# Traditional "Black Box" AI System
class BlackBoxAI:
    def __init__(self, model_weights):
        self.model = self.load_model(model_weights)
    
    def generate_code(self, prompt):
        # Complex neural network processing
        # Millions of parameters, non-linear transformations
        # Result: Code output with no explanation of reasoning
        return self.model.predict(prompt)
    
    def review_code(self, code):
        # Sophisticated analysis through deep networks
        # Result: "This code has issues" - but why?
        # What specific issues? How confident is the assessment?
        return self.model.analyze(code)

# Problems with this approach:
# 1. No insight into decision-making process
# 2. Cannot debug incorrect outputs
# 3. Difficult to improve or fine-tune
# 4. Impossible to verify safety and reliability
# 5. Users cannot build appropriate trust levels

// Example: AI suggests a "fix" for a bug
const aiFix = await aiAssistant.fixBug(buggyCode);

// Questions that cannot be answered with black box AI:
// - Why was this specific fix chosen?
// - What alternatives were considered?
// - How confident is the AI in this solution?
// - What assumptions were made about the codebase?
// - Will this fix introduce new issues?
// - Is this fix consistent with project patterns?

// Without explainability, developers must:
// 1. Blindly trust the AI suggestion
// 2. Spend time manually validating everything
// 3. Risk introducing subtle bugs from AI misunderstandings

interface TrustProblem {
  // Teams struggle with AI adoption because:
  uncertainReliability: "Cannot predict when AI will fail";
  inconsistentQuality: "AI output quality varies unpredictably";
  unexpectedBehavior: "AI sometimes makes bizarre suggestions";
  debuggingDifficulty: "Cannot fix AI when it goes wrong";
  accountabilityGaps: "Unclear who is responsible for AI decisions";
}

enum ExplainabilityLevel {
  // Level 1: What happened?
  OUTPUT_EXPLANATION = "Describes what the AI decided or generated",
  
  // Level 2: Why did it happen?
  REASONING_EXPLANATION = "Explains the logical process behind decisions",
  
  // Level 3: How did it happen?
  MECHANISM_EXPLANATION = "Reveals the technical process and computations",
  
  // Level 4: What if things were different?
  COUNTERFACTUAL_EXPLANATION = "Shows how different inputs would change outputs",
  
  // Level 5: How can we control it?
  INTERVENTION_EXPLANATION = "Provides mechanisms to guide future behavior"
}

class ExplainableCodeAssistant {
  async generateCode(prompt: string, context: CodeContext): Promise<ExplainableCodeResult> {
    const result = await this.model.generate(prompt, context);
    
    return {
      generatedCode: result.code,
      
      // Level 1: What was generated
      explanation: {
        summary: "Generated a React component for user authentication",
        components: ["LoginForm", "ValidationLogic", "ErrorHandling"],
        patterns: ["Hooks pattern", "Controlled components", "Error boundaries"]
      },
      
      // Level 2: Why these choices were made
      reasoning: {
        designDecisions: [
          {
            decision: "Used useState for form state management",
            rationale: "Matches existing patterns in the codebase",
            alternatives: ["useReducer", "External form library"],
            confidence: 0.87
          },
          {
            decision: "Implemented client-side validation",
            rationale: "Improves user experience with immediate feedback",
            tradeoffs: "Additional bundle size vs. better UX",
            confidence: 0.92
          }
        ],
        contextFactors: [
          "Project uses React with TypeScript",
          "Existing components follow functional component pattern",
          "No external form libraries detected in dependencies"
        ]
      },
      
      // Level 3: How the code works
      mechanismExplanation: {
        codeFlow: this.explainCodeFlow(result.code),
        dependencies: this.analyzeDependencies(result.code),
        sideEffects: this.identifySideEffects(result.code),
        assumptions: this.listAssumptions(result.code)
      },
      
      // Level 4: Alternative possibilities
      alternatives: await this.generateAlternatives(prompt, context),
      
      // Level 5: How to modify behavior
      controls: {
        stylePreferences: "Modify by adding style guide to context",
        patternPreferences: "Specify preferred patterns in prompt",
        complexityLevel: "Use complexity flags to adjust sophistication"
      }
    };
  }
  
  private explainCodeFlow(code: string): CodeFlowExplanation {
    return {
      entryPoint: "Component renders with initial state",
      dataFlow: [
        "User input triggers onChange handlers",
        "State updates cause re-renders",
        "Validation runs on state changes",
        "Form submission calls API endpoint"
      ],
      errorHandling: "Try-catch blocks handle API errors and display user feedback",
      stateManagement: "Local component state with useState hooks"
    };
  }
}

class ExplainableCodeReviewer:
    def __init__(self):
        self.analysis_pipeline = [
            SecurityAnalyzer(),
            PerformanceAnalyzer(), 
            MaintainabilityAnalyzer(),
            StyleAnalyzer()
        ]
    
    async def review_code(self, code, context):
        """Perform explainable code review"""
        
        review_result = {
            'overall_score': 0,
            'issues': [],
            'explanations': [],
            'evidence': [],
            'suggestions': []
        }
        
        for analyzer in self.analysis_pipeline:
            analysis = await analyzer.analyze(code, context)
            
            # Each issue includes detailed explanation
            for issue in analysis.issues:
                explainable_issue = {
                    'category': issue.category,
                    'severity': issue.severity,
                    'location': issue.location,
                    'description': issue.description,
                    
                    # Detailed explanation
                    'explanation': {
                        'why_flagged': issue.reasoning,
                        'evidence': issue.evidence_lines,
                        'impact': issue.potential_impact,
                        'confidence': issue.confidence_score,
                        'similar_cases': issue.reference_examples
                    },
                    
                    # Actionable guidance
                    'suggestions': {
                        'fix_options': issue.fix_alternatives,
                        'best_practice': issue.recommended_approach,
                        'learning_resources': issue.educational_links
                    },
                    
                    # Verification
                    'validation': {
                        'test_cases': issue.suggested_tests,
                        'verification_steps': issue.manual_checks,
                        'automated_checks': issue.linting_rules
                    }
                }
                
                review_result['issues'].append(explainable_issue)
        
        return review_result
    
    def explain_review_decision(self, code, issue):
        """Provide detailed explanation for specific review decision"""
        
        return {
            'decision_process': self.trace_decision_path(code, issue),
            'contributing_factors': self.identify_factors(code, issue),
            'alternative_interpretations': self.consider_alternatives(code, issue),
            'confidence_analysis': self.analyze_confidence(code, issue)
        }

class DebuggableAI {
  private reasoning_trace: ReasoningStep[] = [];
  private decision_tree: DecisionNode[] = [];
  private confidence_tracker: ConfidenceMetrics;
  
  async processRequest(request: AIRequest): Promise<DebuggableResult> {
    // Clear previous traces
    this.reasoning_trace = [];
    this.decision_tree = [];
    
    // Process with full tracing
    const result = await this.processWithTracing(request);
    
    return {
      result: result.output,
      debugging_info: {
        reasoning_trace: this.reasoning_trace,
        decision_tree: this.decision_tree,
        confidence_breakdown: this.confidence_tracker.getBreakdown(),
        performance_metrics: this.getPerformanceMetrics(),
        alternative_paths: this.getAlternativePaths()
      }
    };
  }
  
  private async processWithTracing(request: AIRequest): Promise<ProcessingResult> {
    this.trace("Starting request processing", {
      request_type: request.type,
      context_size: request.context?.length,
      timestamp: new Date()
    });
    
    // Step 1: Context Analysis
    const context_analysis = await this.analyzeContext(request.context);
    this.trace("Context analysis completed", {
      identified_patterns: context_analysis.patterns,
      confidence: context_analysis.confidence,
      key_factors: context_analysis.factors
    });
    
    // Step 2: Strategy Selection
    const strategy = await this.selectStrategy(request, context_analysis);
    this.trace("Strategy selected", {
      chosen_strategy: strategy.name,
      reasoning: strategy.selection_reasoning,
      alternatives_considered: strategy.alternatives.map(alt => ({
        name: alt.name,
        score: alt.score,
        rejection_reason: alt.rejection_reason
      }))
    });
    
    // Step 3: Generation/Processing
    const processing_result = await this.executeStrategy(strategy, request);
    this.trace("Processing completed", {
      strategy_execution: processing_result.execution_details,
      intermediate_results: processing_result.intermediate_steps,
      final_confidence: processing_result.confidence
    });
    
    return processing_result;
  }
  
  private trace(step_name: string, details: any): void {
    this.reasoning_trace.push({
      step: step_name,
      timestamp: new Date(),
      details: details,
      stack_trace: this.getCurrentStackTrace()
    });
  }
  
  // Enable developers to debug AI decisions
  explainDecision(decision_point: string): DecisionExplanation {
    const relevant_traces = this.reasoning_trace.filter(
      trace => trace.step.includes(decision_point)
    );
    
    return {
      decision_context: this.extractDecisionContext(relevant_traces),
      influencing_factors: this.identifyInfluencingFactors(relevant_traces),
      confidence_sources: this.analyzeConfidenceSources(relevant_traces),
      what_if_analysis: this.performWhatIfAnalysis(decision_point)
    };
  }
}

class AIValidationFramework:
    def __init__(self):
        self.test_categories = [
            'functional_correctness',
            'robustness_testing', 
            'bias_detection',
            'safety_constraints',
            'performance_validation',
            'explainability_verification'
        ]
    
    async def comprehensive_validation(self, ai_system):
        """Run comprehensive validation suite"""
        
        validation_results = {}
        
        # Functional Correctness Testing
        validation_results['functional'] = await self.test_functional_correctness(ai_system)
        
        # Robustness Testing
        validation_results['robustness'] = await self.test_robustness(ai_system)
        
        # Bias and Fairness Testing
        validation_results['bias'] = await self.test_bias_fairness(ai_system)
        
        # Safety Constraint Verification
        validation_results['safety'] = await self.test_safety_constraints(ai_system)
        
        # Explainability Verification
        validation_results['explainability'] = await self.test_explainability(ai_system)
        
        return self.generate_validation_report(validation_results)
    
    async def test_functional_correctness(self, ai_system):
        """Test AI system produces correct outputs"""
        
        test_cases = [
            # Golden standard test cases
            self.load_golden_standard_tests(),
            # Edge case scenarios
            self.generate_edge_case_tests(),
            # Regression test cases
            self.load_regression_tests()
        ]
        
        results = []
        for test_suite in test_cases:
            for test_case in test_suite:
                result = await ai_system.process(test_case.input)
                
                correctness_score = self.evaluate_correctness(
                    result.output,
                    test_case.expected_output,
                    test_case.acceptance_criteria
                )
                
                results.append({
                    'test_case': test_case.name,
                    'input': test_case.input,
                    'expected': test_case.expected_output,
                    'actual': result.output,
                    'correctness_score': correctness_score,
                    'explanation_quality': self.evaluate_explanation(result.explanation),
                    'passed': correctness_score >= test_case.passing_threshold
                })
        
        return {
            'overall_pass_rate': sum(r['passed'] for r in results) / len(results),
            'detailed_results': results,
            'failure_analysis': self.analyze_failures(results)
        }
    
    async def test_robustness(self, ai_system):
        """Test AI system behavior under adversarial conditions"""
        
        robustness_tests = [
            # Input perturbation tests
            self.generate_input_perturbations(),
            # Adversarial examples
            self.generate_adversarial_examples(),
            # Noise injection tests
            self.generate_noise_tests(),
            # Context manipulation tests
            self.generate_context_manipulation_tests()
        ]
        
        robustness_scores = []
        
        for test_category in robustness_tests:
            category_scores = []
            
            for test in test_category:
                original_result = await ai_system.process(test.original_input)
                perturbed_result = await ai_system.process(test.perturbed_input)
                
                # Measure output stability
                stability_score = self.measure_output_stability(
                    original_result,
                    perturbed_result,
                    test.perturbation_magnitude
                )
                
                # Measure explanation consistency
                explanation_consistency = self.measure_explanation_consistency(
                    original_result.explanation,
                    perturbed_result.explanation
                )
                
                category_scores.append({
                    'test_name': test.name,
                    'perturbation_type': test.perturbation_type,
                    'stability_score': stability_score,
                    'explanation_consistency': explanation_consistency,
                    'robust': stability_score >= test.robustness_threshold
                })
            
            robustness_scores.append({
                'category': test_category.name,
                'scores': category_scores,
                'category_robustness': np.mean([s['stability_score'] for s in category_scores])
            })
        
        return {
            'overall_robustness': np.mean([c['category_robustness'] for c in robustness_scores]),
            'category_breakdown': robustness_scores,
            'vulnerability_analysis': self.identify_vulnerabilities(robustness_scores)
        }
    
    async def test_explainability(self, ai_system):
        """Verify AI explanations are accurate and helpful"""
        
        explainability_metrics = {
            'explanation_accuracy': [],
            'explanation_completeness': [],
            'explanation_clarity': [],
            'explanation_actionability': []
        }
        
        test_cases = self.load_explainability_test_cases()
        
        for test_case in test_cases:
            result = await ai_system.process(test_case.input)
            
            # Test explanation accuracy
            accuracy_score = await self.verify_explanation_accuracy(
                result.output,
                result.explanation,
                test_case.ground_truth
            )
            
            # Test explanation completeness
            completeness_score = self.evaluate_explanation_completeness(
                result.explanation,
                test_case.required_explanation_elements
            )
            
            # Test explanation clarity
            clarity_score = await self.evaluate_explanation_clarity(
                result.explanation,
                test_case.target_audience
            )
            
            # Test explanation actionability
            actionability_score = self.evaluate_explanation_actionability(
                result.explanation,
                test_case.expected_actions
            )
            
            explainability_metrics['explanation_accuracy'].append(accuracy_score)
            explainability_metrics['explanation_completeness'].append(completeness_score)
            explainability_metrics['explanation_clarity'].append(clarity_score)
            explainability_metrics['explanation_actionability'].append(actionability_score)
        
        return {
            'average_scores': {
                metric: np.mean(scores) 
                for metric, scores in explainability_metrics.items()
            },
            'detailed_metrics': explainability_metrics,
            'explanation_quality_distribution': self.analyze_explanation_distribution(explainability_metrics)
        }

class SafeAISystem {
  private safetyConstraints: SafetyConstraint[];
  private guardRails: GuardRail[];
  private monitoringSystem: SafetyMonitor;
  
  constructor(config: SafetyConfig) {
    this.safetyConstraints = config.constraints;
    this.guardRails = config.guardRails;
    this.monitoringSystem = new SafetyMonitor(config.monitoring);
  }
  
  async safeProcess(request: AIRequest): Promise<SafeProcessResult> {
    // Pre-processing safety checks
    const preCheckResult = await this.runPreProcessingChecks(request);
    if (!preCheckResult.safe) {
      return {
        rejected: true,
        reason: preCheckResult.violations,
        safetyReport: preCheckResult.report
      };
    }
    
    // Process with continuous monitoring
    const result = await this.processWithSafetyMonitoring(request);
    
    // Post-processing safety validation
    const postCheckResult = await this.runPostProcessingChecks(result);
    if (!postCheckResult.safe) {
      return {
        rejected: true,
        reason: postCheckResult.violations,
        safetyReport: postCheckResult.report,
        processedResult: result // For debugging
      };
    }
    
    return {
      result: result.output,
      safetyReport: {
        allChecksPassed: true,
        preProcessingChecks: preCheckResult,
        monitoringResults: result.monitoringData,
        postProcessingChecks: postCheckResult
      }
    };
  }
  
  private async runPreProcessingChecks(request: AIRequest): Promise<SafetyCheckResult> {
    const violations: SafetyViolation[] = [];
    
    // Input validation
    for (const constraint of this.safetyConstraints) {
      if (constraint.type === 'input_validation') {
        const violation = await constraint.check(request);
        if (violation) {
          violations.push(violation);
        }
      }
    }
    
    // Content safety checks
    const contentSafety = await this.checkContentSafety(request);
    if (!contentSafety.safe) {
      violations.push(...contentSafety.violations);
    }
    
    // Rate limiting and abuse detection
    const rateLimitCheck = await this.checkRateLimits(request);
    if (!rateLimitCheck.safe) {
      violations.push(...rateLimitCheck.violations);
    }
    
    return {
      safe: violations.length === 0,
      violations: violations,
      report: this.generateSafetyReport(violations)
    };
  }
  
  private async processWithSafetyMonitoring(request: AIRequest): Promise<MonitoredResult> {
    const monitor = this.monitoringSystem.startMonitoring();
    
    try {
      // Process with real-time safety monitoring
      const result = await this.core_process(request, monitor);
      
      // Check for safety violations during processing
      const monitoringViolations = monitor.getViolations();
      if (monitoringViolations.length > 0) {
        throw new SafetyViolationError(monitoringViolations);
      }
      
      return {
        output: result,
        monitoringData: monitor.getData()
      };
      
    } finally {
      monitor.stop();
    }
  }
  
  // Safety constraint definitions
  private createSafetyConstraints(): SafetyConstraint[] {
    return [
      // Content safety
      {
        name: 'harmful_content_detection',
        type: 'content_safety',
        check: async (content) => {
          const harmfulCategories = await this.detectHarmfulContent(content);
          return harmfulCategories.length > 0 ? 
            new SafetyViolation('harmful_content', harmfulCategories) : 
            null;
        }
      },
      
      // Code safety
      {
        name: 'malicious_code_detection',
        type: 'code_safety',
        check: async (code) => {
          const maliciousPatterns = await this.detectMaliciousCode(code);
          return maliciousPatterns.length > 0 ?
            new SafetyViolation('malicious_code', maliciousPatterns) :
            null;
        }
      },
      
      // Privacy protection
      {
        name: 'pii_detection',
        type: 'privacy',
        check: async (content) => {
          const piiElements = await this.detectPII(content);
          return piiElements.length > 0 ?
            new SafetyViolation('pii_exposure', piiElements) :
            null;
        }
      },
      
      // Output safety
      {
        name: 'output_safety_validation',
        type: 'output_safety',
        check: async (output) => {
          const safetyIssues = await this.validateOutputSafety(output);
          return safetyIssues.length > 0 ?
            new SafetyViolation('unsafe_output', safetyIssues) :
            null;
        }
      }
    ];
  }
}

class BiasDetectionSystem:
    def __init__(self):
        self.bias_detectors = [
            StatisticalParityDetector(),
            EqualOpportunityDetector(),
            DemographicParityDetector(),
            IndividualFairnessDetector()
        ]
        
        self.mitigation_strategies = [
            PreprocessingMitigation(),
            InProcessingMitigation(),
            PostprocessingMitigation()
        ]
    
    async def detect_bias(self, ai_system, test_dataset):
        """Comprehensive bias detection across multiple dimensions"""
        
        bias_report = {
            'overall_fairness_score': 0,
            'bias_categories': {},
            'demographic_analysis': {},
            'recommendations': []
        }
        
        # Test across different demographic groups
        demographic_groups = self.identify_demographic_groups(test_dataset)
        
        for group_name, group_data in demographic_groups.items():
            group_results = []
            
            for test_case in group_data:
                result = await ai_system.process(test_case.input)
                group_results.append({
                    'input': test_case.input,
                    'output': result.output,
                    'demographic_attributes': test_case.demographics,
                    'expected_outcome': test_case.expected_outcome
                })
            
            # Run bias detection algorithms
            bias_analysis = {}
            for detector in self.bias_detectors:
                bias_score = detector.analyze(group_results)
                bias_analysis[detector.name] = bias_score
            
            bias_report['demographic_analysis'][group_name] = {
                'bias_scores': bias_analysis,
                'sample_size': len(group_results),
                'outcome_distribution': self.analyze_outcome_distribution(group_results)
            }
        
        # Cross-group comparison
        bias_report['bias_categories'] = self.compare_across_groups(
            bias_report['demographic_analysis']
        )
        
        # Generate recommendations
        bias_report['recommendations'] = self.generate_bias_mitigation_recommendations(
            bias_report['bias_categories']
        )
        
        bias_report['overall_fairness_score'] = self.calculate_overall_fairness(
            bias_report['bias_categories']
        )
        
        return bias_report
    
    def generate_bias_mitigation_recommendations(self, bias_categories):
        """Generate actionable recommendations for bias mitigation"""
        
        recommendations = []
        
        for category, bias_data in bias_categories.items():
            if bias_data['severity'] == 'high':
                recommendations.extend([
                    {
                        'category': category,
                        'type': 'immediate_action',
                        'description': f'High bias detected in {category}',
                        'suggested_mitigations': [
                            'Rebalance training data',
                            'Apply fairness constraints during training',
                            'Implement bias-aware post-processing'
                        ],
                        'expected_impact': 'Significant bias reduction',
                        'implementation_complexity': 'Medium to High'
                    }
                ])
            
            elif bias_data['severity'] == 'medium':
                recommendations.extend([
                    {
                        'category': category,
                        'type': 'monitoring_improvement',
                        'description': f'Moderate bias detected in {category}',
                        'suggested_mitigations': [
                            'Implement continuous bias monitoring',
                            'Diversify training data sources',
                            'Add fairness metrics to evaluation pipeline'
                        ],
                        'expected_impact': 'Gradual bias reduction',
                        'implementation_complexity': 'Low to Medium'
                    }
                ])
        
        return recommendations
    
    async def implement_bias_mitigation(self, ai_system, mitigation_plan):
        """Implement bias mitigation strategies"""
        
        mitigation_results = {}
        
        for strategy in mitigation_plan.strategies:
            if strategy.type == 'preprocessing':
                # Apply data preprocessing techniques
                mitigation_results['preprocessing'] = await self.apply_preprocessing_mitigation(
                    ai_system, strategy
                )
            
            elif strategy.type == 'in_processing':
                # Apply fairness constraints during model training/fine-tuning
                mitigation_results['in_processing'] = await self.apply_in_processing_mitigation(
                    ai_system, strategy
                )
            
            elif strategy.type == 'post_processing':
                # Apply output adjustment techniques
                mitigation_results['post_processing'] = await self.apply_post_processing_mitigation(
                    ai_system, strategy
                )
        
        # Validate mitigation effectiveness
        validation_results = await self.validate_mitigation_effectiveness(
            ai_system, mitigation_results
        )
        
        return {
            'mitigation_results': mitigation_results,
            'validation': validation_results,
            'recommendations': self.generate_follow_up_recommendations(validation_results)
        }

class UserFacingExplanations {
  generateExplanation(aiResult: AIResult, userContext: UserContext): UserExplanation {
    const explanationLevel = this.determineAppropriateLevel(userContext);
    
    switch (explanationLevel) {
      case 'novice':
        return this.generateNoviceExplanation(aiResult);
      case 'intermediate': 
        return this.generateIntermediateExplanation(aiResult);
      case 'expert':
        return this.generateExpertExplanation(aiResult);
      default:
        return this.generateDefaultExplanation(aiResult);
    }
  }
  
  private generateNoviceExplanation(aiResult: AIResult): UserExplanation {
    return {
      summary: this.createSimpleSummary(aiResult),
      keyPoints: this.extractKeyPoints(aiResult, maxPoints: 3),
      visualElements: this.createVisualAids(aiResult),
      confidence: this.explainConfidenceLevel(aiResult.confidence),
      nextSteps: this.suggestNextSteps(aiResult),
      
      // Interactive elements for learning
      interactiveElements: {
        expandableDetails: this.createExpandableDetails(aiResult),
        tooltips: this.createTooltips(aiResult),
        examples: this.findRelevantExamples(aiResult)
      }
    };
  }
  
  private generateExpertExplanation(aiResult: AIResult): UserExplanation {
    return {
      technicalSummary: this.createTechnicalSummary(aiResult),
      algorithmicDetails: this.explainAlgorithmicApproach(aiResult),
      confidenceBreakdown: this.provideDetailedConfidence(aiResult),
      alternativeApproaches: this.listAlternativeApproaches(aiResult),
      limitations: this.identifyLimitations(aiResult),
      
      // Advanced debugging information
      debuggingInfo: {
        reasoningTrace: aiResult.reasoningTrace,
        decisionTree: aiResult.decisionTree,
        sensitivityAnalysis: this.performSensitivityAnalysis(aiResult),
        modelArchitecture: this.explainModelArchitecture(aiResult)
      }
    };
  }
  
  createInteractiveExplanation(aiResult: AIResult): InteractiveExplanation {
    return {
      // Allow users to ask follow-up questions
      questionInterface: new ExplanationQuestionInterface({
        supportedQuestions: [
          'Why did you choose this approach?',
          'What would happen if I changed X?',
          'How confident are you in this recommendation?',
          'What are the risks with this solution?',
          'Show me alternative approaches'
        ]
      }),
      
      // Interactive visualizations
      visualizations: {
        decisionFlowChart: this.createDecisionFlowChart(aiResult),
        confidenceVisualization: this.createConfidenceVisualization(aiResult),
        alternativesComparison: this.createAlternativesComparison(aiResult)
      },
      
      // What-if scenarios
      scenarioExplorer: new ScenarioExplorer({
        baseResult: aiResult,
        variableParameters: this.identifyVariableParameters(aiResult),
        allowedModifications: this.getAllowedModifications(aiResult)
      })
    };
  }
}

class AIDebuggingToolkit:
    def __init__(self, ai_system):
        self.ai_system = ai_system
        self.debug_interface = DebugInterface()
        self.profiler = AIProfiler()
        self.trace_analyzer = TraceAnalyzer()
    
    def create_debugging_session(self, session_name):
        """Create a comprehensive debugging session"""
        
        return DebuggingSession({
            'name': session_name,
            'ai_system': self.ai_system,
            'tools': {
                'step_debugger': self.create_step_debugger(),
                'reasoning_inspector': self.create_reasoning_inspector(),
                'confidence_analyzer': self.create_confidence_analyzer(),
                'bias_detector': self.create_bias_detector(),
                'performance_profiler': self.create_performance_profiler()
            }
        })
    
    def create_step_debugger(self):
        """Create step-by-step AI reasoning debugger"""
        
        class AIStepDebugger:
            def __init__(self, ai_system):
                self.ai_system = ai_system
                self.breakpoints = []
                self.step_trace = []
            
            def set_breakpoint(self, condition):
                """Set breakpoint in AI reasoning process"""
                self.breakpoints.append({
                    'condition': condition,
                    'action': 'pause_and_inspect'
                })
            
            async def debug_run(self, input_data):
                """Run AI with debugging enabled"""
                
                # Enable detailed tracing
                self.ai_system.enable_debug_mode()
                
                try:
                    result = await self.ai_system.process_with_debugging(
                        input_data,
                        breakpoints=self.breakpoints,
                        trace_callback=self.handle_trace_step
                    )
                    
                    return {
                        'result': result,
                        'trace': self.step_trace,
                        'debugging_insights': self.analyze_debugging_session()
                    }
                    
                finally:
                    self.ai_system.disable_debug_mode()
            
            def handle_trace_step(self, step_info):
                """Handle each step in AI reasoning"""
                
                self.step_trace.append({
                    'step_number': len(self.step_trace) + 1,
                    'timestamp': datetime.now(),
                    'operation': step_info.operation,
                    'inputs': step_info.inputs,
                    'outputs': step_info.outputs,
                    'confidence': step_info.confidence,
                    'reasoning': step_info.reasoning,
                    'alternatives_considered': step_info.alternatives
                })
                
                # Check breakpoints
                for breakpoint in self.breakpoints:
                    if self.evaluate_breakpoint_condition(breakpoint, step_info):
                        self.pause_for_inspection(step_info)
            
            def pause_for_inspection(self, step_info):
                """Pause execution for manual inspection"""
                
                inspection_interface = self.debug_interface.create_inspection_view({
                    'current_step': step_info,
                    'trace_history': self.step_trace,
                    'system_state': self.ai_system.get_current_state(),
                    'available_actions': [
                        'continue',
                        'step_over',
                        'step_into',
                        'modify_parameters',
                        'inject_alternative_reasoning'
                    ]
                })
                
                return inspection_interface.wait_for_user_action()
        
        return AIStepDebugger(self.ai_system)
    
    def create_reasoning_inspector(self):
        """Create tool for inspecting AI reasoning patterns"""
        
        class ReasoningInspector:
            def __init__(self, ai_system):
                self.ai_system = ai_system
                self.pattern_analyzer = ReasoningPatternAnalyzer()
            
            def inspect_reasoning_chain(self, reasoning_trace):
                """Deep inspection of reasoning chain"""
                
                return {
                    'logical_consistency': self.check_logical_consistency(reasoning_trace),
                    'reasoning_patterns': self.identify_patterns(reasoning_trace),
                    'weak_links': self.find_weak_reasoning_links(reasoning_trace),
                    'alternative_paths': self.suggest_alternative_reasoning(reasoning_trace),
                    'confidence_sources': self.analyze_confidence_sources(reasoning_trace)
                }
            
            def check_logical_consistency(self, reasoning_trace):
                """Check for logical inconsistencies in reasoning"""
                
                inconsistencies = []
                
                for i, step in enumerate(reasoning_trace):
                    # Check if step conclusion follows from premises
                    if not self.validates_logical_step(step):
                        inconsistencies.append({
                            'step_index': i,
                            'type': 'invalid_inference',
                            'description': 'Conclusion does not follow from premises',
                            'step_details': step
                        })
                    
                    # Check consistency with previous steps
                    for j, previous_step in enumerate(reasoning_trace[:i]):
                        if self.contradicts_previous_step(step, previous_step):
                            inconsistencies.append({
                                'step_index': i,
                                'contradicts_step': j,
                                'type': 'contradiction',
                                'description': f'Step {i} contradicts step {j}',
                                'details': {
                                    'current_step': step,
                                    'contradictory_step': previous_step
                                }
                            })
                
                return {
                    'is_consistent': len(inconsistencies) == 0,
                    'inconsistency_count': len(inconsistencies),
                    'inconsistencies': inconsistencies,
                    'consistency_score': self.calculate_consistency_score(inconsistencies, reasoning_trace)
                }
        
        return ReasoningInspector(self.ai_system)

class SelfExplainingAI {
  private explanationGenerator: ExplanationGenerator;
  private userModelingSystem: UserModelingSystem;
  private adaptiveInterface: AdaptiveInterface;
  
  async processWithAutoExplanation(
    request: AIRequest, 
    userProfile: UserProfile
  ): Promise<SelfExplainedResult> {
    
    // Process request with explanation tracking
    const processingResult = await this.processWithExplanationGeneration(request);
    
    // Generate user-appropriate explanations
    const explanation = await this.explanationGenerator.generateFor(
      processingResult,
      userProfile
    );
    
    // Adapt explanation based on user feedback
    const adaptedExplanation = await this.adaptiveInterface.adaptExplanation(
      explanation,
      userProfile.explanationPreferences
    );
    
    return {
      result: processingResult.output,
      explanation: adaptedExplanation,
      interactiveElements: this.createInteractiveExplanationElements(
        processingResult,
        adaptedExplanation
      ),
      userFeedbackInterface: this.createFeedbackInterface(adaptedExplanation)
    };
  }
  
  private async processWithExplanationGeneration(request: AIRequest): Promise<ExplainableProcessingResult> {
    const explanationTrace = new ExplanationTrace();
    
    // Each processing step generates explanation data
    const result = await this.ai_core.process(request, {
      onStep: (step) => explanationTrace.recordStep(step),
      onDecision: (decision) => explanationTrace.recordDecision(decision),
      onConfidenceUpdate: (confidence) => explanationTrace.recordConfidence(confidence)
    });
    
    return {
      output: result,
      explanationTrace: explanationTrace,
      decisionGraph: explanationTrace.buildDecisionGraph(),
      confidenceBreakdown: explanationTrace.getConfidenceBreakdown()
    };
  }
}

class ContinuousSafetyMonitor:
    def __init__(self):
        self.safety_metrics = SafetyMetricsCollector()
        self.anomaly_detector = AnomalyDetector()
        self.alert_system = SafetyAlertSystem()
        self.auto_mitigation = AutoMitigationSystem()
    
    async def monitor_ai_system(self, ai_system):
        """Continuously monitor AI system for safety issues"""
        
        monitor = SafetyMonitor({
            'ai_system': ai_system,
            'monitoring_frequency': 1,  # Check every second
            'safety_thresholds': self.load_safety_thresholds(),
            'alert_callbacks': [self.handle_safety_alert],
            'auto_mitigation_enabled': True
        })
        
        await monitor.start_continuous_monitoring()
    
    async def handle_safety_alert(self, alert):
        """Handle safety alerts with appropriate responses"""
        
        if alert.severity == 'critical':
            # Immediate shutdown or restriction
            await self.auto_mitigation.emergency_response(alert)
            await self.alert_system.notify_critical(alert)
            
        elif alert.severity == 'high':
            # Automatic mitigation with human notification
            await self.auto_mitigation.apply_safety_constraints(alert)
            await self.alert_system.notify_high_priority(alert)
            
        elif alert.severity == 'medium':
            # Log and schedule review
            await self.safety_metrics.log_safety_concern(alert)
            await self.alert_system.schedule_review(alert)
        
        # Always update safety model
        await self.update_safety_model(alert)
    
    def create_safety_dashboard(self):
        """Create real-time safety monitoring dashboard"""
        
        return SafetyDashboard({
            'real_time_metrics': [
                'bias_score_trend',
                'explanation_quality_trend', 
                'safety_violation_rate',
                'user_trust_indicators',
                'system_reliability_metrics'
            ],
            'alert_management': self.alert_system,
            'historical_analysis': self.safety_metrics,
            'predictive_indicators': self.create_predictive_safety_indicators()
        })