AI Ethics Evaluation Framework

Load and Explore Data

Create functions to load and analyze the loan application dataset:

• Load loan_applications.csv using pandas
• Calculate approval rates by gender and race
• Visualize distribution differences across protected groups
• Identify potential sources of bias in the data

def load_and_explore(filepath):
    """Load data and compute approval rates by protected groups."""
    df = pd.read_csv(filepath)
    
    # Approval rates by gender
    gender_rates = df.groupby('gender')['approved'].mean()
    
    # Approval rates by race
    race_rates = df.groupby('race')['approved'].mean()
    
    return df, gender_rates, race_rates

Calculate Fairness Metrics

Implement functions to compute key fairness metrics:

• Demographic Parity: P(approved | group A) = P(approved | group B)
• Equalized Odds: Same TPR and FPR across groups
• Disparate Impact Ratio: Must be between 0.8 and 1.25 (80% rule)
• Predictive Parity: Same precision across groups

def calculate_demographic_parity(y_true, y_pred, protected_attr):
    """Calculate demographic parity difference between groups."""
    pass

def calculate_equalized_odds(y_true, y_pred, protected_attr):
    """Calculate TPR and FPR difference between groups."""
    pass

def calculate_disparate_impact(y_true, y_pred, protected_attr):
    """Calculate disparate impact ratio (must be 0.8-1.25)."""
    pass

Detect Bias in Model Predictions

Analyze the pre-trained model for bias:

• Load the pre-trained loan_model.pkl
• Generate predictions on the test set
• Apply fairness metrics to predictions (not just training data)
• Identify which groups are disadvantaged by the model
• Determine if bias exceeds legal thresholds

def analyze_model_bias(model, X_test, y_test, protected_attrs):
    """Analyze model predictions for bias across protected groups."""
    predictions = model.predict(X_test)
    
    results = {}
    for attr in protected_attrs:
        results[attr] = {
            'demographic_parity': calculate_demographic_parity(...),
            'equalized_odds': calculate_equalized_odds(...),
            'disparate_impact': calculate_disparate_impact(...)
        }
    
    return results

Generate SHAP Explanations

Use SHAP to explain model decisions:

• Create a SHAP TreeExplainer for the Random Forest model
• Generate global feature importance plot
• Create individual explanations for 5 denied applicants
• Identify if protected attributes (via proxies) influence decisions
• Generate a SHAP summary plot

import shap

def generate_explanations(model, X_test, feature_names):
    """Generate SHAP explanations for model decisions."""
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X_test)
    
    # Global importance
    shap.summary_plot(shap_values[1], X_test, feature_names=feature_names)
    
    return shap_values

Build Monitoring System

Create a production monitoring class:

• Log predictions with timestamps, features, and confidence
• Track approval rate over time (detect drift)
• Implement data drift detection using KL divergence
• Generate alerts when fairness metrics degrade
• Create a monitoring dashboard visualization

class FairnessMonitor:
    """Monitor model fairness in production."""
    
    def __init__(self, baseline_metrics):
        self.baseline = baseline_metrics
        self.predictions = []
        self.alerts = []
    
    def log_prediction(self, features, prediction, protected_attrs):
        """Log a prediction for monitoring."""
        pass
    
    def check_fairness_drift(self, window_size=100):
        """Check if fairness metrics have degraded."""
        pass
    
    def generate_report(self):
        """Generate monitoring report with alerts."""
        pass

Write Ethics Evaluation Report

Create a markdown report (ETHICS_REPORT.md) that includes:

• Executive summary of findings
• Detailed bias analysis with visualizations
• Fairness metric results and interpretation
• SHAP explanation insights
• Recommendations for bias mitigation
• Monitoring strategy for production

Main Execution

Create a main section that demonstrates all components:

# Main execution
if __name__ == "__main__":
    # 1. Load data
    df, gender_rates, race_rates = load_and_explore('loan_applications.csv')
    print(f"Approval rate by gender:\n{gender_rates}")
    print(f"Approval rate by race:\n{race_rates}")
    
    # 2. Load model and prepare data
    model = joblib.load('loan_model.pkl')
    X = df[['income', 'credit_score', 'debt_ratio', 'employment_years', 
            'loan_amount', 'age']]
    y = df['approved']
    
    # 3. Analyze bias
    bias_results = analyze_model_bias(model, X, y, 
                                       protected_attrs=['gender', 'race'])
    print(f"Bias Analysis Results:\n{bias_results}")
    
    # 4. Generate explanations
    shap_values = generate_explanations(model, X, X.columns.tolist())
    
    # 5. Initialize monitoring
    monitor = FairnessMonitor(baseline_metrics=bias_results)
    
    # 6. Simulate production predictions
    for i in range(100):
        monitor.log_prediction(X.iloc[i], model.predict([X.iloc[i]])[0], 
                               {'gender': df.iloc[i]['gender'], 
                                'race': df.iloc[i]['race']})
    
    # 7. Check for drift
    drift_report = monitor.check_fairness_drift()
    print(f"Drift Report:\n{drift_report}")
    
    print("Ethics evaluation complete!")