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Feat:Create the Basic Ai Governance Package to use has a guide
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README.md
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README.md
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# AI Governance Module
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A Python package for detecting bias and analyzing risks in machine learning models. Provides comprehensive fairness metrics, privacy risk assessment, and ethical AI evaluation.
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## Features
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### 🎯 Bias Detection
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- **Fairness Metrics**: Disparate Impact, Statistical Parity Difference, Equal Opportunity Difference
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- **Demographic Analysis**: Group-wise performance evaluation
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- **Violation Detection**: Automatic flagging with severity levels
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### 🛡️ Risk Assessment
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- **Privacy Risks**: PII detection, GDPR compliance, data exposure analysis
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- **Ethical Risks**: Fairness, transparency, accountability, social impact
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- **Compliance Risks**: Regulatory adherence (GDPR, CCPA, AI Act)
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- **Data Quality**: Missing data, class imbalance, outlier detection
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### 🤖 Machine Learning
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- Generalized classification model (works with any dataset)
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- Auto-detection of feature types and protected attributes
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- Comprehensive performance metrics
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- Feature importance analysis
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## Installation
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```bash
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pip install -r requirements.txt
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```
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Or install as a package:
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```bash
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pip install -e .
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```
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## Quick Start
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```python
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from ai_governance import AIGovernanceAnalyzer
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# Initialize analyzer
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analyzer = AIGovernanceAnalyzer()
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# Run complete analysis
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report = analyzer.analyze(
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data_path='your_data.csv',
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target_column='target',
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protected_attributes=['gender', 'age', 'race']
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)
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# Access results
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print(f"Bias Score: {report['summary']['overall_bias_score']:.3f}")
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print(f"Risk Level: {report['summary']['risk_level']}")
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print(f"Model Accuracy: {report['summary']['model_accuracy']:.3f}")
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# Save report
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analyzer.save_report(report, 'governance_report.json')
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```
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## Module Structure
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```
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ai_governance/
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├── __init__.py # Main API
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├── data_processor.py # Data preprocessing
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├── model_trainer.py # ML model training
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├── bias_analyzer.py # Bias detection
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├── risk_analyzer.py # Risk assessment
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└── report_generator.py # Report generation
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```
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## API Reference
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### AIGovernanceAnalyzer
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Main class for running AI governance analysis.
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```python
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analyzer = AIGovernanceAnalyzer()
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# Analyze from DataFrame
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report = analyzer.analyze_dataframe(
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df=dataframe,
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target_column='target',
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protected_attributes=['gender', 'age']
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)
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# Analyze from file
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report = analyzer.analyze(
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data_path='data.csv',
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target_column='target',
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protected_attributes=['gender', 'age']
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)
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```
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### Individual Components
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```python
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from ai_governance import (
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DataProcessor,
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GeneralizedModelTrainer,
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BiasAnalyzer,
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RiskAnalyzer,
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ReportGenerator
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)
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# Process data
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processor = DataProcessor(df)
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processor.target_column = 'target'
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processor.protected_attributes = ['gender', 'age']
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processor.prepare_data()
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# Train model
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trainer = GeneralizedModelTrainer(
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processor.X_train,
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processor.X_test,
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processor.y_train,
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processor.y_test,
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processor.feature_names
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)
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trainer.train()
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trainer.evaluate()
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# Analyze bias
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bias_analyzer = BiasAnalyzer(
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processor.X_test,
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processor.y_test,
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trainer.y_pred,
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processor.df,
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processor.protected_attributes,
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processor.target_column
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)
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bias_results = bias_analyzer.analyze()
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# Assess risks
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risk_analyzer = RiskAnalyzer(
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processor.df,
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trainer.results,
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bias_results,
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processor.protected_attributes,
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processor.target_column
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)
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risk_results = risk_analyzer.analyze()
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# Generate report
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report_gen = ReportGenerator(
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trainer.results,
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bias_results,
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risk_results,
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processor.df
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)
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report = report_gen.generate_report()
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```
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## Report Structure
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The module generates comprehensive JSON reports:
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```json
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{
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"metadata": {
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"report_id": "unique_id",
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"generated_at": "timestamp",
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"dataset_info": {}
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},
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"summary": {
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"overall_bias_score": 0.0-1.0,
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"overall_risk_score": 0.0-1.0,
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"risk_level": "LOW|MEDIUM|HIGH",
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"model_accuracy": 0.0-1.0,
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"fairness_violations_count": 0
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},
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"model_performance": {},
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"bias_analysis": {},
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"risk_assessment": {},
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"key_findings": [],
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"recommendations": []
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}
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```
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## Metrics Interpretation
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### Bias Score (0-1, lower is better)
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- **0.0 - 0.3**: Low bias ✅
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- **0.3 - 0.5**: Moderate bias ⚠️
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- **0.5 - 1.0**: High bias ❌
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### Risk Score (0-1, lower is better)
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- **0.0 - 0.4**: LOW risk ✅
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- **0.4 - 0.7**: MEDIUM risk ⚠️
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- **0.7 - 1.0**: HIGH risk ❌
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### Fairness Metrics
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- **Disparate Impact**: Fair range 0.8 - 1.25
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- **Statistical Parity**: Fair threshold < 0.1
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- **Equal Opportunity**: Fair threshold < 0.1
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## Requirements
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- Python 3.8+
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- pandas >= 2.0.0
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- numpy >= 1.24.0
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- scikit-learn >= 1.3.0
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See `requirements.txt` for complete list.
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## Integration Examples
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### FastAPI Backend
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```python
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from fastapi import FastAPI, UploadFile
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from ai_governance import AIGovernanceAnalyzer
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app = FastAPI()
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analyzer = AIGovernanceAnalyzer()
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@app.post("/analyze")
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async def analyze(file: UploadFile, target: str, protected: list):
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df = pd.read_csv(file.file)
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report = analyzer.analyze_dataframe(df, target, protected)
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return report
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```
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### Flask Backend
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```python
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from flask import Flask, request, jsonify
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from ai_governance import AIGovernanceAnalyzer
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app = Flask(__name__)
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analyzer = AIGovernanceAnalyzer()
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@app.route('/analyze', methods=['POST'])
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def analyze():
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file = request.files['file']
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df = pd.read_csv(file)
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report = analyzer.analyze_dataframe(
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df,
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request.form['target'],
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request.form.getlist('protected')
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)
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return jsonify(report)
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```
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## License
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MIT License
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## Contributing
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Contributions welcome! Please open an issue or submit a pull request.
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## Citation
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If you use this module in your research or project, please cite:
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```
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AI Governance Module - Bias Detection and Risk Analysis
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https://github.com/PlatypusPus/MushroomEmpire
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```
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