AI Ethics & Bias

Why AI Ethics Matters

As AI systems become more powerful and widely deployed, their impact on society grows. AI ethics addresses the moral principles and values that should guide AI development and deployment, ensuring these systems benefit humanity while minimizing harm.

Types of AI Bias

Detecting Bias in AI Systems

// Bias Detection Framework
class BiasDetector {
  constructor() {
    this.protectedAttributes = ['gender', 'race', 'age'];
  }

  // Calculate demographic parity
  checkDemographicParity(predictions, groups) {
    // Positive prediction rate should be similar across groups
    const rates = {};
    
    Object.keys(groups).forEach(group => {
      const groupData = groups[group];
      const positiveCount = groupData.filter((_, i) => predictions[i] === 1).length;
      rates[group] = positiveCount / groupData.length;
    });
    
    console.log("Demographic Parity Analysis:");
    Object.entries(rates).forEach(([group, rate]) => {
      console.log("  " + group + ": " + (rate * 100).toFixed(1) + "% positive predictions");
    });
    
    // Check if rates differ significantly
    const rateValues = Object.values(rates);
    const maxDiff = Math.max(...rateValues) - Math.min(...rateValues);
    
    if (maxDiff > 0.1) {
      console.log("  ⚠️  Warning: " + (maxDiff * 100).toFixed(1) + "% difference detected!");
      return false;
    }
    
    console.log("  ✓ Demographic parity satisfied");
    return true;
  }

  // Calculate equalized odds
  checkEqualizedOdds(predictions, actualLabels, groups) {
    // True positive rate and false positive rate should be equal
    const metrics = {};
    
    Object.keys(groups).forEach(group => {
      const groupData = groups[group];
      let tp = 0, fp = 0, tn = 0, fn = 0;
      
      groupData.forEach((_, i) => {
        if (predictions[i] === 1 && actualLabels[i] === 1) tp++;
        if (predictions[i] === 1 && actualLabels[i] === 0) fp++;
        if (predictions[i] === 0 && actualLabels[i] === 0) tn++;
        if (predictions[i] === 0 && actualLabels[i] === 1) fn++;
      });
      
      const tpr = tp / (tp + fn) || 0; // True Positive Rate
      const fpr = fp / (fp + tn) || 0; // False Positive Rate
      
      metrics[group] = { tpr, fpr };
    });
    
    console.log("
Equalized Odds Analysis:");
    Object.entries(metrics).forEach(([group, m]) => {
      console.log("  " + group + ":");
      console.log("    TPR: " + (m.tpr * 100).toFixed(1) + "%");
      console.log("    FPR: " + (m.fpr * 100).toFixed(1) + "%");
    });
    
    return metrics;
  }

  // Analyze feature importance
  checkFeatureCorrelation(features, protectedAttribute) {
    console.log("
Feature Correlation with " + protectedAttribute + ":");
    console.log("Checking if features are proxies for protected attributes...");
    
    // In practice, calculate actual correlations
    // Here we simulate the concept
    const suspiciousFeatures = [
      { name: 'zip_code', correlation: 0.65, risk: 'high' },
      { name: 'first_name', correlation: 0.52, risk: 'medium' },
      { name: 'education_level', correlation: 0.38, risk: 'medium' }
    ];
    
    suspiciousFeatures.forEach(feature => {
      const icon = feature.risk === 'high' ? '⚠️' : '⚠️';
      console.log("  " + icon + " " + feature.name + ": " + (feature.correlation * 100).toFixed(1) + "% correlation");
    });
  }
}

// Example usage
const detector = new BiasDetector();

// Simulated loan approval predictions
const predictions = [1, 1, 0, 1, 0, 1, 0, 0, 1, 1];
const actualLabels = [1, 1, 0, 1, 1, 1, 0, 0, 1, 0];

const groups = {
  'Group A': [0, 1, 2, 3, 4],
  'Group B': [5, 6, 7, 8, 9]
};

detector.checkDemographicParity(predictions, groups);
detector.checkEqualizedOdds(predictions, actualLabels, groups);
detector.checkFeatureCorrelation(['zip_code', 'income', 'education'], 'race');

Fairness Metrics

Ethical AI Principles

Implementing Responsible AI

// Responsible AI Checklist
class ResponsibleAIChecklist {
  constructor() {
    this.checks = [
      {
        category: 'Data',
        items: [
          'Diverse and representative training data',
          'Data privacy protections in place',
          'Documented data sources and collection methods',
          'Checked for historical biases in data',
          'Obtained proper consent for data use'
        ]
      },
      {
        category: 'Model Development',
        items: [
          'Tested on diverse demographic groups',
          'Measured fairness metrics',
          'Documented model limitations',
          'Implemented explainability features',
          'Adversarial testing completed'
        ]
      },
      {
        category: 'Deployment',
        items: [
          'Human oversight mechanism in place',
          'Clear disclosure that AI is being used',
          'Appeals/redress process established',
          'Monitoring system for drift and bias',
          'Regular audits scheduled'
        ]
      },
      {
        category: 'Governance',
        items: [
          'Ethics review completed',
          'Impact assessment conducted',
          'Stakeholder feedback collected',
          'Compliance with regulations verified',
          'Incident response plan ready'
        ]
      }
    ];
  }

  evaluate(project) {
    console.log("Responsible AI Evaluation
");
    console.log("Project: " + project.name + "
");
    
    let totalChecks = 0;
    let passedChecks = 0;
    
    this.checks.forEach(category => {
      console.log("=== " + category.category + " ===");
      category.items.forEach(item => {
        totalChecks++;
        // In practice, actually evaluate each item
        const passed = Math.random() > 0.3; // Simulated
        passedChecks += passed ? 1 : 0;
        
        const icon = passed ? '✓' : '✗';
        const color = passed ? 'green' : 'red';
        console.log("  " + icon + " " + item + "");
      });
      console.log("");
    });
    
    const score = (passedChecks / totalChecks * 100).toFixed(1);
    console.log("Overall Score: " + score + "%");
    
    if (score < 70) {
      console.log("⚠️  Warning: Project needs significant improvements before deployment");
    } else if (score < 90) {
      console.log("⚠️  Caution: Address remaining issues before deployment");
    } else {
      console.log("✓ Project meets responsible AI standards");
    }
  }
}

// Example
const checker = new ResponsibleAIChecklist();
checker.evaluate({ name: "Loan Approval AI" });