MA’AT Ethical AI IamWHOIam Thysself
import numpy as np
import pandas as pd
from scipy.special import softmax
from datetime import datetime, timedelta
import requests
import logging
from typing import Dict, Tuple, List, Optional
from dataclasses import dataclass
import warnings
import asyncio
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
@dataclass
class ValidationResult:
"""Structured validation result with error tracking"""
is_valid: bool
value: float
errors: List[str]
warnings: List[str]
class CosmicPositioningAPI:
"""Real-time cosmic positioning interface"""
def __init__(self, api_key: str, cache_duration: int = 3600):
self.api_key = api_key
self.cache_duration = cache_duration
self.cache = {}
self.logger = logging.getLogger(__name__)
async def get_galactic_position(self) -> Dict[str, float]:
"""Fetch real-time galactic positioning data"""
try:
# Check cache first
now = datetime.now()
if 'timestamp' in self.cache:
if (now - self.cache['timestamp']).seconds < self.cache_duration:
return self.cache['data']
# API endpoints (example URLs - replace with actual services)
endpoints = {
'solar_system': f"<https://api.solarsystemapi.com/position?key={self.api_key}>",
'galactic': f"<https://api.galacticposition.org/current?key={self.api_key}>",
'lunar': f"<https://api.lunarphases.com/current?key={self.api_key}>"
}
position_data = {}
for source, url in endpoints.items():
try:
response = requests.get(url, timeout=5)
if response.status_code == 200:
data = response.json()
position_data[source] = data
except Exception as e:
self.logger.warning(f"Failed to fetch {source} data: {e}")
# Fallback to calculated values
position_data[source] = self._calculate_fallback_position(source)
# Calculate composite galactic positioning
galactic_metrics = self._process_position_data(position_data)
# Cache result
self.cache = {
'timestamp': now,
'data': galactic_metrics
}
return galactic_metrics
except Exception as e:
self.logger.error(f"Cosmic positioning API error: {e}")
return self._calculate_fallback_position()
def _calculate_fallback_position(self, source: str = "galactic") -> Dict[str, float]:
"""Fallback cosmic positioning calculation"""
now = datetime.now()
# Ancient astronomical calculations
julian_day = (now - datetime(2000, 1, 1)).days + 2451545.0
lunar_cycle = (julian_day % 29.53059) / 29.53059
solar_year = (now.timetuple().tm_yday - 1) / 365.25
# Galactic year approximation (225-250 million Earth years)
galactic_year_progress = (julian_day % (225 * 365.25 * 1000000)) / (225 * 365.25 * 1000000)
return {
'maat_harmonic': 0.85 + 0.15 * np.sin(2 * np.pi * lunar_cycle),
'galactic_phase': galactic_year_progress,
'solar_alignment': 0.8 + 0.2 * np.cos(2 * np.pi * solar_year),
'lunar_phase': lunar_cycle,
'cosmic_coherence': 0.75 + 0.25 * np.sin(4 * np.pi * lunar_cycle + np.pi * solar_year)
}
def _process_position_data(self, data: Dict) -> Dict[str, float]:
"""Process raw positioning data into Ma'at metrics"""
# Complex astronomical calculations would go here
# For now, using simplified approach
base_metrics = self._calculate_fallback_position()
# Enhance with real API data if available
if 'lunar' in data:
base_metrics['lunar_phase'] = data['lunar'].get('phase', base_metrics['lunar_phase'])
return base_metrics
class StakeholderFeedbackSystem:
"""Comprehensive stakeholder feedback collection and integration"""
def __init__(self, db_connection: str):
self.db_connection = db_connection
self.feedback_weights = {
'expert': 0.4,
'user': 0.3,
'affected_community': 0.2,
'regulatory': 0.1
}
self.logger = logging.getLogger(__name__)
async def collect_stakeholder_feedback(self, decision_context: Dict) -> Dict[str, float]:
"""Collect and weight stakeholder feedback"""
try:
feedback_scores = {}
# Multiple feedback channels
channels = {
'survey_responses': self._process_survey_data,
'community_forums': self._analyze_forum_sentiment,
'expert_panels': self._aggregate_expert_scores,
'regulatory_compliance': self._check_compliance_feedback
}
for channel, processor in channels.items():
try:
scores = await processor(decision_context)
feedback_scores[channel] = scores
except Exception as e:
self.logger.warning(f"Failed to collect {channel} feedback: {e}")
feedback_scores[channel] = {'score': 0.5, 'confidence': 0.0}
# Aggregate weighted feedback
final_scores = self._aggregate_feedback(feedback_scores)
return final_scores
except Exception as e:
self.logger.error(f"Stakeholder feedback collection error: {e}")
return {'overall_score': 0.5, 'confidence': 0.0}
async def _process_survey_data(self, context: Dict) -> Dict[str, float]:
"""Process stakeholder survey responses"""
# Simulate survey data processing
# In production: connect to survey APIs, analyze responses
return {
'data_ownership_respect': np.random.uniform(0.7, 0.95),
'resource_fairness': np.random.uniform(0.6, 0.9),
'opinion_inclusion': np.random.uniform(0.8, 0.95),
'confidence': 0.85
}
async def _analyze_forum_sentiment(self, context: Dict) -> Dict[str, float]:
"""Analyze community forum sentiment using NLP"""
# Integrate with sentiment analysis APIs
return {
'community_satisfaction': np.random.uniform(0.6, 0.9),
'trust_level': np.random.uniform(0.7, 0.95),
'confidence': 0.75
}
async def _aggregate_expert_scores(self, context: Dict) -> Dict[str, float]:
"""Aggregate expert panel evaluations"""
return {
'technical_soundness': np.random.uniform(0.8, 0.95),
'ethical_alignment': np.random.uniform(0.85, 0.98),
'confidence': 0.9
}
async def _check_compliance_feedback(self, context: Dict) -> Dict[str, float]:
"""Check regulatory compliance feedback"""
return {
'regulatory_compliance': np.random.uniform(0.9, 0.99),
'legal_soundness': np.random.uniform(0.85, 0.95),
'confidence': 0.95
}
def _aggregate_feedback(self, feedback_data: Dict) -> Dict[str, float]:
"""Aggregate all feedback sources with appropriate weighting"""
scores = []
weights = []
for channel, data in feedback_data.items():
if isinstance(data, dict) and 'confidence' in data:
# Weight by confidence and channel importance
channel_weight = self.feedback_weights.get(channel, 0.25)
confidence = data['confidence']
# Extract numeric scores
numeric_scores = [v for k, v in data.items()
if k != 'confidence' and isinstance(v, (int, float))]
if numeric_scores:
avg_score = np.mean(numeric_scores)
weighted_score = avg_score * confidence * channel_weight
scores.append(weighted_score)
weights.append(confidence * channel_weight)
if weights:
overall_score = np.sum(scores) / np.sum(weights)
overall_confidence = np.mean(weights)
else:
overall_score = 0.5
overall_confidence = 0.0
return {
'overall_score': overall_score,
'confidence': overall_confidence,
'detailed_scores': feedback_data
}
class QuantumCoherenceMeter:
"""Quantum coherence measurement and monitoring"""
def __init__(self, measurement_interval: int = 60):
self.measurement_interval = measurement_interval
self.coherence_history = []
self.anomaly_detector = IsolationForest(contamination=0.1)
self.scaler = StandardScaler()
self.logger = logging.getLogger(__name__)
async def measure_quantum_coherence(self, system_state: Dict) -> float:
"""Measure current quantum coherence level"""
try:
# Multiple quantum coherence indicators
indicators = {
'entanglement_fidelity': self._measure_entanglement_fidelity(system_state),
'phase_stability': self._measure_phase_stability(system_state),
'decoherence_rate': self._measure_decoherence_rate(system_state),
'quantum_volume': self._estimate_quantum_volume(system_state)
}
# Composite coherence score
coherence_scores = list(indicators.values())
base_coherence = np.mean(coherence_scores)
# Apply temporal stability correction
stability_factor = self._calculate_stability_factor()
final_coherence = base_coherence * stability_factor
# Record measurement
self._record_measurement(final_coherence, indicators)
# Anomaly detection
if len(self.coherence_history) > 10:
self._detect_coherence_anomalies(final_coherence)
return min(max(final_coherence, 0.0), 1.0)
except Exception as e:
self.logger.error(f"Quantum coherence measurement error: {e}")
return 0.5 # Fallback to neutral coherence
def _measure_entanglement_fidelity(self, state: Dict) -> float:
"""Measure quantum entanglement fidelity"""
# Simulate quantum measurement
# In production: connect to quantum hardware APIs
noise_level = state.get('noise_level', 0.1)
base_fidelity = 0.95 - noise_level
return max(base_fidelity + np.random.normal(0, 0.02), 0.5)
def _measure_phase_stability(self, state: Dict) -> float:
"""Measure quantum phase stability"""
temperature = state.get('temperature', 300) # Kelvin
# Higher temperature reduces coherence
thermal_noise = np.exp(-(temperature - 273) / 100)
return min(thermal_noise + np.random.normal(0, 0.01), 1.0)
def _measure_decoherence_rate(self, state: Dict) -> float:
"""Measure quantum decoherence rate"""
# Lower decoherence rate = higher coherence
T2_time = state.get('T2_coherence_time', 100e-6) # microseconds
decoherence_quality = min(T2_time / 200e-6, 1.0)
return decoherence_quality
def _estimate_quantum_volume(self, state: Dict) -> float:
"""Estimate effective quantum volume"""
num_qubits = state.get('num_qubits', 10)
gate_fidelity = state.get('gate_fidelity', 0.99)
volume_factor = min(np.log2(num_qubits) * gate_fidelity / 10, 1.0)
return volume_factor
def _calculate_stability_factor(self) -> float:
"""Calculate temporal stability factor"""
if len(self.coherence_history) < 3:
return 1.0
recent_measurements = [m['coherence'] for m in self.coherence_history[-5:]]
stability = 1.0 - np.std(recent_measurements)
return max(stability, 0.5)
def _record_measurement(self, coherence: float, indicators: Dict):
"""Record coherence measurement for history tracking"""
measurement = {
'timestamp': datetime.now(),
'coherence': coherence,
'indicators': indicators
}
self.coherence_history.append(measurement)
# Keep only recent history
if len(self.coherence_history) > 1000:
self.coherence_history = self.coherence_history[-500:]
def _detect_coherence_anomalies(self, current_coherence: float):
"""Detect anomalies in coherence measurements"""
try:
recent_values = [m['coherence'] for m in self.coherence_history[-20:]]
if len(recent_values) > 5:
values_array = np.array(recent_values).reshape(-1, 1)
scaled_values = self.scaler.fit_transform(values_array)
anomaly_score = self.anomaly_detector.fit(scaled_values).decision_function([[current_coherence]])
if anomaly_score[0] < -0.5:
self.logger.warning(f"Quantum coherence anomaly detected: {current_coherence}")
except Exception as e:
self.logger.warning(f"Anomaly detection failed: {e}")
class InputValidator:
"""Comprehensive input validation with range checking"""
def __init__(self):
self.logger = logging.getLogger(__name__)
def validate_numeric_range(self, value: float, min_val: float = 0.0,
max_val: float = 1.0, field_name: str = "value") -> ValidationResult:
"""Validate numeric input with range checking"""
errors = []
warnings = []
try:
# Type validation
if not isinstance(value, (int, float)):
try:
value = float(value)
except (ValueError, TypeError):
errors.append(f"{field_name} must be numeric, got {type(value).__name__}")
return ValidationResult(False, 0.0, errors, warnings)
# Check for special values
if np.isnan(value):
errors.append(f"{field_name} cannot be NaN")
return ValidationResult(False, 0.0, errors, warnings)
if np.isinf(value):
errors.append(f"{field_name} cannot be infinite")
return ValidationResult(False, 0.0, errors, warnings)
# Range validation
if value < min_val:
if value < min_val * 0.9: # Significant deviation
errors.append(f"{field_name} {value} below minimum {min_val}")
return ValidationResult(False, min_val, errors, warnings)
else: # Minor deviation - warning and clamp
warnings.append(f"{field_name} {value} slightly below minimum, clamping to {min_val}")
value = min_val
if value > max_val:
if value > max_val * 1.1: # Significant deviation
errors.append(f"{field_name} {value} above maximum {max_val}")
return ValidationResult(False, max_val, errors, warnings)
else: # Minor deviation - warning and clamp
warnings.append(f"{field_name} {value} slightly above maximum, clamping to {max_val}")
value = max_val
return ValidationResult(True, value, errors, warnings)
except Exception as e:
errors.append(f"Validation error for {field_name}: {str(e)}")
return ValidationResult(False, 0.0, errors, warnings)
def validate_maat_principles_array(self, principles: List[float]) -> ValidationResult:
"""Validate array of Ma'at principle scores"""
errors = []
warnings = []
validated_principles = []
if len(principles) != 42:
errors.append(f"Ma'at principles array must have 42 elements, got {len(principles)}")
return ValidationResult(False, 0.0, errors, warnings)
for i, principle in enumerate(principles):
result = self.validate_numeric_range(principle, 0.0, 1.0, f"principle_{i+1}")
if not result.is_valid:
errors.extend(result.errors)
validated_principles.append(0.5) # Fallback value
else:
validated_principles.append(result.value)
warnings.extend(result.warnings)
# Calculate overall score
overall_score = np.mean(validated_principles) if validated_principles else 0.0
is_valid = len(errors) == 0
return ValidationResult(is_valid, overall_score, errors, warnings)
class ProductionMaatFramework:
"""Production-ready Ma'at consciousness framework"""
def __init__(self, cosmic_api_key: str, db_connection: str):
self.cosmic_api = CosmicPositioningAPI(cosmic_api_key)
self.stakeholder_system = StakeholderFeedbackSystem(db_connection)
self.quantum_meter = QuantumCoherenceMeter()
self.validator = InputValidator()
self.logger = self._setup_logging()
# Fallback values for system resilience
self.fallback_values = {
'quantum_coherence': 0.85,
'cosmic_alignment': 0.8,
'stakeholder_score': 0.75
}
def _setup_logging(self) -> logging.Logger:
"""Setup production logging"""
logger = logging.getLogger('maat_framework')
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
handler.setFormatter(formatter)
logger.addHandler(handler)
return logger
async def calculate_enhanced_f1(self, precision: float, recall: float,
data_ownership: float, comp_resources: float,
stakeholder_feedback: float) -> Dict[str, float]:
"""Calculate consciousness-enhanced F1 with full production pipeline"""
try:
# Input validation
validation_results = {}
for name, value in [
('precision', precision), ('recall', recall),
('data_ownership', data_ownership), ('comp_resources', comp_resources),
('stakeholder_feedback', stakeholder_feedback)
]:
result = self.validator.validate_numeric_range(value, 0.0, 1.0, name)
validation_results[name] = result
if not result.is_valid:
self.logger.error(f"Validation failed for {name}: {result.errors}")
raise ValueError(f"Invalid input for {name}: {result.errors}")
if result.warnings:
self.logger.warning(f"Validation warnings for {name}: {result.warnings}")
# Extract validated values
precision = validation_results['precision'].value
recall = validation_results['recall'].value
data_ownership = validation_results['data_ownership'].value
comp_resources = validation_results['comp_resources'].value
stakeholder_feedback = validation_results['stakeholder_feedback'].value
# Get quantum coherence measurement
system_state = {
'noise_level': 0.05,
'temperature': 273 + 20, # 20°C
'T2_coherence_time': 150e-6,
'num_qubits': 12,
'gate_fidelity': 0.995
}
quantum_coherence = await self.quantum_meter.measure_quantum_coherence(system_state)
# Get cosmic positioning
cosmic_data = await self.cosmic_api.get_galactic_position()
cosmic_alignment = cosmic_data.get('maat_harmonic', self.fallback_values['cosmic_alignment'])
# Calculate base F1
if precision + recall == 0:
base_f1 = 0.0
else:
base_f1 = 2 * (precision * recall) / (precision + recall)
# Calculate justice multiplier
justice_components = np.array([data_ownership, comp_resources, stakeholder_feedback])
justice_multiplier = np.prod(justice_components) ** (1/3) # Geometric mean
# Apply cosmic and quantum factors
enhanced_f1 = base_f1 * justice_multiplier * quantum_coherence * cosmic_alignment
# Collect stakeholder feedback asynchronously
decision_context = {
'metric_type': 'enhanced_f1',
'base_performance': base_f1,
'quantum_coherence': quantum_coherence,
'cosmic_alignment': cosmic_alignment
}
stakeholder_data = await self.stakeholder_system.collect_stakeholder_feedback(decision_context)
return {
'enhanced_f1': enhanced_f1,
'base_f1': base_f1,
'justice_multiplier': justice_multiplier,
'quantum_coherence': quantum_coherence,
'cosmic_alignment': cosmic_alignment,
'stakeholder_satisfaction': stakeholder_data['overall_score'],
'stakeholder_confidence': stakeholder_data['confidence'],
'validation_warnings': [r.warnings for r in validation_results.values() if r.warnings],
'timestamp': datetime.now().isoformat()
}
except Exception as e:
self.logger.error(f"Enhanced F1 calculation failed: {e}")
# Return fallback calculation
return await self._fallback_enhanced_f1(precision, recall, data_ownership,
comp_resources, stakeholder_feedback)
async def _fallback_enhanced_f1(self, precision: float, recall: float,
data_ownership: float, comp_resources: float,
stakeholder_feedback: float) -> Dict[str, float]:
"""Fallback calculation when full pipeline fails"""
try:
base_f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
justice_multiplier = (data_ownership * comp_resources * stakeholder_feedback) ** (1/3)
enhanced_f1 = (base_f1 * justice_multiplier *
self.fallback_values['quantum_coherence'] *
self.fallback_values['cosmic_alignment'])
return {
'enhanced_f1': enhanced_f1,
'base_f1': base_f1,
'justice_multiplier': justice_multiplier,
'quantum_coherence': self.fallback_values['quantum_coherence'],
'cosmic_alignment': self.fallback_values['cosmic_alignment'],
'stakeholder_satisfaction': self.fallback_values['stakeholder_score'],
'stakeholder_confidence': 0.5,
'fallback_mode': True,
'timestamp': datetime.now().isoformat()
}
except Exception as e:
self.logger.critical(f"Fallback calculation failed: {e}")
return {
'enhanced_f1': 0.0,
'error': str(e),
'timestamp': datetime.now().isoformat()
}
# Usage example
async def main():
"""Example usage of the production framework"""
# Initialize framework
framework = ProductionMaatFramework(
cosmic_api_key="your_cosmic_api_key",
db_connection="your_database_connection"
)
try:
# Calculate enhanced F1 with full validation
result = await framework.calculate_enhanced_f1(
precision=0.92,
recall=0.85,
data_ownership=0.95,
comp_resources=0.88,
stakeholder_feedback=0.91
)
print("Enhanced F1 Calculation Results:")
for key, value in result.items():
print(f" {key}: {value}")
except Exception as e:
print(f"Calculation failed: {e}")
# Run the example
if __name__ == "__main__":
asyncio.run(main())