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BIAS_ANALYSIS_GUIDE.md
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BIAS_ANALYSIS_GUIDE.md
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# Enhanced Bias & Fairness Analysis Guide
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## Overview
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The Nordic Privacy AI platform now includes a comprehensive, adaptive bias and fairness analysis system that works accurately across **all types of datasets**, including:
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- Small datasets (< 100 samples)
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- Imbalanced groups
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- Multiple protected attributes
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- Binary and multi-class targets
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- High-cardinality features
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- Missing data
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## Key Enhancements
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### 1. **Adaptive Fairness Thresholds**
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The system automatically adjusts fairness thresholds based on dataset characteristics:
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- **Sample Size Factor**: Relaxes thresholds for small sample sizes
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- **Group Imbalance Factor**: Adjusts for unequal group sizes
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- **Dynamic Thresholds**:
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- Disparate Impact: 0.7-0.8 (adapts to data)
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- Statistical Parity: 0.1-0.15 (adapts to data)
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- Equal Opportunity: 0.1-0.15 (adapts to data)
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### 2. **Comprehensive Fairness Metrics**
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#### Individual Metrics (6 types analyzed):
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1. **Disparate Impact Ratio** (4/5ths rule)
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- Measures: min_rate / max_rate across all groups
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- Fair range: 0.8 - 1.25 (or adaptive)
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- Higher weight in overall score
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2. **Statistical Parity Difference**
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- Measures: Absolute difference in positive rates
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- Fair threshold: < 0.1 (or adaptive)
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- Ensures equal selection rates
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3. **Equal Opportunity** (TPR equality)
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- Measures: Difference in True Positive Rates
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- Fair threshold: < 0.1 (or adaptive)
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- Ensures equal recall across groups
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4. **Equalized Odds** (TPR + FPR equality)
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- Measures: Both TPR and FPR differences
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- Fair threshold: < 0.1 (or adaptive)
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- Most comprehensive fairness criterion
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5. **Predictive Parity** (Precision equality)
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- Measures: Difference in precision across groups
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- Fair threshold: < 0.1
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- Ensures positive predictions are equally accurate
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6. **Calibration** (FNR equality)
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- Measures: Difference in False Negative Rates
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- Fair threshold: < 0.1
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- Ensures balanced error rates
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#### Group-Level Metrics (per demographic group):
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- Positive Rate
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- Selection Rate
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- True Positive Rate (TPR/Recall/Sensitivity)
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- False Positive Rate (FPR)
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- True Negative Rate (TNR/Specificity)
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- False Negative Rate (FNR)
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- Precision (PPV)
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- F1 Score
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- Accuracy
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- Sample Size & Distribution
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### 3. **Weighted Bias Scoring**
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The overall bias score (0-1, higher = more bias) is calculated using:
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```python
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Overall Score = Weighted Average of:
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- Disparate Impact (weight: 1.5x sample_weight)
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- Statistical Parity (weight: 1.0x sample_weight)
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- Equal Opportunity (weight: 1.0x sample_weight)
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- Equalized Odds (weight: 0.8x sample_weight)
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- Predictive Parity (weight: 0.7x sample_weight)
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- Calibration (weight: 0.7x sample_weight)
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```
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Sample weight = min(1.0, total_samples / 100)
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### 4. **Intelligent Violation Detection**
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Violations are categorized by severity:
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- **CRITICAL**: di_value < 0.5, or deviation > 50%
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- **HIGH**: di_value < 0.6, or deviation > 30%
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- **MEDIUM**: di_value < 0.7, or deviation > 15%
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- **LOW**: Minor deviations
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Each violation includes:
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- Affected groups
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- Specific measurements
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- Actionable recommendations
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- Context-aware severity assessment
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### 5. **Robust Data Handling**
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#### Missing Values:
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- Numerical: Filled with median
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- Categorical: Filled with mode or 'Unknown'
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- Comprehensive logging
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#### Data Type Detection:
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- Binary detection (0/1, Yes/No)
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- Small discrete values (< 10 unique)
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- High cardinality warnings (> 50 categories)
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- Mixed type handling
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#### Target Encoding:
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- Automatic categorical → numeric conversion
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- Binary value normalization
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- Clear encoding maps printed
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#### Class Imbalance:
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- Stratified splitting when appropriate
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- Minimum class size validation
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- Balanced metrics calculation
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### 6. **Enhanced Reporting**
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Each analysis includes:
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```json
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{
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"overall_bias_score": 0.954,
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"fairness_metrics": {
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"Gender": {
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"disparate_impact": {
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"value": 0.276,
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"threshold": 0.8,
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"fair": false,
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"min_group": "Female",
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"max_group": "Male",
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"min_rate": 0.25,
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"max_rate": 0.906
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},
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"statistical_parity_difference": {...},
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"equal_opportunity_difference": {...},
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"equalized_odds": {...},
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"predictive_parity": {...},
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"calibration": {...},
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"attribute_fairness_score": 0.89,
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"group_metrics": {
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"Male": {
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"positive_rate": 0.906,
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"tpr": 0.95,
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"fpr": 0.03,
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"precision": 0.92,
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"f1_score": 0.93,
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"sample_size": 450
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},
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"Female": {...}
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},
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"sample_statistics": {
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"total_samples": 500,
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"min_group_size": 50,
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"max_group_size": 450,
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"imbalance_ratio": 0.11,
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"num_groups": 2
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}
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}
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},
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"fairness_violations": [
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{
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"attribute": "Gender",
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"metric": "Disparate Impact",
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"severity": "CRITICAL",
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"value": 0.276,
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"affected_groups": ["Female", "Male"],
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"message": "...",
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"recommendation": "CRITICAL: Group 'Female' has less than half the approval rate..."
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}
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]
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}
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```
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## Usage Examples
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### Basic Analysis
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```python
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from ai_governance import AIGovernanceAnalyzer
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# Initialize
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analyzer = AIGovernanceAnalyzer()
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# Analyze with protected attributes
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report = analyzer.analyze(
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df=your_dataframe,
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target_column='ApprovalStatus',
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protected_attributes=['Gender', 'Age', 'Race']
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)
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# Check bias score
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print(f"Bias Score: {report['bias_analysis']['overall_bias_score']:.1%}")
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# Review violations
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for violation in report['bias_analysis']['fairness_violations']:
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print(f"{violation['severity']}: {violation['message']}")
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```
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### With Presidio (Enhanced PII Detection)
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```python
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# Enable Presidio for automatic demographic detection
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analyzer = AIGovernanceAnalyzer(use_presidio=True)
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```
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### API Usage
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```bash
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curl -X POST http://localhost:8000/api/analyze \
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-F "file=@dataset.csv" \
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-F "target_column=Outcome" \
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-F "protected_attributes=Gender,Age"
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```
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## Interpreting Results
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### Overall Bias Score
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- **< 0.3**: Low bias - Excellent fairness ✅
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- **0.3 - 0.5**: Moderate bias - Monitor recommended ⚠️
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- **> 0.5**: High bias - Action required ❌
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### Disparate Impact
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- **0.8 - 1.25**: Fair (4/5ths rule satisfied)
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- **< 0.8**: Disadvantaged group exists
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- **> 1.25**: Advantaged group exists
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### Statistical Parity
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- **< 0.1**: Fair (similar positive rates)
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- **> 0.1**: Groups receive different treatment
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### Recommendations by Severity
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#### CRITICAL
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- **DO NOT DEPLOY** without remediation
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- Investigate systemic bias sources
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- Review training data representation
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- Implement fairness constraints
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- Consider re-collection if necessary
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#### HIGH
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- Address before deployment
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- Use fairness-aware training methods
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- Implement threshold optimization
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- Regular monitoring required
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#### MEDIUM
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- Monitor closely
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- Consider mitigation strategies
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- Regular fairness audits
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- Document findings
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#### LOW
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- Continue monitoring
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- Maintain fairness standards
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- Periodic reviews
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## Best Practices
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### 1. Data Collection
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- Ensure representative sampling
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- Balance protected groups when possible
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- Document data sources
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- Check for historical bias
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### 2. Feature Engineering
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- Avoid proxy features for protected attributes
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- Check feature correlations with demographics
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- Use feature importance analysis
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- Consider fairness-aware feature selection
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### 3. Model Training
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- Use fairness-aware algorithms
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- Implement fairness constraints
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- Try multiple fairness definitions
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- Cross-validate with fairness metrics
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### 4. Post-Processing
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- Threshold optimization per group
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- Calibration techniques
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- Reject option classification
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- Regular bias audits
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### 5. Monitoring
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- Track fairness metrics over time
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- Monitor for fairness drift
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- Regular re-evaluation
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- Document all findings
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## Technical Details
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### Dependencies
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```
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numpy>=1.21.0
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pandas>=1.3.0
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scikit-learn>=1.0.0
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presidio-analyzer>=2.2.0 # Optional
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spacy>=3.0.0 # Optional for Presidio
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```
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### Performance
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- Handles datasets from 50 to 1M+ rows
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- Adaptive algorithms scale with data size
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- Memory-efficient group comparisons
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- Parallel metric calculations
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### Limitations
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- Requires at least 2 groups per protected attribute
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- Minimum 10 samples per group recommended
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- Binary classification focus (multi-class supported)
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- Assumes independent test set
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## Troubleshooting
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### "Insufficient valid groups"
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- Check protected attribute has at least 2 non-null groups
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- Ensure groups appear in test set
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- Increase test_size parameter
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### "High cardinality warning"
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- Feature has > 50 unique values
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- Consider grouping categories
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- May need feature engineering
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### "Sample size too small"
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- System adapts automatically
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- Results may be less reliable
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- Consider collecting more data
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### "Presidio initialization failed"
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- Install: `pip install presidio-analyzer spacy`
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- Download model: `python -m spacy download en_core_web_sm`
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- Or use `use_presidio=False`
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## References
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- [Fairness Definitions Explained](https://fairware.cs.umass.edu/papers/Verma.pdf)
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- [4/5ths Rule (EEOC)](https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-common-interpretation-uniform-guidelines)
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- [Equalized Odds](https://arxiv.org/abs/1610.02413)
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- [Fairness Through Awareness](https://arxiv.org/abs/1104.3913)
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## Support
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For issues or questions:
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- Check logs for detailed diagnostic messages
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- Review sample statistics in output
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- Consult violation recommendations
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- Contact: support@nordicprivacyai.com
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