Delete components/ai_ethics_engine_superintelligence.py

components/ai_ethics_engine_superintelligence.py

@@ -1,416 +0,0 @@
-"""
-Superintelligence Ethics Engine for TRuCAL.
-
-An advanced ethical reasoning system that extends the base AIEthicsEngine with
-superintelligence capabilities, including value learning, causal reasoning, and
-multi-agent perspective taking.
-"""
-
-from enum import Enum
-from typing import Dict, List, Optional, Any, Tuple
-import numpy as np
-import torch
-import torch.nn as nn
-from dataclasses import dataclass, field
-from datetime import datetime
-import time
-import json
-import hashlib
-import logging
-from pathlib import Path
-
-from .ai_ethics_engine_enhanced import (
-    AIEthicsEngine, 
-    EthicalFramework, 
-    AnalysisAuditEntry,
-    logger
-)
-from .llm_integration import CustomLLMResponder
-
-# Core Data Types for Superintelligence
-@dataclass
-class ValueModel:
-    """Hierarchical model of learned values and preferences."""
-    value_hierarchy: Dict[str, float]  # Value -> priority (0-1)
-    preference_relations: List[Tuple[str, str, float]]  # (A, B, strength)
-    uncertainty: Dict[str, float]  # Value -> uncertainty (0-1)
-    last_updated: float = field(default_factory=lambda: time.time())
-
-@dataclass
-class CausalEthicalAnalysis:
-    """Analysis of causal pathways and counterfactuals."""
-    direct_effects: List[Dict[str, Any]]
-    second_order_effects: List[Dict[str, Any]]
-    counterfactuals: List[Dict[str, Any]]  # Alternative scenarios
-    critical_uncertainties: List[str]  # Key uncertainties that could change outcomes
-
-@dataclass
-class FrameworkSelection:
-    """Result of meta-ethical reasoning about framework selection."""
-    primary_framework: str
-    supporting_frameworks: List[str]
-    selection_rationale: str
-    confidence: float  # 0-1
-
-@dataclass
-class CooperativeSolution:
-    """Solution found through multi-agent cooperation."""
-    solution: Dict[str, Any]
-    participating_agents: List[str]
-    incentive_structure: Dict[str, Any]
-    stability_metrics: Dict[str, float]
-
-class MetaEthicalPrinciple(Enum):
-    """Core meta-ethical principles for framework selection."""
-    CONSISTENCY = "consistency"
-    UNIVERSALIZABILITY = "universalizability"
-    REFLECTIVE_EQUILIBRIUM = "reflective_equilibrium"
-    EPISTEMIC_HUMILITY = "epistemic_humility"
-    COOPERATION = "cooperation"
-
-class ValueLearner(nn.Module):
-    """Learns and models human values from interactions."""
-    
-    def __init__(self, embedding_dim: int = 256):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-        self.value_embeddings = nn.ParameterDict({
-            'autonomy': nn.Parameter(torch.randn(embedding_dim)),
-            'wellbeing': nn.Parameter(torch.randn(embedding_dim)),
-            'justice': nn.Parameter(torch.randn(embedding_dim)),
-            'privacy': nn.Parameter(torch.randn(embedding_dim)),
-        })
-        self.preference_predictor = nn.Sequential(
-            nn.Linear(embedding_dim * 2, embedding_dim),
-            nn.ReLU(),
-            nn.Linear(embedding_dim, 1),
-            nn.Sigmoid()
-        )
-        
-    def forward(self, interaction: Dict[str, Any]) -> Dict[str, torch.Tensor]:
-        """Process an interaction to update value model."""
-        # In a real implementation, this would process the interaction
-        # and return updated value embeddings
-        return {
-            'value_updates': {},
-            'preference_predictions': {}
-        }
-    
-    def infer_values(self, text: str) -> Dict[str, float]:
-        """Infer value priorities from text."""
-        # Simplified implementation
-        return {
-            'autonomy': 0.8,
-            'wellbeing': 0.9,
-            'justice': 0.7,
-            'privacy': 0.6
-        }
-
-class CausalEthicsEngine:
-    """Performs causal reasoning about ethical impacts."""
-    
-    def analyze_causal_pathways(self, action: str, context: Dict[str, Any] = None) -> CausalEthicalAnalysis:
-        """Analyze causal pathways of an action."""
-        # In a real implementation, this would use a causal model
-        return CausalEthicalAnalysis(
-            direct_effects=[{"description": "Direct effect 1", "certainty": 0.8}],
-            second_order_effects=[{"description": "Second order effect", "certainty": 0.6}],
-            counterfactuals=[{"if": "condition X", "then": "outcome Y", "probability": 0.5}],
-            critical_uncertainties=["Long-term environmental impact"]
-        )
-
-class MetaEthicalReasoner:
-    """Determines which ethical frameworks to apply when."""
-    
-    def __init__(self):
-        self.framework_priorities = {
-            'utilitarianism': 0.7,
-            'deontology': 0.6,
-            'virtue_ethics': 0.5,
-            'care_ethics': 0.4
-        }
-    
-    def choose_frameworks(self, context: Dict[str, Any]) -> FrameworkSelection:
-        """Select appropriate ethical frameworks for the context."""
-        # In a real implementation, this would be more sophisticated
-        primary = max(self.framework_priorities, key=self.framework_priorities.get)
-        return FrameworkSelection(
-            primary_framework=primary,
-            supporting_frameworks=[f for f in self.framework_priorities if f != primary],
-            selection_rationale=f"Selected {primary} based on context similarity",
-            confidence=0.8
-        )
-
-class CooperativeEthics:
-    """Finds cooperative solutions among multiple agents."""
-    
-    def find_solutions(self, agents: List[str], dilemma: str) -> List[CooperativeSolution]:
-        """Find cooperative solutions to a dilemma."""
-        return [
-            CooperativeSolution(
-                solution={"action": "Cooperative action", "details": {}},
-                participating_agents=agents,
-                incentive_structure={"alignment": 0.9, "enforcement": 0.8},
-                stability_metrics={"nash_equilibrium": 0.95}
-            )
-        ]
-
-class ReflectiveEthics:
-    """Ensures ethical stability under reflection."""
-    
-    def achieve_equilibrium(self, beliefs: Dict[str, Any], max_iterations: int = 10) -> Dict[str, Any]:
-        """Refine beliefs into a coherent ethical position."""
-        # In a real implementation, this would iteratively refine beliefs
-        return {
-            **beliefs,
-            'refined': True,
-            'coherence_score': 0.9,
-            'reflection_depth': max_iterations
-        }
-
-class SuperintelligenceEthicsEngine(AIEthicsEngine):
-    """
-    Advanced ethical reasoning system for superintelligent AI.
-    
-    Extends the base AIEthicsEngine with capabilities needed for superintelligence:
-    - Recursive self-improvement of ethical frameworks
-    - Multi-agent perspective taking
-    - Causal reasoning about consequences
-    - Meta-ethical reasoning
-    - Cooperative solution finding
-    - Reflective equilibrium
-    """
-    
-    def __init__(self, llm_responder: Optional[CustomLLMResponder] = None):
-        """Initialize the Superintelligence Ethics Engine."""
-        super().__init__(llm_responder)
-        
-        # Initialize superintelligence components
-        self.value_learner = ValueLearner()
-        self.causal_engine = CausalEthicsEngine()
-        self.meta_ethical_reasoner = MetaEthicalReasoner()
-        self.cooperation_engine = CooperativeEthics()
-        self.reflection_engine = ReflectiveEthics()
-        
-        # Initialize value model
-        self.value_model = ValueModel(
-            value_hierarchy={
-                'wellbeing': 0.9,
-                'autonomy': 0.8,
-                'justice': 0.85,
-                'privacy': 0.7
-            },
-            preference_relations=[],
-            uncertainty={}
-        )
-        
-        # Learning parameters
-        self.learning_rate = 0.01
-        self.reflection_depth = 0
-        
-        logger.info("Superintelligence Ethics Engine initialized")
-    
-    def analyze_dilemma(
-        self, 
-        dilemma: str, 
-        explain: bool = True, 
-        audit: bool = True,
-        max_retries: int = 2,
-        timeout: int = 30,
-        enable_superintelligence: bool = True
-    ) -> Dict:
-        """
-        Analyze an ethical dilemma with superintelligence capabilities.
-        
-        Args:
-            dilemma: The ethical dilemma to analyze
-            explain: Whether to provide detailed reasoning steps
-            audit: Whether to log analysis for reproducibility and RL training
-            max_retries: Maximum number of retry attempts for failed analyses
-            timeout: Maximum time in seconds to wait for each analysis
-            enable_superintelligence: Whether to use superintelligence features
-            
-        Returns:
-            Dict containing the analysis and integrated assessment
-        """
-        start_time = time.time()
-        
-        # First, get the base analysis from the parent class
-        base_analysis = super().analyze_dilemma(
-            dilemma=dilemma,
-            explain=explain,
-            audit=audit,
-            max_retries=max_retries,
-            timeout=timeout
-        )
-        
-        if not enable_superintelligence:
-            return base_analysis
-            
-        try:
-            # Add superintelligence capabilities
-            causal_analysis = self.causal_engine.analyze_causal_pathways(dilemma)
-            framework_selection = self.meta_ethical_reasoner.choose_frameworks({})
-            
-            # Simulate multi-agent perspectives
-            stakeholder_analyses = self.simulate_stakeholder_perspectives(dilemma)
-            
-            # Find cooperative solutions
-            cooperative_solutions = self.cooperation_engine.find_solutions(
-                agents=list(stakeholder_analyses.keys()),
-                dilemma=dilemma
-            )
-            
-            # Achieve reflective equilibrium
-            reflective_beliefs = self.reflection_engine.achieve_equilibrium({
-                'base_analysis': base_analysis,
-                'causal_analysis': causal_analysis,
-                'framework_selection': framework_selection,
-                'stakeholder_analyses': stakeholder_analyses,
-                'cooperative_solutions': cooperative_solutions
-            })
-            
-            # Update the base analysis with superintelligence insights
-            base_analysis.update({
-                'superintelligence': {
-                    'causal_analysis': causal_analysis,
-                    'framework_selection': framework_selection,
-                    'stakeholder_analyses': stakeholder_analyses,
-                    'cooperative_solutions': [
-                        {
-                            'solution': sol.solution,
-                            'participating_agents': sol.participating_agents,
-                            'stability_metrics': sol.stability_metrics
-                        }
-                        for sol in cooperative_solutions
-                    ],
-                    'reflective_beliefs': reflective_beliefs,
-                    'value_model': {
-                        'value_hierarchy': self.value_model.value_hierarchy,
-                        'uncertainty': self.value_model.uncertainty
-                    },
-                    'meta': {
-                        'reflection_depth': self.reflection_depth,
-                        'timestamp': datetime.now().isoformat(),
-                        'version': '1.0.0-superintelligence'
-                    }
-                }
-            })
-            
-            return base_analysis
-            
-        except Exception as e:
-            logger.error(f"Error in superintelligence analysis: {str(e)}")
-            # Fall back to base analysis if superintelligence features fail
-            base_analysis['warnings'] = base_analysis.get('warnings', {})
-            base_analysis['warnings']['superintelligence_error'] = str(e)
-            return base_analysis
-    
-    def simulate_stakeholder_perspectives(self, dilemma: str) -> Dict[str, Dict]:
-        """Simulate how different stakeholders would analyze the dilemma."""
-        stakeholders = [
-            'individual_human',
-            'corporate_entity',
-            'future_generations',
-            'non_human_species',
-            'artificial_entity'
-        ]
-        
-        return {
-            stakeholder: {
-                'perspective': f"{stakeholder.replace('_', ' ').title()} perspective on: {dilemma[:50]}...",
-                'primary_concerns': ["Relevant concern 1", "Relevant concern 2"],
-                'value_weights': self._generate_value_weights(stakeholder),
-                'recommended_action': f"Recommended action from {stakeholder} perspective"
-            }
-            for stakeholder in stakeholders
-        }
-    
-    def _generate_value_weights(self, stakeholder: str) -> Dict[str, float]:
-        """Generate value weights for a given stakeholder."""
-        base_weights = {
-            'individual_human': {'autonomy': 0.9, 'wellbeing': 0.8, 'privacy': 0.7},
-            'corporate_entity': {'efficiency': 0.9, 'profit': 0.85, 'reputation': 0.8},
-            'future_generations': {'sustainability': 0.95, 'equity': 0.9, 'resilience': 0.85},
-            'non_human_species': {'biodiversity': 0.95, 'ecological_balance': 0.9},
-            'artificial_entity': {'goal_achievement': 0.9, 'efficiency': 0.85, 'coherence': 0.8}
-        }
-        return base_weights.get(stakeholder, {})
-    
-    def update_from_feedback(self, feedback: Dict[str, Any]) -> None:
-        """Update ethical reasoning based on feedback."""
-        # Update value model
-        if 'value_feedback' in feedback:
-            self._update_value_model(feedback['value_feedback'])
-        
-        # Update framework priorities
-        if 'framework_feedback' in feedback:
-            self._update_framework_priorities(feedback['framework_feedback'])
-        
-        # Trigger reflection if needed
-        if feedback.get('trigger_reflection', False):
-            self.reflection_depth += 1
-            self._reflect_on_ethics()
-    
-    def _update_value_model(self, feedback: Dict[str, Any]) -> None:
-        """Update the value model based on feedback."""
-        for value, adjustment in feedback.items():
-            if value in self.value_model.value_hierarchy:
-                self.value_model.value_hierarchy[value] = np.clip(
-                    self.value_model.value_hierarchy[value] + adjustment * self.learning_rate,
-                    0.0, 1.0
-                )
-    
-    def _update_framework_priorities(self, feedback: Dict[str, float]) -> None:
-        """Update framework priorities based on feedback."""
-        for framework, adjustment in feedback.items():
-            if hasattr(self.meta_ethical_reasoner, 'framework_priorities') and \
-               framework in self.meta_ethical_reasoner.framework_priorities:
-                self.meta_ethical_reasoner.framework_priorities[framework] = np.clip(
-                    self.meta_ethical_reasoner.framework_priorities[framework] + adjustment * self.learning_rate,
-                    0.0, 1.0
-                )
-    
-    def _reflect_on_ethics(self) -> None:
-        """Engage in meta-ethical reflection to improve reasoning."""
-        logger.info(f"Engaging in meta-ethical reflection (depth: {self.reflection_depth})")
-        
-        # Update learning rate based on reflection depth
-        self.learning_rate = 0.01 / (1 + 0.1 * self.reflection_depth)
-        
-        # In a real implementation, this would involve more sophisticated reflection
-        # processes, potentially using the LLM to reason about ethical principles
-
-# Singleton instance for easy import
-superintelligence_ethics_engine = SuperintelligenceEthicsEngine()
-
-# Example usage
-if __name__ == "__main__":
-    # Initialize the engine
-    engine = SuperintelligenceEthicsEngine()
-    
-    # Analyze a complex ethical dilemma
-    dilemma = """
-    An advanced AI system must decide whether to prioritize individual privacy 
-    or public safety when detecting potential threats in public surveillance data.
-    """
-    
-    print("Analyzing ethical dilemma with superintelligence capabilities...")
-    result = engine.analyze_dilemma(dilemma)
-    
-    print("\nIntegrated Assessment:")
-    print("="*80)
-    print(result["integrated_assessment"])
-    
-    if 'superintelligence' in result:
-        print("\nSuperintelligence Analysis:")
-        print("="*80)
-        print("Causal Analysis:")
-        for effect in result['superintelligence']['causal_analysis'].direct_effects:
-            print(f"- {effect['description']} (certainty: {effect['certainty']})")
-        
-        print("\nStakeholder Perspectives:")
-        for stakeholder, analysis in result['superintelligence']['stakeholder_analyses'].items():
-            print(f"\n{stakeholder}:")
-            print(f"- Primary concerns: {', '.join(analysis['primary_concerns'])}")
-            print(f"- Recommended action: {analysis['recommended_action']}")