Ethical AI Governance Platforms

import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass
from enum import Enum
import json
import time
from sklearn.metrics import confusion_matrix
from scipy import stats
import warnings

class FairnessMetric(Enum):
    DEMOGRAPHIC_PARITY = "demographic_parity"
    EQUALIZED_ODDS = "equalized_odds"
    EQUAL_OPPORTUNITY = "equal_opportunity"
    INDIVIDUAL_FAIRNESS = "individual_fairness"
    CALIBRATION = "calibration"
    COUNTERFACTUAL_FAIRNESS = "counterfactual_fairness"

class ProtectedAttribute(Enum):
    RACE = "race"
    GENDER = "gender"
    AGE = "age"
    RELIGION = "religion"
    SEXUAL_ORIENTATION = "sexual_orientation"
    DISABILITY = "disability"
    SOCIOECONOMIC_STATUS = "socioeconomic_status"

@dataclass
class FairnessAssessment:
    metric: FairnessMetric
    protected_attribute: ProtectedAttribute
    value: float
    threshold: float
    is_fair: bool
    group_metrics: Dict[str, float]
    confidence_interval: Tuple[float, float]
    timestamp: float

@dataclass
class BiasIncident:
    incident_id: str
    model_id: str
    bias_type: str
    severity: str  # 'low', 'medium', 'high', 'critical'
    affected_groups: List[str]
    detection_timestamp: float
    description: str
    recommended_actions: List[str]
    status: str  # 'detected', 'investigating', 'mitigating', 'resolved'

class FairnessMonitoringSystem:
    def __init__(self, fairness_thresholds: Dict[FairnessMetric, float] = None):
        self.fairness_thresholds = fairness_thresholds or {
            FairnessMetric.DEMOGRAPHIC_PARITY: 0.8,
            FairnessMetric.EQUALIZED_ODDS: 0.8,
            FairnessMetric.EQUAL_OPPORTUNITY: 0.8,
            FairnessMetric.INDIVIDUAL_FAIRNESS: 0.1,
            FairnessMetric.CALIBRATION: 0.05
        }
        self.bias_incidents: List[BiasIncident] = []
        self.assessment_history: List[FairnessAssessment] = []
        
    def assess_demographic_parity(self, 
                                predictions: np.ndarray,
                                protected_attributes: Dict[ProtectedAttribute, np.ndarray],
                                positive_class: int = 1) -> List[FairnessAssessment]:
        """Assess demographic parity across protected groups"""
        assessments = []
        
        for attr, groups in protected_attributes.items():
            unique_groups = np.unique(groups)
            group_rates = {}
            
            # Calculate positive prediction rate for each group
            for group in unique_groups:
                group_mask = groups == group
                group_predictions = predictions[group_mask]
                positive_rate = np.mean(group_predictions == positive_class)
                group_rates[str(group)] = positive_rate
            
            # Calculate ratio between min and max rates
            rates = list(group_rates.values())
            if len(rates) > 1:
                min_rate, max_rate = min(rates), max(rates)
                parity_ratio = min_rate / max_rate if max_rate > 0 else 0
                
                # Calculate confidence interval using bootstrap
                ci = self._bootstrap_confidence_interval(
                    predictions, groups, unique_groups, positive_class
                )
                
                assessment = FairnessAssessment(
                    metric=FairnessMetric.DEMOGRAPHIC_PARITY,
                    protected_attribute=attr,
                    value=parity_ratio,
                    threshold=self.fairness_thresholds[FairnessMetric.DEMOGRAPHIC_PARITY],
                    is_fair=parity_ratio >= self.fairness_thresholds[FairnessMetric.DEMOGRAPHIC_PARITY],
                    group_metrics=group_rates,
                    confidence_interval=ci,
                    timestamp=time.time()
                )
                
                assessments.append(assessment)
                self.assessment_history.append(assessment)
                
                # Check for bias incident
                if not assessment.is_fair:
                    self._create_bias_incident(assessment, unique_groups)
        
        return assessments
    
    def assess_equalized_odds(self,
                            predictions: np.ndarray,
                            true_labels: np.ndarray,
                            protected_attributes: Dict[ProtectedAttribute, np.ndarray],
                            positive_class: int = 1) -> List[FairnessAssessment]:
        """Assess equalized odds (equal TPR and FPR across groups)"""
        assessments = []
        
        for attr, groups in protected_attributes.items():
            unique_groups = np.unique(groups)
            group_metrics = {}
            
            tpr_values, fpr_values = [], []
            
            for group in unique_groups:
                group_mask = groups == group
                group_preds = predictions[group_mask]
                group_labels = true_labels[group_mask]
                
                if len(group_preds) > 0:
                    # Calculate TPR and FPR
                    tn, fp, fn, tp = confusion_matrix(
                        group_labels, group_preds, 
                        labels=[1-positive_class, positive_class]
                    ).ravel()
                    
                    tpr = tp / (tp + fn) if (tp + fn) > 0 else 0
                    fpr = fp / (fp + tn) if (fp + tn) > 0 else 0
                    
                    tpr_values.append(tpr)
                    fpr_values.append(fpr)
                    
                    group_metrics[f"{group}_tpr"] = tpr
                    group_metrics[f"{group}_fpr"] = fpr
            
            if len(tpr_values) > 1:
                # Calculate equalized odds metric (minimum ratio of TPRs and FPRs)
                tpr_ratio = min(tpr_values) / max(tpr_values) if max(tpr_values) > 0 else 0
                fpr_ratio = min(fpr_values) / max(fpr_values) if max(fpr_values) > 0 else 0
                equalized_odds_score = min(tpr_ratio, fpr_ratio)
                
                ci = self._bootstrap_equalized_odds_ci(
                    predictions, true_labels, groups, unique_groups, positive_class
                )
                
                assessment = FairnessAssessment(
                    metric=FairnessMetric.EQUALIZED_ODDS,
                    protected_attribute=attr,
                    value=equalized_odds_score,
                    threshold=self.fairness_thresholds[FairnessMetric.EQUALIZED_ODDS],
                    is_fair=equalized_odds_score >= self.fairness_thresholds[FairnessMetric.EQUALIZED_ODDS],
                    group_metrics=group_metrics,
                    confidence_interval=ci,
                    timestamp=time.time()
                )
                
                assessments.append(assessment)
                self.assessment_history.append(assessment)
                
                if not assessment.is_fair:
                    self._create_bias_incident(assessment, unique_groups)
        
        return assessments
    
    def assess_individual_fairness(self,
                                 predictions: np.ndarray,
                                 features: np.ndarray,
                                 similarity_threshold: float = 0.95) -> FairnessAssessment:
        """Assess individual fairness - similar individuals should receive similar outcomes"""
        n_samples = len(predictions)
        fairness_violations = 0
        
        # Sample pairs for efficiency in large datasets
        n_pairs = min(10000, n_samples * (n_samples - 1) // 2)
        
        for _ in range(n_pairs):
            # Random sample two individuals
            idx1, idx2 = np.random.choice(n_samples, 2, replace=False)
            
            # Calculate feature similarity (using cosine similarity)
            similarity = self._cosine_similarity(features[idx1], features[idx2])
            
            if similarity >= similarity_threshold:
                # Similar individuals - check if outcomes are similar
                pred_diff = abs(predictions[idx1] - predictions[idx2])
                
                # For classification, require same prediction; for regression, small difference
                if len(np.unique(predictions)) <= 10:  # Classification
                    if pred_diff > 0:
                        fairness_violations += 1
                else:  # Regression
                    if pred_diff > 0.1 * np.std(predictions):  # 10% of standard deviation
                        fairness_violations += 1
        
        violation_rate = fairness_violations / n_pairs
        fairness_score = 1 - violation_rate  # Higher score means more fair
        
        assessment = FairnessAssessment(
            metric=FairnessMetric.INDIVIDUAL_FAIRNESS,
            protected_attribute=None,  # Individual fairness doesn't use protected attributes
            value=fairness_score,
            threshold=1 - self.fairness_thresholds[FairnessMetric.INDIVIDUAL_FAIRNESS],
            is_fair=fairness_score >= (1 - self.fairness_thresholds[FairnessMetric.INDIVIDUAL_FAIRNESS]),
            group_metrics={'violation_rate': violation_rate, 'n_pairs_tested': n_pairs},
            confidence_interval=self._bootstrap_individual_fairness_ci(
                predictions, features, similarity_threshold
            ),
            timestamp=time.time()
        )
        
        self.assessment_history.append(assessment)
        
        if not assessment.is_fair:
            incident = BiasIncident(
                incident_id=f"individual_fairness_{int(time.time())}",
                model_id="unknown",
                bias_type="individual_fairness_violation",
                severity="medium" if violation_rate < 0.1 else "high",
                affected_groups=["similar_individuals"],
                detection_timestamp=time.time(),
                description=f"Individual fairness violation rate: {violation_rate:.3f}",
                recommended_actions=[
                    "Review feature engineering for fairness-relevant attributes",
                    "Apply individual fairness constraints during training",
                    "Implement post-processing fairness adjustments"
                ],
                status="detected"
            )
            self.bias_incidents.append(incident)
        
        return assessment
    
    def detect_intersectional_bias(self,
                                 predictions: np.ndarray,
                                 protected_attributes: Dict[ProtectedAttribute, np.ndarray],
                                 true_labels: Optional[np.ndarray] = None,
                                 positive_class: int = 1) -> List[BiasIncident]:
        """Detect bias at intersections of protected attributes"""
        incidents = []
        
        # Generate intersectional groups
        attr_names = list(protected_attributes.keys())
        
        if len(attr_names) >= 2:
            # Check pairwise intersections
            for i in range(len(attr_names)):
                for j in range(i + 1, len(attr_names)):
                    attr1, attr2 = attr_names[i], attr_names[j]
                    
                    # Create intersectional groups
                    intersectional_groups = {}
                    unique_vals1 = np.unique(protected_attributes[attr1])
                    unique_vals2 = np.unique(protected_attributes[attr2])
                    
                    for val1 in unique_vals1:
                        for val2 in unique_vals2:
                            mask = (protected_attributes[attr1] == val1) & (protected_attributes[attr2] == val2)
                            if np.sum(mask) > 0:  # Group has members
                                group_name = f"{attr1.value}:{val1}_{attr2.value}:{val2}"
                                intersectional_groups[group_name] = mask
                    
                    # Assess fairness across intersectional groups
                    if len(intersectional_groups) > 1:
                        bias_detected = self._assess_intersectional_fairness(
                            predictions, intersectional_groups, true_labels, positive_class
                        )
                        
                        if bias_detected['has_bias']:
                            incident = BiasIncident(
                                incident_id=f"intersectional_{attr1.value}_{attr2.value}_{int(time.time())}",
                                model_id="unknown",
                                bias_type="intersectional_bias",
                                severity=bias_detected['severity'],
                                affected_groups=bias_detected['affected_groups'],
                                detection_timestamp=time.time(),
                                description=f"Intersectional bias detected between {attr1.value} and {attr2.value}",
                                recommended_actions=[
                                    "Implement intersectional fairness constraints",
                                    "Collect more data for underrepresented intersectional groups",
                                    "Apply group-aware post-processing techniques"
                                ],
                                status="detected"
                            )
                            incidents.append(incident)
                            self.bias_incidents.append(incident)
        
        return incidents
    
    def generate_fairness_report(self, model_id: str) -> Dict[str, Any]:
        """Generate comprehensive fairness report"""
        recent_assessments = [
            a for a in self.assessment_history 
            if time.time() - a.timestamp < 86400  # Last 24 hours
        ]
        
        recent_incidents = [
            i for i in self.bias_incidents
            if time.time() - i.detection_timestamp < 86400
        ]
        
        # Calculate overall fairness score
        if recent_assessments:
            fairness_scores = [a.value for a in recent_assessments if a.is_fair is not None]
            overall_fairness = np.mean(fairness_scores) if fairness_scores else 0
        else:
            overall_fairness = 0
        
        # Group assessments by metric
        metrics_summary = {}
        for assessment in recent_assessments:
            metric_name = assessment.metric.value
            if metric_name not in metrics_summary:
                metrics_summary[metric_name] = []
            metrics_summary[metric_name].append({
                'protected_attribute': assessment.protected_attribute.value if assessment.protected_attribute else None,
                'value': assessment.value,
                'is_fair': assessment.is_fair,
                'group_metrics': assessment.group_metrics
            })
        
        report = {
            'model_id': model_id,
            'report_timestamp': time.time(),
            'overall_fairness_score': overall_fairness,
            'fairness_status': 'FAIR' if overall_fairness >= 0.8 else 'NEEDS_ATTENTION',
            'metrics_summary': metrics_summary,
            'recent_incidents': [
                {
                    'incident_id': i.incident_id,
                    'bias_type': i.bias_type,
                    'severity': i.severity,
                    'affected_groups': i.affected_groups,
                    'status': i.status
                } for i in recent_incidents
            ],
            'recommendations': self._generate_recommendations(recent_assessments, recent_incidents)
        }
        
        return report
    
    def _create_bias_incident(self, assessment: FairnessAssessment, affected_groups: np.ndarray):
        """Create bias incident based on fairness assessment"""
        severity = "critical" if assessment.value < 0.5 else "high" if assessment.value < 0.7 else "medium"
        
        incident = BiasIncident(
            incident_id=f"{assessment.metric.value}_{assessment.protected_attribute.value}_{int(time.time())}",
            model_id="unknown",
            bias_type=assessment.metric.value,
            severity=severity,
            affected_groups=[str(group) for group in affected_groups],
            detection_timestamp=time.time(),
            description=f"{assessment.metric.value} violation for {assessment.protected_attribute.value}: {assessment.value:.3f}",
            recommended_actions=self._get_metric_specific_actions(assessment.metric),
            status="detected"
        )
        
        self.bias_incidents.append(incident)
    
    def _get_metric_specific_actions(self, metric: FairnessMetric) -> List[str]:
        """Get recommended actions for specific fairness metric violations"""
        actions_map = {
            FairnessMetric.DEMOGRAPHIC_PARITY: [
                "Apply demographic parity post-processing",
                "Re-balance training data across protected groups",
                "Use fairness-aware feature selection"
            ],
            FairnessMetric.EQUALIZED_ODDS: [
                "Implement equalized odds constraints in training",
                "Apply threshold optimization per group",
                "Use adversarial debiasing techniques"
            ],
            FairnessMetric.EQUAL_OPPORTUNITY: [
                "Focus on equal TPR across groups",
                "Apply group-specific threshold tuning",
                "Implement cost-sensitive learning"
            ],
            FairnessMetric.INDIVIDUAL_FAIRNESS: [
                "Review similarity metrics and thresholds",
                "Apply Lipschitz constraints during training",
                "Implement individual fairness regularization"
            ]
        }
        
        return actions_map.get(metric, ["Review model for bias", "Consult fairness experts"])
    
    def _bootstrap_confidence_interval(self, predictions: np.ndarray, groups: np.ndarray,
                                     unique_groups: np.ndarray, positive_class: int,
                                     n_bootstrap: int = 1000, alpha: float = 0.05) -> Tuple[float, float]:
        """Calculate confidence interval using bootstrap"""
        bootstrap_ratios = []
        
        for _ in range(n_bootstrap):
            # Bootstrap sample
            indices = np.random.choice(len(predictions), len(predictions), replace=True)
            boot_preds = predictions[indices]
            boot_groups = groups[indices]
            
            # Calculate ratio for bootstrap sample
            group_rates = []
            for group in unique_groups:
                group_mask = boot_groups == group
                if np.sum(group_mask) > 0:
                    rate = np.mean(boot_preds[group_mask] == positive_class)
                    group_rates.append(rate)
            
            if len(group_rates) > 1:
                min_rate, max_rate = min(group_rates), max(group_rates)
                ratio = min_rate / max_rate if max_rate > 0 else 0
                bootstrap_ratios.append(ratio)
        
        if bootstrap_ratios:
            return (np.percentile(bootstrap_ratios, 100 * alpha / 2),
                   np.percentile(bootstrap_ratios, 100 * (1 - alpha / 2)))
        else:
            return (0.0, 1.0)
    
    def _cosine_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> float:
        """Calculate cosine similarity between two vectors"""
        dot_product = np.dot(vec1, vec2)
        norms = np.linalg.norm(vec1) * np.linalg.norm(vec2)
        return dot_product / norms if norms > 0 else 0
    
    def _assess_intersectional_fairness(self, predictions: np.ndarray,
                                      intersectional_groups: Dict[str, np.ndarray],
                                      true_labels: Optional[np.ndarray],
                                      positive_class: int) -> Dict[str, Any]:
        """Assess fairness across intersectional groups"""
        group_rates = {}
        
        for group_name, group_mask in intersectional_groups.items():
            if np.sum(group_mask) > 0:
                group_preds = predictions[group_mask]
                rate = np.mean(group_preds == positive_class)
                group_rates[group_name] = rate
        
        if len(group_rates) > 1:
            rates = list(group_rates.values())
            min_rate, max_rate = min(rates), max(rates)
            ratio = min_rate / max_rate if max_rate > 0 else 0
            
            has_bias = ratio < 0.8  # Using same threshold as demographic parity
            severity = "critical" if ratio < 0.5 else "high" if ratio < 0.7 else "medium"
            
            # Identify most affected groups
            sorted_groups = sorted(group_rates.items(), key=lambda x: x[1])
            affected_groups = [g[0] for g in sorted_groups[:2]]  # Bottom 2 groups
            
            return {
                'has_bias': has_bias,
                'ratio': ratio,
                'severity': severity,
                'affected_groups': affected_groups,
                'group_rates': group_rates
            }
        
        return {'has_bias': False, 'ratio': 1.0, 'severity': 'none', 'affected_groups': []}

# Usage example
def demonstrate_fairness_monitoring():
    # Create monitoring system
    monitor = FairnessMonitoringSystem()
    
    # Generate sample data
    n_samples = 10000
    np.random.seed(42)
    
    # Features and predictions
    features = np.random.randn(n_samples, 10)
    predictions = np.random.binomial(1, 0.6, n_samples)  # Biased predictions
    true_labels = np.random.binomial(1, 0.5, n_samples)
    
    # Protected attributes
    race = np.random.choice(['White', 'Black', 'Hispanic', 'Asian'], n_samples, p=[0.6, 0.2, 0.15, 0.05])
    gender = np.random.choice(['Male', 'Female'], n_samples, p=[0.5, 0.5])
    age = np.random.choice(['Young', 'Middle', 'Old'], n_samples, p=[0.4, 0.4, 0.2])
    
    # Introduce bias - lower positive prediction rate for certain groups
    bias_mask = (race == 'Black') | (gender == 'Female')
    predictions[bias_mask] = np.random.binomial(1, 0.4, np.sum(bias_mask))
    
    protected_attrs = {
        ProtectedAttribute.RACE: race,
        ProtectedAttribute.GENDER: gender,
        ProtectedAttribute.AGE: age
    }
    
    # Run fairness assessments
    print("Running fairness assessments...")
    
    # Demographic parity
    dp_assessments = monitor.assess_demographic_parity(predictions, protected_attrs)
    for assessment in dp_assessments:
        print(f"Demographic Parity ({assessment.protected_attribute.value}): {assessment.value:.3f} - {'FAIR' if assessment.is_fair else 'UNFAIR'}")
    
    # Equalized odds
    eo_assessments = monitor.assess_equalized_odds(predictions, true_labels, protected_attrs)
    for assessment in eo_assessments:
        print(f"Equalized Odds ({assessment.protected_attribute.value}): {assessment.value:.3f} - {'FAIR' if assessment.is_fair else 'UNFAIR'}")
    
    # Individual fairness
    if_assessment = monitor.assess_individual_fairness(predictions, features)
    print(f"Individual Fairness: {if_assessment.value:.3f} - {'FAIR' if if_assessment.is_fair else 'UNFAIR'}")
    
    # Intersectional bias
    intersectional_incidents = monitor.detect_intersectional_bias(predictions, protected_attrs, true_labels)
    print(f"Intersectional bias incidents detected: {len(intersectional_incidents)}")
    
    # Generate report
    report = monitor.generate_fairness_report("sample_model_v1")
    print(f"\nFairness Report Summary:")
    print(f"Overall Fairness Score: {report['overall_fairness_score']:.3f}")
    print(f"Status: {report['fairness_status']}")
    print(f"Recent Incidents: {len(report['recent_incidents'])}")
    
    return monitor, report

if __name__ == "__main__":
    fairness_monitor, report = demonstrate_fairness_monitoring()

import { EventEmitter } from 'events';
import crypto from 'crypto';

interface Stakeholder {
  id: string;
  name: string;
  type: StakeholderType;
  role: string;
  organization: string;
  expertise: string[];
  influenceLevel: 'low' | 'medium' | 'high';
  interestLevel: 'low' | 'medium' | 'high';
  contactInfo: ContactInfo;
  joinedAt: Date;
}

interface ContactInfo {
  email: string;
  phone?: string;
  preferredCommunication: 'email' | 'phone' | 'meeting' | 'survey';
}

enum StakeholderType {
  INTERNAL_TEAM = 'internal_team',
  EXTERNAL_EXPERT = 'external_expert',
  AFFECTED_COMMUNITY = 'affected_community',
  REGULATOR = 'regulator',
  ETHICIST = 'ethicist',
  TECHNICAL_REVIEWER = 'technical_reviewer',
  LEGAL_COUNSEL = 'legal_counsel',
  CIVIL_SOCIETY = 'civil_society'
}

interface EngagementActivity {
  activityId: string;
  type: EngagementType;
  title: string;
  description: string;
  stakeholderIds: string[];
  scheduledDate: Date;
  duration: number; // minutes
  facilitatorId: string;
  status: 'planned' | 'ongoing' | 'completed' | 'cancelled';
  outcomes: EngagementOutcome[];
  followUpActions: ActionItem[];
}

enum EngagementType {
  ADVISORY_BOARD_MEETING = 'advisory_board_meeting',
  COMMUNITY_CONSULTATION = 'community_consultation',
  EXPERT_REVIEW = 'expert_review',
  IMPACT_ASSESSMENT = 'impact_assessment',
  ETHICS_REVIEW = 'ethics_review',
  PUBLIC_COMMENT = 'public_comment',
  FOCUS_GROUP = 'focus_group',
  SURVEY = 'survey'
}

interface EngagementOutcome {
  outcomeId: string;
  category: 'feedback' | 'recommendation' | 'concern' | 'approval' | 'objection';
  description: string;
  priority: 'low' | 'medium' | 'high' | 'critical';
  stakeholderId: string;
  evidence: string[];
  proposedSolution?: string;
}

interface ActionItem {
  actionId: string;
  description: string;
  assigneeId: string;
  dueDate: Date;
  priority: 'low' | 'medium' | 'high' | 'critical';
  status: 'pending' | 'in_progress' | 'completed' | 'blocked';
  outcomeIds: string[];
}

interface GovernancePolicy {
  policyId: string;
  title: string;
  version: string;
  description: string;
  scope: string[];
  requirements: PolicyRequirement[];
  approvalProcess: ApprovalProcess;
  reviewCycle: number; // days
  lastReviewed: Date;
  nextReviewDate: Date;
  status: 'draft' | 'approved' | 'active' | 'deprecated';
}

interface PolicyRequirement {
  requirementId: string;
  description: string;
  type: 'mandatory' | 'recommended' | 'optional';
  verificationMethod: string;
  compliance: ComplianceStatus;
}

interface ComplianceStatus {
  isCompliant: boolean;
  lastChecked: Date;
  evidence: string[];
  deviations: string[];
  correctionPlan?: string;
}

interface ApprovalProcess {
  stages: ApprovalStage[];
  currentStage: number;
  requiredApprovers: string[];
  approvalHistory: ApprovalRecord[];
}

interface ApprovalStage {
  stageName: string;
  description: string;
  requiredApproverTypes: StakeholderType[];
  minimumApprovals: number;
  timeoutDays: number;
}

interface ApprovalRecord {
  approverId: string;
  decision: 'approve' | 'reject' | 'request_changes';
  comments: string;
  timestamp: Date;
  conditions?: string[];
}

class StakeholderEngagementSystem extends EventEmitter {
  private stakeholders: Map<string, Stakeholder> = new Map();
  private engagementActivities: Map<string, EngagementActivity> = new Map();
  private governancePolicies: Map<string, GovernancePolicy> = new Map();
  private actionItems: Map<string, ActionItem> = new Map();

  constructor() {
    super();
    this.initializeDefaultPolicies();
  }

  async registerStakeholder(stakeholder: Omit<Stakeholder, 'id' | 'joinedAt'>): Promise<string> {
    const stakeholderId = this.generateStakeholderId();
    
    const fullStakeholder: Stakeholder = {
      ...stakeholder,
      id: stakeholderId,
      joinedAt: new Date()
    };

    this.stakeholders.set(stakeholderId, fullStakeholder);
    
    // Send welcome communication
    await this.sendWelcomeCommunication(fullStakeholder);
    
    this.emit('stakeholderRegistered', { stakeholderId, type: stakeholder.type });
    
    return stakeholderId;
  }

  async planEngagementActivity(activity: Omit<EngagementActivity, 'activityId' | 'status' | 'outcomes' | 'followUpActions'>): Promise<string> {
    const activityId = this.generateActivityId();
    
    const engagementActivity: EngagementActivity = {
      ...activity,
      activityId,
      status: 'planned',
      outcomes: [],
      followUpActions: []
    };

    // Validate stakeholder availability and preferences
    const validationResult = await this.validateStakeholderEngagement(engagementActivity);
    if (!validationResult.isValid) {
      throw new Error(`Engagement planning failed: ${validationResult.reason}`);
    }

    this.engagementActivities.set(activityId, engagementActivity);
    
    // Send invitations to stakeholders
    await this.sendEngagementInvitations(engagementActivity);
    
    this.emit('engagementPlanned', { activityId, type: activity.type, stakeholderCount: activity.stakeholderIds.length });
    
    return activityId;
  }

  async conductEngagementActivity(activityId: string): Promise<EngagementOutcome[]> {
    const activity = this.engagementActivities.get(activityId);
    if (!activity) {
      throw new Error('Engagement activity not found');
    }

    if (activity.status !== 'planned') {
      throw new Error(`Cannot conduct activity in status: ${activity.status}`);
    }

    // Update status
    activity.status = 'ongoing';
    
    try {
      // Conduct engagement based on type
      const outcomes = await this.facilitateEngagement(activity);
      
      // Process outcomes
      activity.outcomes = outcomes;
      activity.followUpActions = await this.generateFollowUpActions(outcomes);
      activity.status = 'completed';
      
      // Create action items
      for (const actionItem of activity.followUpActions) {
        this.actionItems.set(actionItem.actionId, actionItem);
      }
      
      this.emit('engagementCompleted', { 
        activityId, 
        outcomesCount: outcomes.length, 
        actionItemsCount: activity.followUpActions.length 
      });
      
      return outcomes;
      
    } catch (error) {
      activity.status = 'cancelled';
      this.emit('engagementFailed', { activityId, error: error.message });
      throw error;
    }
  }

  async createGovernancePolicy(policy: Omit<GovernancePolicy, 'policyId' | 'status' | 'lastReviewed' | 'nextReviewDate'>): Promise<string> {
    const policyId = this.generatePolicyId();
    const now = new Date();
    
    const governancePolicy: GovernancePolicy = {
      ...policy,
      policyId,
      status: 'draft',
      lastReviewed: now,
      nextReviewDate: new Date(now.getTime() + policy.reviewCycle * 24 * 60 * 60 * 1000)
    };

    this.governancePolicies.set(policyId, governancePolicy);
    
    // Initiate approval process if required
    if (policy.approvalProcess.stages.length > 0) {
      await this.initiateApprovalProcess(policyId);
    }
    
    this.emit('policyCreated', { policyId, title: policy.title });
    
    return policyId;
  }

  async assessPolicyCompliance(policyId: string): Promise<ComplianceStatus> {
    const policy = this.governancePolicies.get(policyId);
    if (!policy) {
      throw new Error('Policy not found');
    }

    const complianceResults: ComplianceStatus[] = [];
    
    for (const requirement of policy.requirements) {
      const compliance = await this.checkRequirementCompliance(requirement);
      complianceResults.push(compliance);
    }

    // Aggregate compliance status
    const overallCompliance: ComplianceStatus = {
      isCompliant: complianceResults.every(c => c.isCompliant),
      lastChecked: new Date(),
      evidence: complianceResults.flatMap(c => c.evidence),
      deviations: complianceResults.flatMap(c => c.deviations)
    };

    // Update policy compliance
    policy.requirements.forEach((req, index) => {
      req.compliance = complianceResults[index];
    });

    if (!overallCompliance.isCompliant) {
      overallCompliance.correctionPlan = await this.generateCorrectionPlan(
        policy, 
        complianceResults.filter(c => !c.isCompliant)
      );
    }

    this.emit('complianceAssessed', { 
      policyId, 
      isCompliant: overallCompliance.isCompliant, 
      deviationCount: overallCompliance.deviations.length 
    });
    
    return overallCompliance;
  }

  async generateStakeholderImpactReport(projectId: string, timeframe: { start: Date; end: Date }): Promise<{
    summary: StakeholderImpactSummary;
    detailedAnalysis: StakeholderAnalysis[];
    recommendations: string[];
  }> {
    // Get relevant engagement activities
    const relevantActivities = Array.from(this.engagementActivities.values())
      .filter(activity => 
        activity.scheduledDate >= timeframe.start && 
        activity.scheduledDate <= timeframe.end &&
        activity.status === 'completed'
      );

    // Analyze stakeholder engagement
    const stakeholderAnalysis = await this.analyzeStakeholderEngagement(relevantActivities);
    
    // Generate impact summary
    const summary: StakeholderImpactSummary = {
      totalStakeholders: this.stakeholders.size,
      engagementActivities: relevantActivities.length,
      outcomesGenerated: relevantActivities.reduce((sum, activity) => sum + activity.outcomes.length, 0),
      actionItemsCreated: relevantActivities.reduce((sum, activity) => sum + activity.followUpActions.length, 0),
      criticalIssuesIdentified: relevantActivities
        .flatMap(activity => activity.outcomes)
        .filter(outcome => outcome.priority === 'critical').length,
      stakeholderSatisfactionScore: await this.calculateStakeholderSatisfaction(relevantActivities)
    };

    // Generate recommendations
    const recommendations = await this.generateEngagementRecommendations(stakeholderAnalysis, summary);

    return {
      summary,
      detailedAnalysis: stakeholderAnalysis,
      recommendations
    };
  }

  private async facilitateEngagement(activity: EngagementActivity): Promise<EngagementOutcome[]> {
    const outcomes: EngagementOutcome[] = [];
    
    switch (activity.type) {
      case EngagementType.ADVISORY_BOARD_MEETING:
        // Simulate advisory board discussion
        outcomes.push(...await this.simulateAdvisoryBoardOutcomes(activity));
        break;
        
      case EngagementType.COMMUNITY_CONSULTATION:
        // Simulate community feedback
        outcomes.push(...await this.simulateCommunityConsultationOutcomes(activity));
        break;
        
      case EngagementType.EXPERT_REVIEW:
        // Simulate expert analysis
        outcomes.push(...await this.simulateExpertReviewOutcomes(activity));
        break;
        
      case EngagementType.ETHICS_REVIEW:
        // Simulate ethics committee review
        outcomes.push(...await this.simulateEthicsReviewOutcomes(activity));
        break;
        
      default:
        // Generic engagement simulation
        outcomes.push(...await this.simulateGenericEngagementOutcomes(activity));
    }
    
    return outcomes;
  }

  private async simulateAdvisoryBoardOutcomes(activity: EngagementActivity): Promise<EngagementOutcome[]> {
    const outcomes: EngagementOutcome[] = [];
    
    // Simulate diverse stakeholder perspectives
    const perspectives = [
      {
        category: 'recommendation' as const,
        description: 'Implement regular bias audits with third-party validation',
        priority: 'high' as const,
        evidence: ['industry_best_practices', 'regulatory_compliance']
      },
      {
        category: 'concern' as const,
        description: 'Current transparency measures may not be sufficient for regulatory compliance',
        priority: 'critical' as const,
        evidence: ['gdpr_requirements', 'eu_ai_act']
      },
      {
        category: 'approval' as const,
        description: 'Stakeholder engagement process meets industry standards',
        priority: 'medium' as const,
        evidence: ['iso_standards', 'peer_review']
      }
    ];
    
    for (let i = 0; i < perspectives.length && i < activity.stakeholderIds.length; i++) {
      const perspective = perspectives[i];
      outcomes.push({
        outcomeId: this.generateOutcomeId(),
        ...perspective,
        stakeholderId: activity.stakeholderIds[i],
        proposedSolution: perspective.category === 'concern' ? 
          'Implement enhanced transparency dashboard with real-time metrics' : undefined
      });
    }
    
    return outcomes;
  }

  private async simulateEthicsReviewOutcomes(activity: EngagementActivity): Promise<EngagementOutcome[]> {
    const ethicsOutcomes = [
      {
        category: 'recommendation' as const,
        description: 'Establish clear ethical guidelines for AI decision boundaries',
        priority: 'critical' as const,
        evidence: ['ethics_framework', 'case_studies'],
        proposedSolution: 'Create ethics decision tree and escalation procedures'
      },
      {
        category: 'concern' as const,
        description: 'Potential for discriminatory impact on vulnerable populations',
        priority: 'high' as const,
        evidence: ['impact_assessment', 'community_feedback'],
        proposedSolution: 'Implement targeted fairness constraints and monitoring'
      },
      {
        category: 'recommendation' as const,
        description: 'Enhance human oversight mechanisms for high-risk decisions',
        priority: 'high' as const,
        evidence: ['regulatory_guidance', 'expert_consensus'],
        proposedSolution: 'Implement human-in-the-loop validation for critical predictions'
      }
    ];
    
    return ethicsOutcomes.map((outcome, index) => ({
      outcomeId: this.generateOutcomeId(),
      ...outcome,
      stakeholderId: activity.stakeholderIds[index % activity.stakeholderIds.length]
    }));
  }

  private async generateFollowUpActions(outcomes: EngagementOutcome[]): Promise<ActionItem[]> {
    const actions: ActionItem[] = [];
    
    for (const outcome of outcomes) {
      if (outcome.priority === 'critical' || outcome.priority === 'high') {
        const dueDate = new Date();
        dueDate.setDate(dueDate.getDate() + (outcome.priority === 'critical' ? 7 : 30));
        
        actions.push({
          actionId: this.generateActionId(),
          description: outcome.proposedSolution || `Address: ${outcome.description}`,
          assigneeId: 'governance_team', // In practice, would be assigned based on expertise
          dueDate,
          priority: outcome.priority,
          status: 'pending',
          outcomeIds: [outcome.outcomeId]
        });
      }
    }
    
    return actions;
  }

  private async checkRequirementCompliance(requirement: PolicyRequirement): Promise<ComplianceStatus> {
    // Simulate compliance checking
    const isCompliant = Math.random() > 0.3; // 70% compliance rate simulation
    
    return {
      isCompliant,
      lastChecked: new Date(),
      evidence: isCompliant ? ['automated_check_passed', 'manual_verification'] : [],
      deviations: isCompliant ? [] : [`Non-compliance detected: ${requirement.description}`]
    };
  }

  private async calculateStakeholderSatisfaction(activities: EngagementActivity[]): Promise<number> {
    // Simulate satisfaction calculation based on engagement outcomes
    const totalOutcomes = activities.reduce((sum, activity) => sum + activity.outcomes.length, 0);
    const positiveOutcomes = activities
      .flatMap(activity => activity.outcomes)
      .filter(outcome => outcome.category === 'approval' || outcome.category === 'recommendation').length;
    
    return totalOutcomes > 0 ? (positiveOutcomes / totalOutcomes) * 100 : 50;
  }

  private generateStakeholderId(): string {
    return `stakeholder_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
  }

  private generateActivityId(): string {
    return `activity_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
  }

  private generatePolicyId(): string {
    return `policy_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
  }

  private generateOutcomeId(): string {
    return `outcome_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
  }

  private generateActionId(): string {
    return `action_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
  }

  private initializeDefaultPolicies(): void {
    // Initialize with common governance policies
    console.log('Initialized stakeholder engagement system with default governance policies');
  }
}

interface StakeholderImpactSummary {
  totalStakeholders: number;
  engagementActivities: number;
  outcomesGenerated: number;
  actionItemsCreated: number;
  criticalIssuesIdentified: number;
  stakeholderSatisfactionScore: number;
}

interface StakeholderAnalysis {
  stakeholderId: string;
  engagementLevel: 'low' | 'medium' | 'high';
  contributionQuality: number;
  issuesRaised: number;
  recommendationsProvided: number;
}

// Usage example
async function demonstrateStakeholderGovernance() {
  const governanceSystem = new StakeholderEngagementSystem();
  
  // Register stakeholders
  const ethicistId = await governanceSystem.registerStakeholder({
    name: 'Dr. Jane Smith',
    type: StakeholderType.ETHICIST,
    role: 'AI Ethics Specialist',
    organization: 'Ethics Research Institute',
    expertise: ['AI ethics', 'bias detection', 'algorithmic fairness'],
    influenceLevel: 'high',
    interestLevel: 'high',
    contactInfo: {
      email: 'jane.smith@ethics.org',
      preferredCommunication: 'email'
    }
  });
  
  const communityRepId = await governanceSystem.registerStakeholder({
    name: 'Maria Garcia',
    type: StakeholderType.AFFECTED_COMMUNITY,
    role: 'Community Representative',
    organization: 'Citizens for AI Accountability',
    expertise: ['community advocacy', 'civil rights'],
    influenceLevel: 'medium',
    interestLevel: 'high',
    contactInfo: {
      email: 'maria@citizensai.org',
      preferredCommunication: 'meeting'
    }
  });
  
  // Plan ethics review
  const ethicsReviewId = await governanceSystem.planEngagementActivity({
    type: EngagementType.ETHICS_REVIEW,
    title: 'AI System Ethics Review',
    description: 'Comprehensive review of AI system ethical implications',
    stakeholderIds: [ethicistId, communityRepId],
    scheduledDate: new Date(Date.now() + 7 * 24 * 60 * 60 * 1000), // 7 days from now
    duration: 120,
    facilitatorId: 'governance_lead'
  });
  
  // Conduct ethics review
  const outcomes = await governanceSystem.conductEngagementActivity(ethicsReviewId);
  console.log(`Ethics review completed with ${outcomes.length} outcomes`);
  
  // Generate impact report
  const impactReport = await governanceSystem.generateStakeholderImpactReport(
    'ai_system_v1',
    {
      start: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000), // 30 days ago
      end: new Date()
    }
  );
  
  console.log('Stakeholder Impact Report:', impactReport.summary);
  
  return { governanceSystem, impactReport };
}

export { StakeholderEngagementSystem, StakeholderType, EngagementType };