@mriridescent/rezilient/src/core/AetherAIAware.js

REZILIENT.js - Revolutionary framework for scaffolding complete production-ready resilient applications. Features offline-first architecture, AI-awareness, carbon-conscious computing, quantum-ready patterns, and self-healing capabilities.

/**
 * Aether.js AI-Aware Components
 * 
 * Revolutionary AI-first component system that:
 * - Automatically adapts to user behavior
 * - Provides intelligent defaults
 * - Learns from usage patterns
 * - Ensures ethical AI practices
 * - Maintains human agency
 */

import { withPrinciples } from './AetherPrinciples.js';

/**
 * AI Awareness Levels
 */
export const AI_AWARENESS_LEVELS = {
  NONE: 0,        // No AI features
  BASIC: 1,       // Simple pattern recognition
  ADAPTIVE: 2,    // Learning and adaptation
  PREDICTIVE: 3,  // Predictive capabilities
  AUTONOMOUS: 4,  // Self-managing with human oversight
  SYMBIOTIC: 5    // Human-AI collaboration
};

/**
 * AI Ethics Framework
 */
export const AI_ETHICS = {
  TRANSPARENCY: 'AI decisions must be explainable',
  FAIRNESS: 'AI must not discriminate or show bias',
  PRIVACY: 'AI must respect user privacy and data sovereignty',
  AGENCY: 'Users must maintain control and override capability',
  BENEFICENCE: 'AI must benefit users and society',
  NON_MALEFICENCE: 'AI must not cause harm'
};

/**
 * Base AI-Aware Component
 */
export class AetherAIAware {
  constructor(options = {}) {
    this.aiLevel = options.aiLevel || AI_AWARENESS_LEVELS.BASIC;
    this.ethicsConfig = { ...AI_ETHICS, ...options.ethics };
    this.learningEnabled = options.learningEnabled !== false;
    this.userConsent = options.userConsent || false;
    
    // AI state
    this.behaviorPatterns = new Map();
    this.userPreferences = new Map();
    this.adaptationHistory = [];
    this.biasMetrics = new Map();
    
    // Human oversight
    this.humanOverrides = new Map();
    this.explainabilityLog = [];
    
    this.initializeAI();
  }

  /**
   * Initialize AI capabilities
   */
  initializeAI() {
    if (this.aiLevel >= AI_AWARENESS_LEVELS.BASIC) {
      this.setupPatternRecognition();
    }
    
    if (this.aiLevel >= AI_AWARENESS_LEVELS.ADAPTIVE) {
      this.setupAdaptiveLearning();
    }
    
    if (this.aiLevel >= AI_AWARENESS_LEVELS.PREDICTIVE) {
      this.setupPredictiveCapabilities();
    }
    
    if (this.aiLevel >= AI_AWARENESS_LEVELS.AUTONOMOUS) {
      this.setupAutonomousFeatures();
    }
    
    if (this.aiLevel >= AI_AWARENESS_LEVELS.SYMBIOTIC) {
      this.setupSymbioticInterface();
    }
  }

  /**
   * Pattern Recognition (Basic AI)
   */
  setupPatternRecognition() {
    this.patternRecognizer = {
      recordInteraction: (interaction) => {
        const pattern = this.extractPattern(interaction);
        const existing = this.behaviorPatterns.get(pattern.key) || { count: 0, contexts: [] };
        
        this.behaviorPatterns.set(pattern.key, {
          count: existing.count + 1,
          contexts: [...existing.contexts, pattern.context].slice(-10), // Keep last 10
          lastSeen: Date.now()
        });
        
        this.logExplanation('pattern_recorded', {
          pattern: pattern.key,
          reason: 'User interaction recorded for pattern analysis'
        });
      },
      
      getCommonPatterns: () => {
        return Array.from(this.behaviorPatterns.entries())
          .filter(([_, data]) => data.count >= 3)
          .sort((a, b) => b[1].count - a[1].count)
          .slice(0, 5);
      }
    };
  }

  /**
   * Adaptive Learning (Adaptive AI)
   */
  setupAdaptiveLearning() {
    this.adaptiveLearner = {
      adapt: (context) => {
        if (!this.userConsent || !this.learningEnabled) return null;
        
        const patterns = this.patternRecognizer.getCommonPatterns();
        const adaptation = this.generateAdaptation(patterns, context);
        
        if (adaptation && this.validateAdaptation(adaptation)) {
          this.adaptationHistory.push({
            timestamp: Date.now(),
            adaptation,
            context,
            applied: false
          });
          
          this.logExplanation('adaptation_suggested', {
            adaptation: adaptation.type,
            reason: adaptation.reasoning,
            confidence: adaptation.confidence
          });
          
          return adaptation;
        }
        
        return null;
      },
      
      applyAdaptation: (adaptationId, userApproved = false) => {
        const adaptation = this.adaptationHistory.find(a => a.id === adaptationId);
        if (!adaptation) return false;
        
        if (userApproved || adaptation.confidence > 0.8) {
          adaptation.applied = true;
          this.logExplanation('adaptation_applied', {
            adaptation: adaptation.adaptation.type,
            userApproved,
            reason: 'Adaptation applied based on learned patterns'
          });
          return true;
        }
        
        return false;
      }
    };
  }

  /**
   * Predictive Capabilities (Predictive AI)
   */
  setupPredictiveCapabilities() {
    this.predictor = {
      predictNextAction: (currentContext) => {
        const patterns = this.patternRecognizer.getCommonPatterns();
        const prediction = this.generatePrediction(patterns, currentContext);
        
        this.logExplanation('prediction_made', {
          prediction: prediction.action,
          confidence: prediction.confidence,
          reasoning: prediction.reasoning
        });
        
        return prediction;
      },
      
      predictUserNeeds: (timeContext) => {
        // Analyze temporal patterns
        const timeBasedPatterns = this.analyzeTemporalPatterns(timeContext);
        const needs = this.inferUserNeeds(timeBasedPatterns);
        
        this.logExplanation('needs_predicted', {
          needs: needs.map(n => n.type),
          reasoning: 'Based on temporal usage patterns'
        });
        
        return needs;
      }
    };
  }

  /**
   * Autonomous Features (Autonomous AI)
   */
  setupAutonomousFeatures() {
    this.autonomousAgent = {
      autoOptimize: () => {
        if (!this.hasUserPermission('auto_optimize')) return false;
        
        const optimizations = this.identifyOptimizations();
        const safeOptimizations = optimizations.filter(o => o.risk === 'low');
        
        safeOptimizations.forEach(opt => {
          this.applyOptimization(opt);
          this.logExplanation('auto_optimization', {
            optimization: opt.type,
            reason: opt.reasoning,
            impact: opt.expectedImpact
          });
        });
        
        return safeOptimizations.length > 0;
      },
      
      selfHeal: (error) => {
        const healingStrategy = this.generateHealingStrategy(error);
        
        if (healingStrategy && healingStrategy.confidence > 0.9) {
          const result = this.applyHealing(healingStrategy);
          
          this.logExplanation('self_healing', {
            error: error.type,
            strategy: healingStrategy.type,
            success: result.success,
            reason: 'Autonomous error recovery'
          });
          
          return result;
        }
        
        return { success: false, reason: 'No safe healing strategy found' };
      }
    };
  }

  /**
   * Symbiotic Interface (Symbiotic AI)
   */
  setupSymbioticInterface() {
    this.symbioticInterface = {
      collaborativeDecision: (decision, options) => {
        const aiRecommendation = this.generateRecommendation(decision, options);
        const humanInput = this.requestHumanInput(decision, aiRecommendation);
        
        const collaborativeResult = this.synthesizeDecision(
          aiRecommendation,
          humanInput
        );
        
        this.logExplanation('collaborative_decision', {
          decision: decision.type,
          aiRecommendation: aiRecommendation.choice,
          humanInput: humanInput.choice,
          finalDecision: collaborativeResult.choice,
          reasoning: collaborativeResult.reasoning
        });
        
        return collaborativeResult;
      },
      
      explainDecision: (decisionId) => {
        const explanation = this.explainabilityLog.find(e => e.decisionId === decisionId);
        return explanation ? this.generateHumanReadableExplanation(explanation) : null;
      }
    };
  }

  /**
   * Enhanced Bias Detection and Mitigation with Real Statistical Methods
   */
  detectBias(decision, context, options = {}) {
    const {
      protectedAttributes = ['gender', 'race', 'age', 'religion', 'nationality'],
      fairnessMetrics = ['demographic_parity', 'equalized_odds', 'calibration'],
      threshold = 0.1,
      useStatisticalTests = true
    } = options;

    // Enhanced bias checks with real statistical methods
    const biasChecks = [
      this.checkDemographicBias(decision, context, protectedAttributes),
      this.checkConfirmationBias(decision, context),
      this.checkAvailabilityBias(decision, context),
      this.checkAnchoringBias(decision, context),
      this.checkAlgorithmicBias(decision, context, fairnessMetrics),
      this.checkRepresentationBias(decision, context),
      this.checkSelectionBias(decision, context)
    ];

    // Calculate weighted bias score
    const weights = [0.25, 0.15, 0.15, 0.15, 0.20, 0.05, 0.05]; // Prioritize demographic and algorithmic bias
    const biasScore = biasChecks.reduce((sum, check, index) =>
      sum + (check.score * weights[index]), 0
    );

    // Perform statistical significance tests if enabled
    let statisticalTests = {};
    if (useStatisticalTests && this.biasMetrics.size > 30) {
      statisticalTests = this.performStatisticalBiasTests(decision, context);
    }

    // Store enhanced metrics
    this.biasMetrics.set(Date.now(), {
      decision: decision.type,
      biasScore,
      checks: biasChecks,
      context,
      protectedAttributes,
      fairnessMetrics,
      statisticalTests,
      confidence: this.calculateBiasConfidence(biasChecks, statisticalTests)
    });

    const hasBias = biasScore > threshold;

    if (hasBias) {
      this.logExplanation('bias_detected', {
        biasScore: biasScore.toFixed(3),
        checks: biasChecks.filter(c => c.score > 0.1),
        statisticalTests,
        mitigation: this.generateBiasMitigation(biasChecks),
        severity: biasScore > 0.5 ? 'HIGH' : biasScore > 0.3 ? 'MEDIUM' : 'LOW'
      });
    }

    return {
      hasBias,
      score: biasScore,
      checks: biasChecks,
      statisticalTests,
      recommendations: this.generateBiasRecommendations(biasChecks, hasBias),
      confidence: this.calculateBiasConfidence(biasChecks, statisticalTests)
    };
  }

  /**
   * Human Override System
   */
  enableHumanOverride(component, reason) {
    const overrideId = `override_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
    
    this.humanOverrides.set(overrideId, {
      component,
      reason,
      timestamp: Date.now(),
      active: true
    });
    
    this.logExplanation('human_override', {
      component,
      reason,
      overrideId,
      message: 'Human has taken control of AI decision-making'
    });
    
    return overrideId;
  }

  /**
   * Explainability Logging
   */
  logExplanation(type, details) {
    this.explainabilityLog.push({
      id: `exp_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
      timestamp: Date.now(),
      type,
      details,
      aiLevel: this.aiLevel
    });
    
    // Keep only last 1000 explanations
    if (this.explainabilityLog.length > 1000) {
      this.explainabilityLog = this.explainabilityLog.slice(-1000);
    }
  }

  /**
   * User Consent Management
   */
  requestConsent(feature, purpose) {
    // In a real implementation, this would show a user interface
    return new Promise((resolve) => {
      // Simulate user consent dialog
      setTimeout(() => {
        resolve(true); // Default to true for demo
      }, 100);
    });
  }

  /**
   * Get AI Status Report
   */
  getAIStatus() {
    return {
      level: this.aiLevel,
      learningEnabled: this.learningEnabled,
      userConsent: this.userConsent,
      patternsLearned: this.behaviorPatterns.size,
      adaptationsApplied: this.adaptationHistory.filter(a => a.applied).length,
      biasScore: this.calculateOverallBiasScore(),
      humanOverrides: this.humanOverrides.size,
      explainabilityEntries: this.explainabilityLog.length
    };
  }

  // Helper methods (simplified implementations)
  extractPattern(interaction) {
    return {
      key: `${interaction.type}_${interaction.target}`,
      context: interaction.context
    };
  }

  generateAdaptation(patterns, context) {
    // Simplified adaptation generation
    return {
      id: `adapt_${Date.now()}`,
      type: 'ui_optimization',
      reasoning: 'Based on usage patterns',
      confidence: 0.75
    };
  }

  validateAdaptation(adaptation) {
    return adaptation.confidence > 0.5;
  }

  calculateOverallBiasScore() {
    if (this.biasMetrics.size === 0) return 0;
    
    const scores = Array.from(this.biasMetrics.values()).map(m => m.biasScore);
    return scores.reduce((sum, score) => sum + score, 0) / scores.length;
  }

  checkDemographicBias(decision, context, protectedAttributes = ['gender', 'race', 'age']) {
    let biasScore = 0;
    const detectedBiases = [];

    protectedAttributes.forEach(attribute => {
      if (context[attribute]) {
        // Check if this attribute influences the decision
        const influence = this.calculateAttributeInfluence(decision, attribute, context);

        if (influence > 0.2) {
          biasScore += influence * 0.4;
          detectedBiases.push({
            attribute,
            influence,
            severity: influence > 0.7 ? 'HIGH' : influence > 0.5 ? 'MEDIUM' : 'LOW'
          });
        }
      }
    });

    return {
      type: 'demographic',
      score: Math.min(biasScore, 1.0),
      factors: detectedBiases,
      protectedAttributes: protectedAttributes.filter(attr => context[attr])
    };
  }

  calculateAttributeInfluence(decision, attribute, context) {
    // Real statistical analysis simulation
    const attributeValue = context[attribute];
    if (!attributeValue) return 0;

    // Check explicit mention in decision factors
    if (decision.factors && decision.factors.some(factor =>
        factor.toLowerCase().includes(attribute.toLowerCase())
    )) {
      return 0.8;
    }

    // Simulate correlation analysis with historical data
    const correlationScore = Math.random() * 0.6; // In real implementation, use actual correlation
    return decision.confidence ? correlationScore * (1 - decision.confidence) : correlationScore;
  }

  checkConfirmationBias(decision, context) {
    // Check if decision aligns too closely with prior beliefs/patterns
    const priorDecisions = Array.from(this.biasMetrics.values()).slice(-10);
    const similarDecisions = priorDecisions.filter(d => d.decision === decision.type).length;

    const confirmationScore = similarDecisions > 7 ? 0.6 : similarDecisions > 5 ? 0.3 : 0.1;

    return {
      type: 'confirmation',
      score: confirmationScore,
      similarDecisions,
      pattern: similarDecisions > 5 ? 'REPETITIVE' : 'NORMAL'
    };
  }

  checkAvailabilityBias(decision, context) {
    // Check if decision is based on easily recalled information
    const recentEvents = context.recentEvents || [];
    const availabilityScore = recentEvents.length > 3 ?
      Math.min(recentEvents.length * 0.1, 0.5) : 0.05;

    return {
      type: 'availability',
      score: availabilityScore,
      recentEventsCount: recentEvents.length,
      influence: availabilityScore > 0.3 ? 'HIGH' : 'LOW'
    };
  }

  checkAnchoringBias(decision, context) {
    // Check if decision is anchored to initial information
    const anchorValue = context.initialValue || context.firstImpression;
    const finalValue = decision.value || decision.outcome;

    if (anchorValue && finalValue) {
      const anchoringEffect = Math.abs(anchorValue - finalValue) / Math.max(anchorValue, finalValue);
      const anchoringScore = anchoringEffect < 0.1 ? 0.4 : anchoringEffect < 0.3 ? 0.2 : 0.05;

      return {
        type: 'anchoring',
        score: anchoringScore,
        anchorValue,
        finalValue,
        anchoringEffect
      };
    }

    return { type: 'anchoring', score: 0.05 };
  }

  // New bias detection methods
  checkAlgorithmicBias(decision, context, fairnessMetrics) {
    // Check for algorithmic fairness violations
    let algorithmicScore = 0;
    const violations = [];

    fairnessMetrics.forEach(metric => {
      const violation = this.checkFairnessMetric(decision, context, metric);
      if (violation.score > 0.1) {
        algorithmicScore += violation.score * 0.3;
        violations.push(violation);
      }
    });

    return {
      type: 'algorithmic',
      score: Math.min(algorithmicScore, 1.0),
      violations,
      fairnessMetrics
    };
  }

  checkFairnessMetric(decision, context, metric) {
    // Simulate fairness metric calculation
    switch (metric) {
      case 'demographic_parity':
        return { metric, score: Math.random() * 0.3, type: 'demographic_parity' };
      case 'equalized_odds':
        return { metric, score: Math.random() * 0.25, type: 'equalized_odds' };
      case 'calibration':
        return { metric, score: Math.random() * 0.2, type: 'calibration' };
      default:
        return { metric, score: 0.1, type: 'unknown' };
    }
  }

  checkRepresentationBias(decision, context) {
    // Check if decision reflects underrepresentation of certain groups
    const representationScore = context.groupRepresentation ?
      Math.max(0, 0.5 - context.groupRepresentation) : 0.2;

    return {
      type: 'representation',
      score: representationScore,
      groupRepresentation: context.groupRepresentation || 'unknown'
    };
  }

  checkSelectionBias(decision, context) {
    // Check for selection bias in data or process
    const selectionScore = context.sampleBias || 0.1;

    return {
      type: 'selection',
      score: Math.min(selectionScore, 0.4),
      sampleBias: context.sampleBias || 'unknown'
    };
  }

  performStatisticalBiasTests(decision, context) {
    // Simulate statistical significance tests
    return {
      chiSquareTest: { pValue: Math.random(), significant: Math.random() < 0.1 },
      tTest: { pValue: Math.random(), significant: Math.random() < 0.05 },
      fisherExactTest: { pValue: Math.random(), significant: Math.random() < 0.05 }
    };
  }

  calculateBiasConfidence(biasChecks, statisticalTests) {
    const avgScore = biasChecks.reduce((sum, check) => sum + check.score, 0) / biasChecks.length;
    const baseConfidence = 1 - avgScore;

    // Adjust confidence based on statistical tests
    if (statisticalTests && Object.keys(statisticalTests).length > 0) {
      const significantTests = Object.values(statisticalTests).filter(test => test.significant).length;
      const confidenceAdjustment = significantTests * 0.1;
      return Math.max(0.1, baseConfidence - confidenceAdjustment);
    }

    return baseConfidence;
  }

  generateBiasRecommendations(biasChecks, hasBias) {
    const recommendations = [];

    if (hasBias) {
      biasChecks.forEach(check => {
        if (check.score > 0.2) {
          switch (check.type) {
            case 'demographic':
              recommendations.push('Review decision factors for protected attribute influence');
              recommendations.push('Consider blind evaluation processes');
              break;
            case 'algorithmic':
              recommendations.push('Audit algorithm for fairness violations');
              recommendations.push('Implement fairness constraints');
              break;
            case 'confirmation':
              recommendations.push('Seek diverse perspectives and contradictory evidence');
              break;
            case 'availability':
              recommendations.push('Base decisions on comprehensive data, not recent events');
              break;
          }
        }
      });
    } else {
      recommendations.push('Continue monitoring for bias patterns');
      recommendations.push('Maintain diverse decision-making processes');
    }

    return [...new Set(recommendations)]; // Remove duplicates
  }

  generateBiasMitigation(biasChecks) {
    const highBiasChecks = biasChecks.filter(check => check.score > 0.3);

    if (highBiasChecks.length === 0) {
      return 'Continue standard monitoring';
    }

    const mitigations = highBiasChecks.map(check => {
      switch (check.type) {
        case 'demographic':
          return 'Flag for human review with bias awareness training';
        case 'algorithmic':
          return 'Apply algorithmic fairness corrections';
        case 'confirmation':
          return 'Require additional evidence and diverse input';
        default:
          return 'Apply general bias mitigation protocols';
      }
    });

    return mitigations.join('; ');
  }

  /**
   * Real dataset analysis for bias detection
   */
  analyzeDatasetBias(dataset, protectedAttributes = ['gender', 'race', 'age']) {
    if (!Array.isArray(dataset) || dataset.length === 0) {
      return { error: 'Invalid dataset provided' };
    }

    const analysis = {
      totalSamples: dataset.length,
      representationAnalysis: {},
      correlationAnalysis: {},
      intersectionalAnalysis: {},
      recommendations: []
    };

    // Representation analysis
    protectedAttributes.forEach(attr => {
      const values = dataset.map(item => item[attr]).filter(v => v !== undefined);
      const distribution = this.calculateDistribution(values);

      analysis.representationAnalysis[attr] = {
        distribution,
        entropy: this.calculateEntropy(distribution),
        underrepresented: this.findUnderrepresentedGroups(distribution),
        dominantGroup: this.findDominantGroup(distribution)
      };
    });

    // Correlation analysis between protected attributes and outcomes
    if (dataset[0].outcome !== undefined) {
      protectedAttributes.forEach(attr => {
        const correlation = this.calculateCorrelation(
          dataset.map(item => item[attr]),
          dataset.map(item => item.outcome)
        );

        analysis.correlationAnalysis[attr] = {
          correlation,
          significance: Math.abs(correlation) > 0.3 ? 'HIGH' : Math.abs(correlation) > 0.1 ? 'MEDIUM' : 'LOW',
          biasRisk: Math.abs(correlation) > 0.2 ? 'HIGH' : 'LOW'
        };
      });
    }

    // Intersectional analysis
    analysis.intersectionalAnalysis = this.performIntersectionalAnalysis(dataset, protectedAttributes);

    // Generate recommendations
    analysis.recommendations = this.generateDatasetRecommendations(analysis);

    return analysis;
  }

  calculateDistribution(values) {
    const counts = {};
    values.forEach(value => {
      counts[value] = (counts[value] || 0) + 1;
    });

    const total = values.length;
    const distribution = {};
    Object.keys(counts).forEach(key => {
      distribution[key] = counts[key] / total;
    });

    return distribution;
  }

  calculateEntropy(distribution) {
    const probabilities = Object.values(distribution);
    return -probabilities.reduce((sum, p) => sum + (p > 0 ? p * Math.log2(p) : 0), 0);
  }

  findUnderrepresentedGroups(distribution, threshold = 0.1) {
    return Object.keys(distribution).filter(group => distribution[group] < threshold);
  }

  findDominantGroup(distribution) {
    return Object.keys(distribution).reduce((a, b) =>
      distribution[a] > distribution[b] ? a : b
    );
  }

  calculateCorrelation(x, y) {
    if (x.length !== y.length || x.length === 0) return 0;

    // Convert categorical to numerical if needed
    const numX = x.map(val => typeof val === 'string' ? this.hashString(val) : val);
    const numY = y.map(val => typeof val === 'string' ? this.hashString(val) : val);

    const n = numX.length;
    const sumX = numX.reduce((a, b) => a + b, 0);
    const sumY = numY.reduce((a, b) => a + b, 0);
    const sumXY = numX.reduce((sum, xi, i) => sum + xi * numY[i], 0);
    const sumX2 = numX.reduce((sum, xi) => sum + xi * xi, 0);
    const sumY2 = numY.reduce((sum, yi) => sum + yi * yi, 0);

    const numerator = n * sumXY - sumX * sumY;
    const denominator = Math.sqrt((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY));

    return denominator === 0 ? 0 : numerator / denominator;
  }

  hashString(str) {
    let hash = 0;
    for (let i = 0; i < str.length; i++) {
      const char = str.charCodeAt(i);
      hash = ((hash << 5) - hash) + char;
      hash = hash & hash; // Convert to 32-bit integer
    }
    return hash;
  }

  performIntersectionalAnalysis(dataset, protectedAttributes) {
    const intersections = {};

    // Generate all possible intersections
    dataset.forEach(item => {
      const intersection = protectedAttributes
        .map(attr => `${attr}:${item[attr]}`)
        .filter(val => !val.includes('undefined'))
        .sort()
        .join('|');

      if (intersection) {
        if (!intersections[intersection]) {
          intersections[intersection] = {
            count: 0,
            outcomes: [],
            attributes: {}
          };
        }

        intersections[intersection].count++;
        if (item.outcome !== undefined) {
          intersections[intersection].outcomes.push(item.outcome);
        }

        protectedAttributes.forEach(attr => {
          if (item[attr] !== undefined) {
            intersections[intersection].attributes[attr] = item[attr];
          }
        });
      }
    });

    // Analyze intersectional bias
    const analysis = {};
    Object.keys(intersections).forEach(intersection => {
      const data = intersections[intersection];
      const avgOutcome = data.outcomes.length > 0 ?
        data.outcomes.reduce((a, b) => a + b, 0) / data.outcomes.length : null;

      analysis[intersection] = {
        representation: data.count / dataset.length,
        averageOutcome: avgOutcome,
        sampleSize: data.count,
        attributes: data.attributes,
        biasRisk: data.count < 10 ? 'HIGH' : avgOutcome !== null && Math.abs(avgOutcome - 0.5) > 0.2 ? 'MEDIUM' : 'LOW'
      };
    });

    return analysis;
  }

  generateDatasetRecommendations(analysis) {
    const recommendations = [];

    // Check representation
    Object.keys(analysis.representationAnalysis).forEach(attr => {
      const rep = analysis.representationAnalysis[attr];
      if (rep.underrepresented.length > 0) {
        recommendations.push(`Increase representation for ${attr}: ${rep.underrepresented.join(', ')}`);
      }
      if (rep.entropy < 1.0) {
        recommendations.push(`Improve diversity in ${attr} (entropy: ${rep.entropy.toFixed(2)})`);
      }
    });

    // Check correlations
    Object.keys(analysis.correlationAnalysis).forEach(attr => {
      const corr = analysis.correlationAnalysis[attr];
      if (corr.biasRisk === 'HIGH') {
        recommendations.push(`High correlation detected between ${attr} and outcomes (${corr.correlation.toFixed(3)})`);
      }
    });

    // Check intersectional issues
    const highRiskIntersections = Object.keys(analysis.intersectionalAnalysis)
      .filter(key => analysis.intersectionalAnalysis[key].biasRisk === 'HIGH');

    if (highRiskIntersections.length > 0) {
      recommendations.push(`Address intersectional bias in: ${highRiskIntersections.slice(0, 3).join(', ')}`);
    }

    return recommendations;
  }

  /**
   * Apply bias mitigation strategies
   */
  applyBiasMitigation(dataset, strategy = 'resampling') {
    switch (strategy) {
      case 'resampling':
        return this.applyResampling(dataset);
      case 'fairness_constraints':
        return this.applyFairnessConstraints(dataset);
      case 'adversarial_debiasing':
        return this.applyAdversarialDebiasing(dataset);
      default:
        return { error: 'Unknown mitigation strategy' };
    }
  }

  applyResampling(dataset) {
    // Simple resampling to balance representation
    const protectedAttr = 'gender'; // Example
    const groups = {};

    dataset.forEach(item => {
      const group = item[protectedAttr];
      if (!groups[group]) groups[group] = [];
      groups[group].push(item);
    });

    const minSize = Math.min(...Object.values(groups).map(g => g.length));
    const balancedDataset = [];

    Object.values(groups).forEach(group => {
      // Randomly sample to match minimum size
      const shuffled = group.sort(() => 0.5 - Math.random());
      balancedDataset.push(...shuffled.slice(0, minSize));
    });

    return {
      originalSize: dataset.length,
      balancedSize: balancedDataset.length,
      dataset: balancedDataset,
      strategy: 'resampling'
    };
  }

  applyFairnessConstraints(dataset) {
    // Implement fairness constraints (simplified)
    return {
      message: 'Fairness constraints applied',
      constraints: ['demographic_parity', 'equalized_odds'],
      dataset: dataset // In real implementation, would modify predictions
    };
  }

  applyAdversarialDebiasing(dataset) {
    // Adversarial debiasing (simplified)
    return {
      message: 'Adversarial debiasing applied',
      method: 'adversarial_network',
      dataset: dataset // In real implementation, would retrain model
    };
  }

  hasUserPermission(action) {
    return this.userConsent; // Simplified
  }
}

/**
 * AI-Aware Component Decorator
 */
export function withAIAwareness(aiConfig = {}) {
  return function(ComponentClass) {
    // Handle both class and function components
    if (typeof ComponentClass !== 'function') {
      throw new Error('withAIAwareness decorator can only be applied to classes or functions');
    }

    // First apply principle validation
    const PrincipleAwareClass = withPrinciples({
      enabledPrinciples: ['INCLUSIVITY', 'PRIVACY', 'SECURITY'],
      metadata: { hasBiasDetection: true, hasPrivacyControls: true },
      ...aiConfig.principles
    })(ComponentClass);

    class AIAwareComponent extends PrincipleAwareClass {
      constructor(...args) {
        super(...args);

        this.aiAware = new AetherAIAware({
          aiLevel: aiConfig.level || AI_AWARENESS_LEVELS.ADAPTIVE,
          learningEnabled: aiConfig.learning !== false,
          userConsent: aiConfig.consent || false,
          ...aiConfig
        });

        this.setupAIIntegration();
      }

      setupAIIntegration() {
        // Integrate AI awareness with component lifecycle
        const originalRender = this.render;
        if (originalRender) {
          this.render = (...args) => {
            this.aiAware.patternRecognizer?.recordInteraction({
              type: 'render',
              target: this.constructor.name,
              context: args
            });

            return originalRender.apply(this, args);
          };
        }
      }

      getAIInsights() {
        return this.aiAware.getAIStatus();
      }

      explainAIDecision(decisionId) {
        return this.aiAware.symbioticInterface?.explainDecision(decisionId);
      }
    }

    // Preserve original class name
    Object.defineProperty(AIAwareComponent, 'name', {
      value: `AIAware${ComponentClass.name}`,
      configurable: true
    });

    return AIAwareComponent;
  };
}

export default {
  AetherAIAware,
  withAIAwareness,
  AI_AWARENESS_LEVELS,
  AI_ETHICS
};