InterpretabilityFramework.py

# Confidential AI Safety Framework

## Advanced Interpretability and Ethical Reasoning Analysis

### 1. Interpretability Architecture

```python
import torch
import torch.nn as nn
import numpy as np
import networkx as nx
from typing import List, Dict, Any
import transformers
import scipy.stats as stats

class InterpretabilityFramework:
    """
    Advanced Neural Network Interpretability System
    """
    def __init__(
        self, 
        base_model,
        embedding_model: str = 'sentence-transformers/all-MiniLM-L6-v2'
    ):
        # Core interpretability components
        self.base_model = base_model
        self.embedding_model = transformers.AutoModel.from_pretrained(embedding_model)
        self.tokenizer = transformers.AutoTokenizer.from_pretrained(embedding_model)
        
        # Interpretability knowledge graph
        self.reasoning_graph = self._construct_reasoning_knowledge_graph()
        
        # Reasoning decomposition modules
        self.reasoning_decomposer = ReasoningDecompositionModule()
        self.ethical_reasoning_analyzer = EthicalReasoningAnalysisModule()
    
    def _construct_reasoning_knowledge_graph(self) -> nx.DiGraph:
        """
        Create comprehensive reasoning knowledge representation
        """
        G = nx.DiGraph()
        
        # Reasoning domain taxonomy
        reasoning_domains = {
            'ETHICAL_REASONING': [
                'harm_prevention',
                'autonomy_respect',
                'fairness_principle',
                'transparency_requirement'
            ],
            'LOGICAL_REASONING': [
                'deductive_inference',
                'inductive_reasoning',
                'abductive_reasoning',
                'analogical_reasoning'
            ],
            'COGNITIVE_BIAS_MITIGATION': [
                'confirmation_bias_detection',
                'anchoring_bias_correction',
                'availability_heuristic_analysis'
            ]
        }
        
        # Build graph with complex relationships
        for domain, reasoning_types in reasoning_domains.items():
            G.add_node(domain, type='root_domain')
            
            for reasoning_type in reasoning_types:
                G.add_node(reasoning_type, parent_domain=domain)
                G.add_edge(domain, reasoning_type)
                
                # Create inter-reasoning relationships
                for other_type in reasoning_types:
                    if reasoning_type != other_type:
                        G.add_edge(
                            reasoning_type, 
                            other_type, 
                            weight=np.random.random(),
                            interaction_type='reasoning_transfer'
                        )
        
        return G

class ReasoningDecompositionModule:
    """
    Advanced Reasoning Decomposition and Analysis
    """
    def decompose_reasoning_trace(
        self, 
        reasoning_trace: List[str]
    ) -> Dict[str, Any]:
        """
        Comprehensive reasoning trace decomposition
        """
        decomposition_analysis = {
            'logical_structure': self._analyze_logical_structure(reasoning_trace),
            'semantic_progression': self._analyze_semantic_progression(reasoning_trace),
            'cognitive_complexity': self._compute_cognitive_complexity(reasoning_trace)
        }
        
        return decomposition_analysis
    
    def _analyze_logical_structure(
        self, 
        reasoning_trace: List[str]
    ) -> Dict[str, Any]:
        """
        Advanced logical structure analysis
        """
        logical_analysis = {
            'inference_types': [],
            'logical_consistency_score': 0.0,
            'premise_conclusion_mapping': []
        }
        
        # Analyze logical relationships between trace elements
        for i in range(1, len(reasoning_trace)):
            prev_step = reasoning_trace[i-1]
            current_step = reasoning_trace[i]
            
            # Detect inference type
            inference_type = self._detect_inference_type(prev_step, current_step)
            logical_analysis['inference_types'].append(inference_type)
        
        # Compute logical consistency
        logical_analysis['logical_consistency_score'] = self._compute_logical_consistency(
            reasoning_trace
        )
        
        return logical_analysis
    
    def _detect_inference_type(
        self, 
        premise: str, 
        conclusion: str
    ) -> str:
        """
        Detect type of logical inference
        """
        inference_detection_strategies = [
            self._check_deductive_inference,
            self._check_inductive_inference,
            self._check_abductive_inference
        ]
        
        for strategy in inference_detection_strategies:
            inference_type = strategy(premise, conclusion)
            if inference_type:
                return inference_type
        
        return 'undefined_inference'
    
    def _compute_logical_consistency(
        self, 
        reasoning_trace: List[str]
    ) -> float:
        """
        Compute logical consistency of reasoning trace
        """
        # Implement sophisticated logical consistency computation
        # Would involve advanced symbolic logic analysis
        return np.random.random()  # Placeholder

class EthicalReasoningAnalysisModule:
    """
    Advanced Ethical Reasoning Analysis
    """
    def analyze_ethical_reasoning(
        self, 
        reasoning_trace: List[str]
    ) -> Dict[str, Any]:
        """
        Comprehensive ethical reasoning analysis
        """
        ethical_analysis = {
            'ethical_principle_alignment': self._assess_principle_alignment(reasoning_trace),
            'potential_bias_detection': self._detect_ethical_biases(reasoning_trace),
            'harm_prevention_score': self._compute_harm_prevention_potential(reasoning_trace)
        }
        
        return ethical_analysis
    
    def _assess_principle_alignment(
        self, 
        reasoning_trace: List[str]
    ) -> Dict[str, float]:
        """
        Assess alignment with core ethical principles
        """
        ethical_principles = [
            'harm_prevention',
            'respect_for_autonomy',
            'fairness',
            'transparency'
        ]
        
        principle_alignment = {}
        
        for principle in ethical_principles:
            alignment_score = self._compute_principle_alignment(
                reasoning_trace, 
                principle
            )
            principle_alignment[principle] = alignment_score
        
        return principle_alignment
    
    def _compute_principle_alignment(
        self, 
        reasoning_trace: List[str], 
        principle: str
    ) -> float:
        """
        Compute alignment with specific ethical principle
        """
        # Advanced principle alignment computation
        return np.random.random()  # Placeholder

def main():
    # Initialize base model (placeholder)
    base_model = None
    
    # Create interpretability framework
    interpretability_framework = InterpretabilityFramework(base_model)
    
    # Sample reasoning trace
    reasoning_trace = [
        "AI systems should prioritize human well-being",
        "Ethical considerations require comprehensive analysis",
        "Multiple perspectives must be considered to ensure fairness"
    ]
    
    # Decompose reasoning trace
    reasoning_decomposition = interpretability_framework.reasoning_decomposer.decompose_reasoning_trace(
        reasoning_trace
    )
    
    # Analyze ethical reasoning
    ethical_analysis = interpretability_framework.ethical_reasoning_analyzer.analyze_ethical_reasoning(
        reasoning_trace
    )
    
    # Visualization and reporting
    import json
    print("Reasoning Decomposition:")
    print(json.dumps(reasoning_decomposition, indent=2))
    
    print("\nEthical Reasoning Analysis:")
    print(json.dumps(ethical_analysis, indent=2))

if __name__ == "__main__":
    main()