)
}analyzer = AIGovernanceAnalyzer()
```
@app.route('/analyze', methods=['POST'])
#### **GET /health**def analyze():
Health check endpoint with GPU status. file = request.files['file']
df = pd.read_csv(file)
**Response:** report = analyzer.analyze_dataframe(
```json df,
{ request.form['target'],
"status": "healthy", request.form.getlist('protected')
"version": "1.0.0", )
"gpu_available": true return jsonify(report)
}```
```
## License
#### **GET /reports/{filename}**
Download generated reports and cleaned files.MIT License
---## Contributing
## 🔧 ConfigurationContributions welcome! Please open an issue or submit a pull request.
### Environment Variables## Citation
Create `.env` file in `frontend/nordic-privacy-ai/`:If you use this module in your research or project, please cite:
```env
NEXT_PUBLIC_API_URL=http://localhost:8000```
```AI Governance Module - Bias Detection and Risk Analysis
https://github.com/PlatypusPus/MushroomEmpire
### CORS Configuration```
Edit `api/main.py` to add production domains:
```python
origins = [
"http://localhost:3000",
"https://your-production-domain.com"
]
```
### GPU Acceleration
GPU is automatically detected and used if available. To force CPU mode:
```python
# In cleaning.py or api endpoints
DataCleaner(use_gpu=False)
```
---
## 🧪 Testing
### Test the Backend
```powershell
# Test analyze endpoint
curl -X POST "http://localhost:8000/api/analyze" -F "file=@Datasets/loan_data.csv"
# Test clean endpoint
curl -X POST "http://localhost:8000/api/clean" -F "file=@Datasets/loan_data.csv"
# Check health
curl http://localhost:8000/health
```
### Run Unit Tests
```powershell
# Test cleaning module
python test_cleaning.py
# Run all tests (if pytest configured)
pytest
```
---
## 📊 Usage Examples
### Python SDK Usage
```python
from ai_governance import AIGovernanceAnalyzer
# Initialize analyzer
analyzer = AIGovernanceAnalyzer()
# Analyze dataset
report = analyzer.analyze(
data_path='Datasets/loan_data.csv',
target_column='loan_approved',
protected_attributes=['gender', 'age', 'race']
)
# Print results
print(f"Bias Score: {report['summary']['overall_bias_score']:.3f}")
print(f"Risk Level: {report['summary']['risk_level']}")
print(f"Model Accuracy: {report['summary']['model_accuracy']:.3f}")
# Save report
analyzer.save_report(report, 'my_report.json')
```
### Data Cleaning Usage
```python
from cleaning import DataCleaner
# Initialize cleaner with GPU
cleaner = DataCleaner(use_gpu=True)
# Load and clean data
df = cleaner.load_data('Datasets/loan_data.csv')
cleaned_df, audit = cleaner.anonymize_pii(df)
# Save results
cleaner.save_cleaned_data(cleaned_df, 'cleaned_output.csv')
cleaner.save_audit_report(audit, 'audit_report.json')
```
### Frontend Integration
```typescript
import { analyzeDataset, cleanDataset } from '@/lib/api';
// Analyze uploaded file
const handleAnalyze = async (file: File) => {
const result = await analyzeDataset(file);
console.log('Bias Score:', result.bias_metrics.overall_bias_score);
console.log('Download:', result.report_file);
};
// Clean uploaded file
const handleClean = async (file: File) => {
const result = await cleanDataset(file);
console.log('Cells anonymized:', result.summary.total_cells_affected);
console.log('Download cleaned:', result.files.cleaned_csv);
};
```
---
## 📈 Metrics Interpretation
### Bias Score (0-1, lower is better)
- **0.0 - 0.3**: ✅ Low bias - Good fairness
- **0.3 - 0.5**: ⚠️ Moderate bias - Monitoring recommended
- **0.5 - 1.0**: ❌ High bias - Immediate action required
### Risk Score (0-1, lower is better)
- **0.0 - 0.4**: ✅ LOW risk
- **0.4 - 0.7**: ⚠️ MEDIUM risk
- **0.7 - 1.0**: ❌ HIGH risk
### Fairness Metrics
- **Disparate Impact**: Fair range 0.8 - 1.25
- **Statistical Parity**: Fair threshold < 0.1
- **Equal Opportunity**: Fair threshold < 0.1
---
## 🛠️ Technology Stack
### Backend
- **FastAPI** - Modern Python web framework
- **scikit-learn** - Machine learning
- **spaCy** - NLP for PII detection
- **PyTorch** - GPU acceleration (optional)
- **pandas** - Data processing
### Frontend
- **Next.js 14** - React framework with App Router
- **TypeScript** - Type safety
- **Tailwind CSS** - Styling
- **IndexedDB** - Browser storage
---
## 🤝 Contributing
Contributions are welcome! Please follow these steps:
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
---
## 📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
---
## 🎓 Citation
If you use this project in your research or work, please cite:
```bibtex
@software{nordic_privacy_ai,
title = {Nordic Privacy AI - GDPR Compliance & AI Governance Platform},
author = {PlatypusPus},
year = {2025},
url = {https://github.com/PlatypusPus/MushroomEmpire}
}
```
---
## 📧 Support
- **Issues**: [GitHub Issues](https://github.com/PlatypusPus/MushroomEmpire/issues)
- **Discussions**: [GitHub Discussions](https://github.com/PlatypusPus/MushroomEmpire/discussions)
---
## 🙏 Acknowledgments
- Built for Nordic ecosystems (BankID, MitID, Suomi.fi)
- Inspired by GDPR, CCPA, and EU AI Act requirements
- Developed during a hackathon prototype
---
**Made with ❤️ by the Nordic Privacy AI Team**
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