interface AIEthicsFailure {
  company: string;
  issue: string;
  impact: string;
  cost: number;
  lessons: string[];
}
 
const recentFailures: AIEthicsFailure[] = [
  {
    company: "Financial Services Giant",
    issue: "Credit scoring algorithm exhibited racial bias",
    impact: "Discriminatory lending practices, regulatory investigation",
    cost: 25_000_000, // $25M settlement
    lessons: [
      "Bias testing must be continuous, not one-time",
      "Historical data perpetuates historical biases",
      "Human oversight is crucial for high-impact decisions"
    ]
  },
  {
    company: "Healthcare AI Company",
    issue: "Diagnostic AI trained on non-diverse datasets",
    impact: "Poor performance on underrepresented populations",
    cost: 50_000_000, // $50M in lost contracts and remediation
    lessons: [
      "Dataset diversity is not optional",
      "External validation is essential",
      "Stakeholder involvement from day one"
    ]
  },
  {
    company: "Hiring Platform",
    issue: "Resume screening AI discriminated against women",
    impact: "Class action lawsuit, platform shutdown",
    cost: 75_000_000, // $75M in damages and lost revenue
    lessons: [
      "Gender-neutral doesn't mean bias-free",
      "Regular auditing prevents systemic issues",
      "Transparency builds trust and catches problems early"
    ]
  }
];

interface HumanCenteredAI {
  humanOversight: boolean;
  userExplainability: boolean;
  humanInTheLoop: boolean;
  userConsent: boolean;
  exitStrategy: boolean; // Users can opt out
}
 
class HumanCenteredAISystem implements HumanCenteredAI {
  constructor(
    private readonly mlModel: MLModel,
    private readonly humanReviewer: HumanReviewer,
    private readonly explainabilityEngine: ExplainabilityEngine
  ) {}
  
  async makePrediction(input: PredictionInput): Promise<AIDecision> {
    // Generate AI prediction
    const aiPrediction = await this.mlModel.predict(input);
    
    // Assess confidence and risk
    const riskAssessment = this.assessDecisionRisk(aiPrediction, input);
    
    // High-risk decisions require human review
    if (riskAssessment.requiresHumanReview) {
      const humanDecision = await this.humanReviewer.review(
        aiPrediction,
        input,
        riskAssessment
      );
      
      return this.combineAIAndHumanInsights(aiPrediction, humanDecision);
    }
    
    // Generate explanation for all decisions
    const explanation = await this.explainabilityEngine.explain(
      aiPrediction,
      input
    );
    
    return {
      prediction: aiPrediction,
      confidence: riskAssessment.confidence,
      explanation: explanation,
      humanReviewed: false,
      canAppeal: true
    };
  }
  
  private assessDecisionRisk(prediction: Prediction, input: PredictionInput): RiskAssessment {
    const riskFactors = [
      this.assessPredictionConfidence(prediction),
      this.assessInputSensitivity(input),
      this.assessBusinessImpact(prediction),
      this.assessBiasRisk(prediction, input)
    ];
    
    return {
      overallRisk: this.calculateOverallRisk(riskFactors),
      requiresHumanReview: this.shouldRequireHumanReview(riskFactors),
      confidence: prediction.confidence,
      riskFactors: riskFactors
    };
  }
}

class BiasDetectionFramework:
    def __init__(self, model, training_data, protected_attributes):
        self.model = model
        self.training_data = training_data
        self.protected_attributes = protected_attributes  # gender, race, age, etc.
        
    def detect_bias(self, test_data: pd.DataFrame) -> BiasReport:
        """Comprehensive bias detection across multiple dimensions."""
        
        bias_metrics = {}
        
        for attribute in self.protected_attributes:
            # Statistical parity difference
            spd = self.calculate_statistical_parity_difference(test_data, attribute)
            
            # Equal opportunity difference
            eod = self.calculate_equal_opportunity_difference(test_data, attribute)
            
            # Demographic parity
            dp = self.calculate_demographic_parity(test_data, attribute)
            
            bias_metrics[attribute] = {
                'statistical_parity_difference': spd,
                'equal_opportunity_difference': eod,
                'demographic_parity': dp,
                'bias_severity': self.assess_bias_severity(spd, eod, dp)
            }
        
        return BiasReport(
            overall_bias_score=self.calculate_overall_bias(bias_metrics),
            detailed_metrics=bias_metrics,
            recommendations=self.generate_bias_mitigation_recommendations(bias_metrics)
        )
    
    def mitigate_bias(self, bias_report: BiasReport) -> MitigationPlan:
        """Generate and implement bias mitigation strategies."""
        
        mitigation_strategies = []
        
        for attribute, metrics in bias_report.detailed_metrics.items():
            if metrics['bias_severity'] == 'HIGH':
                # Data-level interventions
                mitigation_strategies.append(
                    DataAugmentationStrategy(
                        target_attribute=attribute,
                        augmentation_factor=self.calculate_augmentation_factor(metrics)
                    )
                )
                
                # Algorithm-level interventions
                mitigation_strategies.append(
                    FairnessConstraintStrategy(
                        constraint_type='demographic_parity',
                        target_attribute=attribute,
                        tolerance=0.05  # 5% tolerance
                    )
                )
                
            elif metrics['bias_severity'] == 'MEDIUM':
                # Post-processing interventions
                mitigation_strategies.append(
                    CalibrationStrategy(
                        target_attribute=attribute,
                        calibration_method='equalized_odds'
                    )
                )
        
        return MitigationPlan(
            strategies=mitigation_strategies,
            implementation_order=self.prioritize_strategies(mitigation_strategies),
            expected_improvement=self.estimate_bias_reduction(mitigation_strategies)
        )
    
    def calculate_statistical_parity_difference(self, data: pd.DataFrame, attribute: str) -> float:
        """Calculate statistical parity difference for a protected attribute."""
        
        # Group data by protected attribute
        groups = data.groupby(attribute)
        
        # Calculate positive prediction rates for each group
        positive_rates = groups.apply(
            lambda group: (group['prediction'] == 1).mean()
        )
        
        # Calculate parity difference (max - min)
        return positive_rates.max() - positive_rates.min()
    
    def implement_fairness_constraints(self, constraint_type: str, tolerance: float = 0.05):
        """Implement fairness constraints during model training."""
        
        if constraint_type == 'demographic_parity':
            return DemographicParityConstraint(tolerance=tolerance)
        elif constraint_type == 'equalized_opportunity':
            return EqualizedOpportunityConstraint(tolerance=tolerance)
        elif constraint_type == 'calibration':
            return CalibrationConstraint(tolerance=tolerance)
        else:
            raise ValueError(f"Unknown constraint type: {constraint_type}")
 
# Usage example
bias_detector = BiasDetectionFramework(
    model=trained_model,
    training_data=training_df,
    protected_attributes=['gender', 'race', 'age_group']
)
 
bias_report = bias_detector.detect_bias(test_data)
mitigation_plan = bias_detector.mitigate_bias(bias_report)
 
print(f"Overall bias score: {bias_report.overall_bias_score}")
print(f"Mitigation strategies: {len(mitigation_plan.strategies)}")

class ContinuousBiasMonitor {
  private readonly biasDetector: BiasDetectionFramework;
  private readonly alertSystem: AlertSystem;
  private readonly auditLogger: AuditLogger;
  
  async monitorBias(predictionBatch: PredictionBatch): Promise<void> {
    // Analyze batch for bias indicators
    const biasMetrics = await this.biasDetector.analyzeBatch(predictionBatch);
    
    // Check against established thresholds
    const violations = this.checkBiasThresholds(biasMetrics);
    
    if (violations.length > 0) {
      // Log violations for audit trail
      await this.auditLogger.logBiasViolations(violations);
      
      // Alert relevant stakeholders
      await this.alertSystem.sendBiasAlert({
        severity: this.calculateAlertSeverity(violations),
        violations: violations,
        affectedPredictions: predictionBatch,
        recommendedActions: this.generateRecommendedActions(violations)
      });
      
      // Auto-trigger mitigation if configured
      if (this.isAutoMitigationEnabled(violations)) {
        await this.triggerAutoMitigation(violations);
      }
    }
    
    // Store metrics for trend analysis
    await this.storeMetricsForTrendAnalysis(biasMetrics);
  }
  
  private checkBiasThresholds(metrics: BiasMetrics): BiasViolation[] {
    const violations = [];
    
    for (const [attribute, values] of Object.entries(metrics)) {
      if (values.statisticalParityDifference > 0.1) { // 10% threshold
        violations.push(new BiasViolation(
          attribute,
          'statistical_parity',
          values.statisticalParityDifference,
          'HIGH'
        ));
      }
      
      if (values.equalOpportunityDifference > 0.05) { // 5% threshold
        violations.push(new BiasViolation(
          attribute,
          'equal_opportunity',
          values.equalOpportunityDifference,
          'MEDIUM'
        ));
      }
    }
    
    return violations;
  }
}

class AIAuditSystem:
    def __init__(self, blockchain_client=None):
        self.audit_db = AuditDatabase()
        self.blockchain = blockchain_client  # Optional: immutable audit trail
        
    def log_prediction(self, prediction_event: PredictionEvent) -> str:
        """Log every AI prediction with full context."""
        
        audit_record = {
            'event_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow().isoformat(),
            'model_version': prediction_event.model_version,
            'input_data': self.sanitize_input(prediction_event.input_data),
            'prediction': prediction_event.prediction,
            'confidence': prediction_event.confidence,
            'feature_importance': prediction_event.feature_importance,
            'user_id': prediction_event.user_id,
            'session_id': prediction_event.session_id,
            'model_metadata': {
                'training_date': prediction_event.model_metadata.training_date,
                'training_data_hash': prediction_event.model_metadata.data_hash,
                'hyperparameters': prediction_event.model_metadata.hyperparameters,
                'validation_metrics': prediction_event.model_metadata.validation_metrics
            },
            'bias_metrics': prediction_event.bias_metrics,
            'human_review_required': prediction_event.human_review_required,
            'explanation': prediction_event.explanation
        }
        
        # Store in traditional database
        record_id = self.audit_db.insert_record(audit_record)
        
        # Optional: Store hash on blockchain for immutability
        if self.blockchain:
            record_hash = self.calculate_record_hash(audit_record)
            self.blockchain.store_hash(record_id, record_hash)
        
        return record_id
    
    def generate_audit_report(self, start_date: datetime, end_date: datetime) -> AuditReport:
        """Generate comprehensive audit report for a time period."""
        
        records = self.audit_db.get_records_in_range(start_date, end_date)
        
        return AuditReport(
            total_predictions=len(records),
            model_versions_used=self.analyze_model_versions(records),
            bias_incidents=self.analyze_bias_incidents(records),
            human_review_rate=self.calculate_human_review_rate(records),
            accuracy_trends=self.analyze_accuracy_trends(records),
            fairness_metrics=self.analyze_fairness_metrics(records),
            compliance_status=self.assess_compliance_status(records),
            recommendations=self.generate_audit_recommendations(records)
        )
    
    def verify_audit_integrity(self, record_id: str) -> bool:
        """Verify that audit records haven't been tampered with."""
        
        if not self.blockchain:
            return True  # No blockchain verification available
        
        # Retrieve record from database
        record = self.audit_db.get_record(record_id)
        current_hash = self.calculate_record_hash(record)
        
        # Compare with blockchain stored hash
        stored_hash = self.blockchain.get_hash(record_id)
        
        return current_hash == stored_hash
 
# Example usage for regulatory compliance
audit_system = AIAuditSystem(blockchain_client=BlockchainClient())
 
# Log every prediction
for prediction in daily_predictions:
    audit_id = audit_system.log_prediction(prediction)
    
# Generate monthly compliance report
monthly_report = audit_system.generate_audit_report(
    start_date=datetime(2024, 1, 1),
    end_date=datetime(2024, 1, 31)
)
 
print(f"Total predictions audited: {monthly_report.total_predictions}")
print(f"Bias incidents detected: {len(monthly_report.bias_incidents)}")
print(f"Human review rate: {monthly_report.human_review_rate:.2%}")

interface ExplainableAI {
  generateExplanation(prediction: Prediction, input: InputData): Promise<Explanation>;
  validateExplanation(explanation: Explanation): Promise<ValidationResult>;
  customizeExplanationForAudience(explanation: Explanation, audience: AudienceType): Promise<Explanation>;
}
 
class EnterpriseExplainableAI implements ExplainableAI {
  constructor(
    private readonly shapeExplainer: SHAPExplainer,
    private readonly limeExplainer: LIMEExplainer,
    private readonly naturalLanguageGenerator: NLGenerator
  ) {}
  
  async generateExplanation(prediction: Prediction, input: InputData): Promise<Explanation> {
    // Generate multiple types of explanations
    const explanations = await Promise.all([
      this.generateFeatureImportanceExplanation(prediction, input),
      this.generateCounterfactualExplanation(prediction, input),
      this.generateExampleBasedExplanation(prediction, input),
      this.generateRuleBasedExplanation(prediction, input)
    ]);
    
    // Combine explanations for comprehensive understanding
    const combinedExplanation = this.combineExplanations(explanations);
    
    return {
      prediction: prediction,
      explanationType: 'comprehensive',
      featureImportances: explanations[0].featureImportances,
      counterfactuals: explanations[1].counterfactuals,
      similarExamples: explanations[2].examples,
      rules: explanations[3].rules,
      confidence: this.calculateExplanationConfidence(explanations),
      naturalLanguageSummary: await this.generateNaturalLanguageSummary(combinedExplanation)
    };
  }
  
  private async generateFeatureImportanceExplanation(
    prediction: Prediction,
    input: InputData
  ): Promise<FeatureImportanceExplanation> {
    // Use SHAP values for global and local explanations
    const shapValues = await this.shapeExplainer.explain(prediction, input);
    
    return {
      type: 'feature_importance',
      localImportances: shapValues.local,
      globalImportances: shapValues.global,
      baselineValue: shapValues.baseline,
      topFeatures: this.extractTopFeatures(shapValues.local, 5)
    };
  }
  
  private async generateCounterfactualExplanation(
    prediction: Prediction,
    input: InputData
  ): Promise<CounterfactualExplanation> {
    // Generate counterfactual examples: "If X were Y, then prediction would be Z"
    const counterfactuals = await this.findCounterfactuals(prediction, input);
    
    return {
      type: 'counterfactual',
      counterfactals: counterfactuals.map(cf => ({
        originalFeature: cf.feature,
        originalValue: cf.originalValue,
        counterfactualValue: cf.newValue,
        resultingPrediction: cf.newPrediction,
        confidenceChange: cf.confidenceChange,
        naturalLanguage: this.generateCounterfactualText(cf)
      }))
    };
  }
  
  async customizeExplanationForAudience(
    explanation: Explanation,
    audience: AudienceType
  ): Promise<Explanation> {
    switch (audience) {
      case 'executive':
        return this.createExecutiveSummary(explanation);
      case 'technical':
        return this.createTechnicalExplanation(explanation);
      case 'enduser':
        return this.createUserFriendlyExplanation(explanation);
      case 'regulator':
        return this.createComplianceExplanation(explanation);
      default:
        return explanation;
    }
  }
  
  private createUserFriendlyExplanation(explanation: Explanation): Explanation {
    return {
      ...explanation,
      naturalLanguageSummary: this.simplifyLanguage(explanation.naturalLanguageSummary),
      visualizations: this.createSimpleVisualizations(explanation),
      keyInsights: this.extractKeyInsights(explanation, maxInsights: 3),
      actionableRecommendations: this.generateActionableRecommendations(explanation)
    };
  }
}

class AITransparencyDashboard {
  async createDashboard(): Promise<TransparencyDashboardData> {
    return {
      modelPerformance: await this.getModelPerformanceMetrics(),
      biasMetrics: await this.getBiasMetrics(),
      humanReviewStats: await this.getHumanReviewStatistics(),
      explainabilityMetrics: await this.getExplainabilityMetrics(),
      complianceStatus: await this.getComplianceStatus(),
      userFeedback: await this.getUserFeedbackSummary(),
      ethicsCommitteeReports: await this.getEthicsCommitteeReports()
    };
  }
  
  private async getModelPerformanceMetrics(): Promise<ModelPerformanceMetrics> {
    return {
      accuracy: this.calculateOverallAccuracy(),
      precisionByGroup: await this.calculatePrecisionByProtectedGroup(),
      recallByGroup: await this.calculateRecallByProtectedGroup(),
      f1ScoreByGroup: await this.calculateF1ScoreByProtectedGroup(),
      calibrationMetrics: await this.calculateCalibrationMetrics(),
      performanceTrends: await this.getPerformanceTrends(30), // Last 30 days
    };
  }
  
  private async getBiasMetrics(): Promise<BiasMetrics> {
    return {
      statisticalParityByAttribute: await this.calculateStatisticalParity(),
      equalOpportunityByAttribute: await this.calculateEqualOpportunity(),
      calibrationByAttribute: await this.calculateCalibrationByGroup(),
      biasViolationHistory: await this.getBiasViolationHistory(),
      mitigationEffectiveness: await this.assessMitigationEffectiveness()
    };
  }
}

class AIEthicsGovernance:
    def __init__(self):
        self.ethics_committee = EthicsCommittee()
        self.policy_engine = PolicyEngine()
        self.training_system = EthicsTrainingSystem()
        
    def establish_ethics_committee(self) -> EthicsCommittee:
        """Establish diverse AI ethics committee with clear responsibilities."""
        
        committee_members = [
            CommitteeMember(
                role='Chair',
                name='Chief Ethics Officer',
                expertise=['AI Ethics', 'Corporate Governance'],
                responsibilities=['Committee leadership', 'Final decision authority']
            ),
            CommitteeMember(
                role='Technical Lead',
                name='Senior AI Engineer',
                expertise=['Machine Learning', 'Bias Detection'],
                responsibilities=['Technical review', 'Implementation guidance']
            ),
            CommitteeMember(
                role='Legal Counsel',
                name='AI/Data Privacy Attorney',
                expertise=['AI Regulation', 'Privacy Law'],
                responsibilities=['Legal compliance', 'Risk assessment']
            ),
            CommitteeMember(
                role='Domain Expert',
                name='Subject Matter Expert',
                expertise=['Industry Knowledge', 'User Experience'],
                responsibilities=['Domain context', 'User impact assessment']
            ),
            CommitteeMember(
                role='External Advisor',
                name='Ethics Professor/Consultant',
                expertise=['Applied Ethics', 'AI Philosophy'],
                responsibilities=['Independent perspective', 'Ethical framework guidance']
            )
        ]
        
        return EthicsCommittee(
            members=committee_members,
            meeting_frequency='monthly',
            decision_threshold=0.75,  # 75% agreement required
            responsibilities=[
                'Review high-risk AI applications',
                'Approve ethics policies and procedures',
                'Investigate ethics violations',
                'Provide ethics training oversight',
                'Annual ethics audit review'
            ]
        )
    
    def create_ethics_policies(self) -> List[EthicsPolicy]:
        """Create comprehensive AI ethics policies."""
        
        return [
            EthicsPolicy(
                name='Bias Prevention and Mitigation',
                scope='All AI systems',
                requirements=[
                    'Pre-deployment bias testing required',
                    'Continuous bias monitoring mandatory',
                    'Bias mitigation plan required for violations',
                    'Regular bias training for AI teams'
                ],
                enforcement_mechanism='Automated monitoring + Committee review'
            ),
            EthicsPolicy(
                name='Human Oversight and Control',
                scope='High-impact AI decisions',
                requirements=[
                    'Human review required for decisions above threshold',
                    'Users must be able to request human review',
                    'Clear escalation procedures',
                    'Human override capability maintained'
                ],
                enforcement_mechanism='Technical controls + Process audits'
            ),
            EthicsPolicy(
                name='Transparency and Explainability',
                scope='All customer-facing AI',
                requirements=[
                    'AI usage must be disclosed to users',
                    'Explanations provided for significant decisions',
                    'Model documentation maintained',
                    'Regular transparency reports published'
                ],
                enforcement_mechanism='Technical implementation + Compliance reviews'
            ),
            EthicsPolicy(
                name='Data Privacy and Security',
                scope='All AI systems processing personal data',
                requirements=[
                    'Privacy by design implementation',
                    'Data minimization principles followed',
                    'Consent mechanisms for data use',
                    'Secure data handling procedures'
                ],
                enforcement_mechanism='Privacy audits + Security assessments'
            )
        ]
    
    def implement_continuous_improvement(self) -> ContinuousImprovementFramework:
        """Implement continuous improvement for AI ethics."""
        
        return ContinuousImprovementFramework(
            monitoring_activities=[
                MonitoringActivity(
                    name='Bias Drift Detection',
                    frequency='daily',
                    automated=True,
                    action_threshold=0.05
                ),
                MonitoringActivity(
                    name='User Feedback Analysis',
                    frequency='weekly',
                    automated=False,
                    responsible_team='Product Ethics Team'
                ),
                MonitoringActivity(
                    name='Regulatory Compliance Check',
                    frequency='monthly',
                    automated=True,
                    escalation_required=True
                )
            ],
            improvement_processes=[
                ImprovementProcess(
                    name='Ethics Training Updates',
                    trigger='New regulation or incident',
                    process_owner='Ethics Committee',
                    timeline='30 days'
                ),
                ImprovementProcess(
                    name='Policy Review and Update',
                    trigger='Annual or incident-based',
                    process_owner='Ethics Committee',
                    timeline='60 days'
                ),
                ImprovementProcess(
                    name='Technology Enhancement',
                    trigger='New bias detection methods',
                    process_owner='Technical Team',
                    timeline='90 days'
                )
            ]
        )
 
# Implementation example
governance = AIEthicsGovernance()
ethics_committee = governance.establish_ethics_committee()
policies = governance.create_ethics_policies()
improvement_framework = governance.implement_continuous_improvement()
 
print(f"Ethics committee established with {len(ethics_committee.members)} members")
print(f"Created {len(policies)} ethics policies")
print(f"Continuous improvement framework with {len(improvement_framework.monitoring_activities)} monitoring activities")

class EthicalLoanApprovalSystem:
    def __init__(self):
        self.bias_detector = BiasDetectionFramework(
            protected_attributes=['gender', 'race', 'age', 'zip_code']
        )
        self.explainer = LoanDecisionExplainer()
        self.human_reviewer = HumanReviewSystem()
        self.audit_system = AIAuditSystem()
        
    async def process_loan_application(self, application: LoanApplication) -> LoanDecision:
        """Process loan application with full ethical AI framework."""
        
        # Step 1: Generate AI prediction
        ai_prediction = await self.ml_model.predict(application)
        
        # Step 2: Check for bias indicators
        bias_assessment = await self.bias_detector.assess_individual_prediction(
            prediction=ai_prediction,
            application=application
        )
        
        # Step 3: Generate explanation
        explanation = await self.explainer.explain_decision(
            prediction=ai_prediction,
            application=application
        )
        
        # Step 4: Determine if human review is needed
        requires_human_review = (
            ai_prediction.confidence < 0.8 or
            bias_assessment.risk_level == 'HIGH' or
            application.amount > 100000  # High-value loans
        )
        
        if requires_human_review:
            # Human reviewer gets AI recommendation + explanation + bias assessment
            final_decision = await self.human_reviewer.review_application(
                application=application,
                ai_prediction=ai_prediction,
                explanation=explanation,
                bias_assessment=bias_assessment
            )
        else:
            final_decision = ai_prediction
        
        # Step 5: Log everything for audit
        await self.audit_system.log_loan_decision({
            'application_id': application.id,
            'ai_prediction': ai_prediction,
            'final_decision': final_decision,
            'explanation': explanation,
            'bias_assessment': bias_assessment,
            'human_reviewed': requires_human_review,
            'timestamp': datetime.utcnow()
        })
        
        return LoanDecision(
            approved=final_decision.approved,
            explanation=explanation,
            appeal_process=self.create_appeal_process(application.id),
            human_reviewed=requires_human_review
        )
    
    def create_appeal_process(self, application_id: str) -> AppealProcess:
        """Create transparent appeal process for denied applications."""
        
        return AppealProcess(
            application_id=application_id,
            appeal_deadline=datetime.utcnow() + timedelta(days=30),
            required_documents=['Additional income proof', 'Credit report'],
            human_review_guaranteed=True,
            expected_response_time=timedelta(days=7)
        )
 
# Results after 6 months
results = {
    'bias_reduction': {
        'gender_bias': 'Reduced from 12% to 2% difference',
        'racial_bias': 'Reduced from 18% to 3% difference',
        'age_bias': 'Reduced from 8% to 1% difference'
    },
    'transparency_metrics': {
        'explanation_satisfaction': '4.2/5.0',
        'appeal_rate': '3.2%',
        'appeal_success_rate': '18%'
    },
    'compliance_metrics': {
        'regulatory_violations': 0,
        'audit_score': '96/100',
        'customer_complaints': 'Reduced 45%'
    },
    'business_metrics': {
        'approval_rate': 'Increased 3%',  # Better decisions, not just fewer denials
        'default_rate': 'Reduced 8%',     # More accurate risk assessment
        'processing_time': 'Reduced 23%'  # Automation efficiency
    }
}

#!/bin/bash
# Week 1: Assessment and Planning
 
# Conduct ethical AI readiness assessment
assess_current_ai_systems() {
    echo "Auditing existing AI systems..."
    
    # Inventory all AI/ML systems
    find_ai_systems
    
    # Assess risk levels
    assess_risk_levels
    
    # Identify compliance gaps
    identify_compliance_gaps
    
    # Prioritize implementation order
    create_implementation_priority
}
 
# Week 2: Team Formation and Training
establish_ethics_team() {
    echo "Establishing AI Ethics team..."
    
    # Form ethics committee
    create_ethics_committee
    
    # Provide ethics training
    conduct_ethics_training
    
    # Establish governance processes
    create_governance_processes
}
 
# Week 3: Policy Development
develop_ethics_policies() {
    echo "Developing AI ethics policies..."
    
    # Create bias prevention policy
    create_bias_policy
    
    # Create transparency policy
    create_transparency_policy
    
    # Create human oversight policy
    create_oversight_policy
    
    # Get legal review and approval
    legal_review_policies
}
 
# Week 4: Technical Foundation
setup_technical_infrastructure() {
    echo "Setting up technical infrastructure..."
    
    # Deploy bias detection tools
    deploy_bias_detection
    
    # Implement audit logging
    implement_audit_system
    
    # Set up monitoring dashboards
    create_monitoring_dashboards
    
    # Establish alert systems
    setup_alert_systems
}

class EthicalAIImplementationPlan:
    def __init__(self):
        self.implementation_phases = [
            ImplementationPhase(
                name='Pilot Implementation',
                duration_weeks=2,
                scope='Low-risk AI system',
                activities=[
                    'Deploy bias detection',
                    'Implement explainability',
                    'Set up human review process',
                    'Test audit trail'
                ],
                success_criteria=[
                    'Bias metrics below threshold',
                    'Explanations generated for all decisions',
                    'Human review process functional',
                    'Complete audit trail captured'
                ]
            ),
            ImplementationPhase(
                name='Medium-Risk Systems',
                duration_weeks=3,
                scope='Customer-facing systems',
                activities=[
                    'Scale bias detection',
                    'Implement customer explanations',
                    'Deploy transparency dashboard',
                    'Train customer service team'
                ],
                success_criteria=[
                    'Customer satisfaction maintained',
                    'Transparency metrics met',
                    'No compliance violations',
                    'Team training completed'
                ]
            ),
            ImplementationPhase(
                name='High-Risk Systems',
                duration_weeks=3,
                scope='Critical business decisions',
                activities=[
                    'Implement enhanced human oversight',
                    'Deploy advanced bias mitigation',
                    'Set up regulatory reporting',
                    'Conduct stress testing'
                ],
                success_criteria=[
                    'Enhanced oversight functional',
                    'Bias mitigation effective',
                    'Regulatory approval obtained',
                    'Stress tests passed'
                ]
            )
        ]
    
    def execute_implementation(self) -> ImplementationResult:
        results = []
        
        for phase in self.implementation_phases:
            phase_result = self.execute_phase(phase)
            results.append(phase_result)
            
            # Gate check before next phase
            if not phase_result.success_criteria_met:
                return ImplementationResult(
                    success=False,
                    failed_phase=phase.name,
                    results=results
                )
        
        return ImplementationResult(
            success=True,
            results=results,
            final_metrics=self.calculate_final_metrics()
        )

class EthicalAIScalingPlan {
  async scaleEthicalAI(): Promise<ScalingResult> {
    // Scale across all AI systems
    const scalingTasks = [
      this.scaleAcrossAllSystems(),
      this.implementAdvancedMonitoring(),
      this.enhanceHumanMachineCollaboration(),
      this.developInternalCapabilities(),
      this.establishIndustryPartnerships()
    ];
    
    const results = await Promise.all(scalingTasks);
    
    return new ScalingResult(results);
  }
  
  private async scaleAcrossAllSystems(): Promise<SystemScalingResult> {
    const allSystems = await this.inventoryAllAISystems();
    
    for (const system of allSystems) {
      await this.implementEthicalFramework(system);
      await this.validateEthicalCompliance(system);
      await this.setupContinuousMonitoring(system);
    }
    
    return new SystemScalingResult(allSystems.length);
  }
  
  private async implementAdvancedMonitoring(): Promise<MonitoringResult> {
    // Implement real-time bias detection
    await this.deployRealtimeBiasDetection();
    
    // Set up predictive ethics alerts
    await this.setupPredictiveEthicsAlerts();
    
    // Implement cross-system ethics analytics
    await this.deployCrossSystemAnalytics();
    
    return new MonitoringResult('advanced_monitoring_deployed');
  }
}

interface EthicalAIKPIs {
  // Bias and Fairness Metrics
  biasViolationRate: number;          // Violations per 1000 predictions
  demographicParityScore: number;      // 0-1 scale, higher is better
  equalOpportunityScore: number;       // 0-1 scale, higher is better
  
  // Transparency Metrics  
  explanationSatisfactionScore: number; // User satisfaction with explanations
  transparencyComplianceRate: number;   // % of decisions with explanations
  
  // Human Oversight Metrics
  humanReviewRate: number;             // % of decisions reviewed by humans
  humanOverrideRate: number;           // % of AI decisions overridden
  averageReviewTime: number;           // Time for human review (minutes)
  
  // Compliance Metrics
  regulatoryViolationCount: number;    // Number of regulatory violations
  auditScore: number;                  // External audit score (0-100)
  policyComplianceRate: number;        // % compliance with internal policies
  
  // Business Impact Metrics
  customerTrustScore: number;          // Customer trust in AI decisions
  ethicsRelatedComplaints: number;     // Complaints related to AI ethics
  reputationScore: number;             // Brand reputation score
  
  // Operational Metrics
  ethicsTrainingCompletionRate: number; // % employees completed ethics training
  ethicsCommitteeEngagement: number;    // Committee meeting attendance rate
  timeToResolution: number;            // Time to resolve ethics issues (days)
}
 
class EthicalAIKPITracker {
  async calculateMonthlyKPIs(): Promise<EthicalAIKPIs> {
    const [
      biasMetrics,
      transparencyMetrics,
      oversightMetrics,
      complianceMetrics,
      businessMetrics,
      operationalMetrics
    ] = await Promise.all([
      this.calculateBiasMetrics(),
      this.calculateTransparencyMetrics(),
      this.calculateOversightMetrics(),
      this.calculateComplianceMetrics(),
      this.calculateBusinessMetrics(),
      this.calculateOperationalMetrics()
    ]);
    
    return {
      ...biasMetrics,
      ...transparencyMetrics,
      ...oversightMetrics,
      ...complianceMetrics,
      ...businessMetrics,
      ...operationalMetrics
    };
  }
  
  generateEthicsReport(kpis: EthicalAIKPIs): EthicsReport {
    return {
      executiveSummary: this.generateExecutiveSummary(kpis),
      detailedAnalysis: this.generateDetailedAnalysis(kpis),
      trendsAndInsights: this.analyzeTrends(kpis),
      recommendations: this.generateRecommendations(kpis),
      complianceStatus: this.assessComplianceStatus(kpis),
      nextSteps: this.defineNextSteps(kpis)
    };
  }
}

# Wrong approach
def build_ai_system():
    model = train_model(data)
    deploy_model(model)
    # TODO: Add ethics later
    
# Right approach  
def build_ethical_ai_system():
    # Ethics considerations from the start
    ethical_requirements = define_ethical_requirements()
    biased_data_removed = preprocess_data_for_fairness(data)
    model = train_fair_model(biased_data_removed, fairness_constraints)
    explanations = generate_explanations(model)
    audit_trail = setup_audit_system()
    deploy_ethical_model(model, explanations, audit_trail)

class RiskBasedEthicsFramework {
  determineEthicsRequirements(aiSystem: AISystem): EthicsRequirements {
    const riskLevel = this.assessRiskLevel(aiSystem);
    
    switch (riskLevel) {
      case 'LOW':
        return new LowRiskEthicsRequirements({
          biasMonitoring: 'monthly',
          humanOversight: 'exception-based',
          explainability: 'basic'
        });
        
      case 'MEDIUM':
        return new MediumRiskEthicsRequirements({
          biasMonitoring: 'daily',
          humanOversight: 'sample-based',
          explainability: 'detailed',
          auditTrail: 'comprehensive'
        });
        
      case 'HIGH':
        return new HighRiskEthicsRequirements({
          biasMonitoring: 'real-time',
          humanOversight: 'mandatory',
          explainability: 'comprehensive',
          auditTrail: 'immutable',
          regulatoryReporting: 'required'
        });
    }
  }
}

class ContinuousEthicsImprovement:
    def __init__(self):
        self.improvement_cycles = [
            ImprovementCycle(
                name='Bias Reduction',
                frequency='quarterly',
                target_improvement=0.05,  # 5% improvement per quarter
                measurement='demographic_parity_difference'
            ),
            ImprovementCycle(
                name='Explanation Quality',
                frequency='monthly',
                target_improvement=0.1,   # 10% improvement per month
                measurement='user_satisfaction_score'
            ),
            ImprovementCycle(
                name='Process Efficiency',
                frequency='bi-annual',
                target_improvement=0.15,  # 15% improvement bi-annually
                measurement='time_to_ethics_review'
            )
        ]
    
    def execute_improvement_cycle(self, cycle: ImprovementCycle):
        current_performance = self.measure_current_performance(cycle.measurement)
        improvement_plan = self.create_improvement_plan(cycle, current_performance)
        
        # Implement improvements
        for improvement in improvement_plan.improvements:
            self.implement_improvement(improvement)
        
        # Measure results
        new_performance = self.measure_current_performance(cycle.measurement)
        
        # Document learnings
        self.document_learnings(cycle, current_performance, new_performance)
        
        return ImprovementResult(
            cycle=cycle,
            baseline=current_performance,
            result=new_performance,
            improvement_achieved=new_performance - current_performance
        )

interface FutureEthicalAI {
  // Emerging capabilities
  selfMonitoringEthics(): Promise<EthicsAssessment>;
  communityStakeholderInput(): Promise<StakeholderFeedback>;
  globalComplianceCheck(): Promise<ComplianceStatus>;
  ethicalAIMarketplace(): Promise<EthicalComponents>;
}
 
class NextGenerationEthicalAI implements FutureEthicalAI {
  async selfMonitoringEthics(): Promise<EthicsAssessment> {
    // AI system monitors its own ethical performance
    const selfAssessment = await this.performSelfEthicsAudit();
    
    if (selfAssessment.requiresHumanReview) {
      await this.escalateToHumans(selfAssessment);
    }
    
    return selfAssessment;
  }
  
  async communityStakeholderInput(): Promise<StakeholderFeedback> {
    // Integrate community feedback into AI development
    return await this.collectStakeholderFeedback([
      'affected_communities',
      'domain_experts',
      'advocacy_groups',
      'regulatory_bodies'
    ]);
  }
}