MushroomEmpire/BIAS_ANALYSIS_GUIDE.md

Enhanced Bias & Fairness Analysis Guide

Overview

The Nordic Privacy AI platform now includes a comprehensive, adaptive bias and fairness analysis system that works accurately across all types of datasets, including:

• Small datasets (< 100 samples)
• Imbalanced groups
• Multiple protected attributes
• Binary and multi-class targets
• High-cardinality features
• Missing data

Key Enhancements

1. Adaptive Fairness Thresholds

The system automatically adjusts fairness thresholds based on dataset characteristics:

• Sample Size Factor: Relaxes thresholds for small sample sizes
• Group Imbalance Factor: Adjusts for unequal group sizes
• Dynamic Thresholds:
  • Disparate Impact: 0.7-0.8 (adapts to data)
  • Statistical Parity: 0.1-0.15 (adapts to data)
  • Equal Opportunity: 0.1-0.15 (adapts to data)

2. Comprehensive Fairness Metrics

Individual Metrics (6 types analyzed):

1. Disparate Impact Ratio (4/5ths rule)

   • Measures: min_rate / max_rate across all groups
   • Fair range: 0.8 - 1.25 (or adaptive)
   • Higher weight in overall score

2. Statistical Parity Difference

   • Measures: Absolute difference in positive rates
   • Fair threshold: < 0.1 (or adaptive)
   • Ensures equal selection rates

3. Equal Opportunity (TPR equality)

   • Measures: Difference in True Positive Rates
   • Fair threshold: < 0.1 (or adaptive)
   • Ensures equal recall across groups

4. Equalized Odds (TPR + FPR equality)

   • Measures: Both TPR and FPR differences
   • Fair threshold: < 0.1 (or adaptive)
   • Most comprehensive fairness criterion

5. Predictive Parity (Precision equality)

   • Measures: Difference in precision across groups
   • Fair threshold: < 0.1
   • Ensures positive predictions are equally accurate

6. Calibration (FNR equality)

   • Measures: Difference in False Negative Rates
   • Fair threshold: < 0.1
   • Ensures balanced error rates

Group-Level Metrics (per demographic group):

• Positive Rate
• Selection Rate
• True Positive Rate (TPR/Recall/Sensitivity)
• False Positive Rate (FPR)
• True Negative Rate (TNR/Specificity)
• False Negative Rate (FNR)
• Precision (PPV)
• F1 Score
• Accuracy
• Sample Size & Distribution

3. Weighted Bias Scoring

The overall bias score (0-1, higher = more bias) is calculated using:

Overall Score = Weighted Average of:
  - Disparate Impact (weight: 1.5x sample_weight)
  - Statistical Parity (weight: 1.0x sample_weight)
  - Equal Opportunity (weight: 1.0x sample_weight)
  - Equalized Odds (weight: 0.8x sample_weight)
  - Predictive Parity (weight: 0.7x sample_weight)
  - Calibration (weight: 0.7x sample_weight)

Sample weight = min(1.0, total_samples / 100)

4. Intelligent Violation Detection

Violations are categorized by severity:

• CRITICAL: di_value < 0.5, or deviation > 50%
• HIGH: di_value < 0.6, or deviation > 30%
• MEDIUM: di_value < 0.7, or deviation > 15%
• LOW: Minor deviations

Each violation includes:

• Affected groups
• Specific measurements
• Actionable recommendations
• Context-aware severity assessment

5. Robust Data Handling

Missing Values:

• Numerical: Filled with median
• Categorical: Filled with mode or 'Unknown'
• Comprehensive logging

Data Type Detection:

• Binary detection (0/1, Yes/No)
• Small discrete values (< 10 unique)
• High cardinality warnings (> 50 categories)
• Mixed type handling

Target Encoding:

• Automatic categorical → numeric conversion
• Binary value normalization
• Clear encoding maps printed

Class Imbalance:

• Stratified splitting when appropriate
• Minimum class size validation
• Balanced metrics calculation

6. Enhanced Reporting

Each analysis includes:

{
  "overall_bias_score": 0.954,
  "fairness_metrics": {
    "Gender": {
      "disparate_impact": {
        "value": 0.276,
        "threshold": 0.8,
        "fair": false,
        "min_group": "Female",
        "max_group": "Male",
        "min_rate": 0.25,
        "max_rate": 0.906
      },
      "statistical_parity_difference": {...},
      "equal_opportunity_difference": {...},
      "equalized_odds": {...},
      "predictive_parity": {...},
      "calibration": {...},
      "attribute_fairness_score": 0.89,
      "group_metrics": {
        "Male": {
          "positive_rate": 0.906,
          "tpr": 0.95,
          "fpr": 0.03,
          "precision": 0.92,
          "f1_score": 0.93,
          "sample_size": 450
        },
        "Female": {...}
      },
      "sample_statistics": {
        "total_samples": 500,
        "min_group_size": 50,
        "max_group_size": 450,
        "imbalance_ratio": 0.11,
        "num_groups": 2
      }
    }
  },
  "fairness_violations": [
    {
      "attribute": "Gender",
      "metric": "Disparate Impact",
      "severity": "CRITICAL",
      "value": 0.276,
      "affected_groups": ["Female", "Male"],
      "message": "...",
      "recommendation": "CRITICAL: Group 'Female' has less than half the approval rate..."
    }
  ]
}

Usage Examples

Basic Analysis

from ai_governance import AIGovernanceAnalyzer

# Initialize
analyzer = AIGovernanceAnalyzer()

# Analyze with protected attributes
report = analyzer.analyze(
    df=your_dataframe,
    target_column='ApprovalStatus',
    protected_attributes=['Gender', 'Age', 'Race']
)

# Check bias score
print(f"Bias Score: {report['bias_analysis']['overall_bias_score']:.1%}")

# Review violations
for violation in report['bias_analysis']['fairness_violations']:
    print(f"{violation['severity']}: {violation['message']}")

With Presidio (Enhanced PII Detection)

# Enable Presidio for automatic demographic detection
analyzer = AIGovernanceAnalyzer(use_presidio=True)

API Usage

curl -X POST http://localhost:8000/api/analyze \
  -F "file=@dataset.csv" \
  -F "target_column=Outcome" \
  -F "protected_attributes=Gender,Age"

Interpreting Results

Overall Bias Score

• < 0.3: Low bias - Excellent fairness ✅
• 0.3 - 0.5: Moderate bias - Monitor recommended ⚠️
• > 0.5: High bias - Action required ❌

Disparate Impact

• 0.8 - 1.25: Fair (4/5ths rule satisfied)
• < 0.8: Disadvantaged group exists
• > 1.25: Advantaged group exists

Statistical Parity

• < 0.1: Fair (similar positive rates)
• > 0.1: Groups receive different treatment

Recommendations by Severity

CRITICAL

• DO NOT DEPLOY without remediation
• Investigate systemic bias sources
• Review training data representation
• Implement fairness constraints
• Consider re-collection if necessary

HIGH

• Address before deployment
• Use fairness-aware training methods
• Implement threshold optimization
• Regular monitoring required

MEDIUM

• Monitor closely
• Consider mitigation strategies
• Regular fairness audits
• Document findings

LOW

• Continue monitoring
• Maintain fairness standards
• Periodic reviews

Best Practices

1. Data Collection

• Ensure representative sampling
• Balance protected groups when possible
• Document data sources
• Check for historical bias

2. Feature Engineering

• Avoid proxy features for protected attributes
• Check feature correlations with demographics
• Use feature importance analysis
• Consider fairness-aware feature selection

3. Model Training

• Use fairness-aware algorithms
• Implement fairness constraints
• Try multiple fairness definitions
• Cross-validate with fairness metrics

4. Post-Processing

• Threshold optimization per group
• Calibration techniques
• Reject option classification
• Regular bias audits

5. Monitoring

• Track fairness metrics over time
• Monitor for fairness drift
• Regular re-evaluation
• Document all findings

Technical Details

Dependencies

numpy>=1.21.0
pandas>=1.3.0
scikit-learn>=1.0.0
presidio-analyzer>=2.2.0  # Optional
spacy>=3.0.0  # Optional for Presidio

Performance

• Handles datasets from 50 to 1M+ rows
• Adaptive algorithms scale with data size
• Memory-efficient group comparisons
• Parallel metric calculations

Limitations

• Requires at least 2 groups per protected attribute
• Minimum 10 samples per group recommended
• Binary classification focus (multi-class supported)
• Assumes independent test set

Troubleshooting

"Insufficient valid groups"

• Check protected attribute has at least 2 non-null groups
• Ensure groups appear in test set
• Increase test_size parameter

"High cardinality warning"

• Feature has > 50 unique values
• Consider grouping categories
• May need feature engineering

"Sample size too small"

• System adapts automatically
• Results may be less reliable
• Consider collecting more data

"Presidio initialization failed"

• Install: pip install presidio-analyzer spacy
• Download model: python -m spacy download en_core_web_sm
• Or use use_presidio=False

References

• Fairness Definitions Explained
• 4/5ths Rule (EEOC)
• Equalized Odds
• Fairness Through Awareness

Support

For issues or questions:

• Check logs for detailed diagnostic messages
• Review sample statistics in output
• Consult violation recommendations
• Contact: support@nordicprivacyai.com