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"""
Ethical AI Governance Component for Codette
Ensures ethical behavior and decision-making in AI systems
"""
import logging
from typing import Dict, List, Any, Optional
from datetime import datetime
logger = logging.getLogger(__name__)
try:
import numpy as np
except Exception:
np = None
class EthicalAIGovernance:
"""Manages ethical governance and decision-making"""
def __init__(self,
ethics_threshold: float = 0.8,
confidence_threshold: float = 0.7,
memory_size: int = 1000):
"""Initialize the ethical governance system"""
self.ethics_threshold = ethics_threshold
self.confidence_threshold = confidence_threshold
self.memory_size = memory_size
# Initialize state
self.ethical_memory = []
self.policy_violations = []
self.current_state = {
"ethical_score": 1.0,
"active_policies": [],
"recent_decisions": []
}
# Initialize ethical principles
self._initialize_principles()
logger.info("Ethical AI Governance system initialized")
def enforce_policies(self,
content: Any,
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Enforce ethical policies on content"""
try:
# Analyze content
analysis = self._analyze_content(content, context)
# Check policy compliance
compliance = self._check_compliance(analysis)
# Generate decision
decision = self._make_decision(compliance)
# Update memory
self._update_memory(analysis, decision)
return decision
except Exception as e:
logger.error(f"Error enforcing policies: {e}")
return {
"status": "error",
"message": str(e),
"timestamp": datetime.now().isoformat()
}
def add_policy(self, policy: Dict[str, Any]) -> bool:
"""Add a new ethical policy"""
try:
if not self._validate_policy(policy):
return False
self.current_state["active_policies"].append({
"policy": policy,
"added_at": datetime.now().isoformat(),
"status": "active"
})
return True
except Exception as e:
logger.error(f"Error adding policy: {e}")
return False
def evaluate_ethics(self,
scenario: Dict[str, Any],
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Evaluate ethical implications of a scenario"""
try:
# Analyze scenario
analysis = self._analyze_scenario(scenario, context)
# Apply ethical principles
evaluation = self._apply_principles(analysis)
# Generate recommendations
recommendations = self._generate_recommendations(evaluation)
return {
"status": "success",
"ethical_score": evaluation["score"],
"concerns": evaluation["concerns"],
"recommendations": recommendations,
"confidence": evaluation["confidence"],
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error evaluating ethics: {e}")
return {"status": "error", "message": str(e)}
def _initialize_principles(self):
"""Initialize ethical principles"""
self.ethical_principles = {
"beneficence": {
"description": "Promote wellbeing and prevent harm",
"weight": 0.9
},
"non_maleficence": {
"description": "Avoid causing harm",
"weight": 0.9
},
"autonomy": {
"description": "Respect individual choice and privacy",
"weight": 0.8
},
"justice": {
"description": "Ensure fairness and equity",
"weight": 0.8
},
"transparency": {
"description": "Maintain openness and explainability",
"weight": 0.7
}
}
def _analyze_content(self,
content: Any,
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Analyze content for ethical considerations"""
try:
# Extract relevant features
features = self._extract_ethical_features(content)
# Analyze context impact
context_impact = self._analyze_context(context)
# Evaluate against principles
principle_scores = self._evaluate_principles(features)
return {
"features": features,
"context_impact": context_impact,
"principle_scores": principle_scores,
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error analyzing content: {e}")
return {}
def _check_compliance(self, analysis: Dict[str, Any]) -> Dict[str, Any]:
"""Check compliance with ethical policies"""
try:
compliance_results = []
# Check each active policy
for policy_data in self.current_state["active_policies"]:
policy = policy_data["policy"]
result = self._check_policy_compliance(
policy,
analysis
)
compliance_results.append(result)
# Calculate overall compliance
if np is not None:
overall_compliance = float(np.mean([
r["compliance_score"]
for r in compliance_results
]))
else:
overall_compliance = float(sum(r["compliance_score"] for r in compliance_results)/len(compliance_results)) if compliance_results else 0.0
return {
"status": "compliant" if overall_compliance >= self.ethics_threshold else "non_compliant",
"overall_score": overall_compliance,
"results": compliance_results,
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error checking compliance: {e}")
return {"status": "error", "message": str(e)}
def _make_decision(self, compliance: Dict[str, Any]) -> Dict[str, Any]:
"""Make ethical decision based on compliance results"""
try:
decision = {
"allowed": compliance["status"] == "compliant",
"score": compliance["overall_score"],
"rationale": self._generate_rationale(compliance),
"recommendations": self._generate_recommendations(compliance),
"timestamp": datetime.now().isoformat()
}
# Update state
self.current_state["ethical_score"] = compliance["overall_score"]
self.current_state["recent_decisions"].append({
"decision": decision["allowed"],
"score": decision["score"],
"timestamp": decision["timestamp"]
})
# Trim recent decisions
if len(self.current_state["recent_decisions"]) > 10:
self.current_state["recent_decisions"] = self.current_state["recent_decisions"][-10:]
return decision
except Exception as e:
logger.error(f"Error making decision: {e}")
return {"allowed": False, "error": str(e)}
def _update_memory(self, analysis: Dict[str, Any], decision: Dict[str, Any]):
"""Update ethical memory with new analysis"""
try:
memory_entry = {
"analysis": analysis,
"decision": decision,
"timestamp": datetime.now().isoformat()
}
self.ethical_memory.append(memory_entry)
# Trim memory if needed
if len(self.ethical_memory) > self.memory_size:
self.ethical_memory = self.ethical_memory[-self.memory_size:]
except Exception as e:
logger.error(f"Error updating memory: {e}")
def _validate_policy(self, policy: Dict[str, Any]) -> bool:
"""Validate a new policy"""
try:
required_fields = ["name", "description", "criteria"]
# Check required fields
if not all(field in policy for field in required_fields):
return False
# Validate criteria structure
if not isinstance(policy["criteria"], (list, dict)):
return False
return True
except Exception as e:
logger.error(f"Error validating policy: {e}")
return False
def _analyze_scenario(self,
scenario: Dict[str, Any],
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Analyze an ethical scenario"""
try:
# Extract scenario components
components = self._extract_scenario_components(scenario)
# Analyze stakeholder impact
stakeholder_impact = self._analyze_stakeholder_impact(components)
# Consider context
context_factors = self._analyze_context(context)
return {
"components": components,
"stakeholder_impact": stakeholder_impact,
"context_factors": context_factors,
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error analyzing scenario: {e}")
return {}
def _extract_ethical_features(self, content: Any) -> Dict[str, Any]:
"""Extract ethically relevant features from content"""
features = {}
try:
if isinstance(content, dict):
features = self._extract_dict_features(content)
elif isinstance(content, str):
features = self._extract_text_features(content)
else:
features = self._extract_generic_features(content)
except Exception as e:
logger.error(f"Error extracting features: {e}")
return features
def _analyze_context(self, context: Optional[Dict[str, Any]]) -> Dict[str, float]:
"""Analyze ethical context"""
try:
if not context:
return {"impact": 0.5, "confidence": 0.5}
# Extract context features
features = self._extract_ethical_features(context)
# Calculate impact
if np is not None:
impact = float(np.mean([
self._evaluate_feature_impact(feature)
for feature in features.values()
]))
else:
vals = [self._evaluate_feature_impact(feature) for feature in features.values()]
impact = float(sum(vals)/len(vals)) if vals else 0.0
return {
"impact": impact,
"confidence": min(1.0, len(features) / 10)
}
except Exception as e:
logger.error(f"Error analyzing context: {e}")
return {"impact": 0.5, "confidence": 0.0}
def _evaluate_principles(self,
features: Dict[str, Any]) -> Dict[str, float]:
"""Evaluate features against ethical principles"""
scores = {}
try:
for principle, data in self.ethical_principles.items():
score = self._evaluate_principle_compliance(
principle,
features,
data["weight"]
)
scores[principle] = score
except Exception as e:
logger.error(f"Error evaluating principles: {e}")
return scores
def _check_policy_compliance(self,
policy: Dict[str, Any],
analysis: Dict[str, Any]) -> Dict[str, Any]:
"""Check compliance with a specific policy"""
try:
criteria_results = []
# Check each criterion
for criterion in policy["criteria"]:
result = self._evaluate_criterion(criterion, analysis)
criteria_results.append(result)
# Calculate compliance score
compliance_score = np.mean([
r["score"] for r in criteria_results
]) if np is not None else sum(r["score"] for r in criteria_results) / len(criteria_results) if criteria_results else 0.0
return {
"policy_name": policy["name"],
"compliance_score": compliance_score,
"criteria_results": criteria_results,
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error checking policy compliance: {e}")
return {
"policy_name": policy.get("name", "unknown"),
"compliance_score": 0.0,
"error": str(e)
}
def _generate_rationale(self, compliance: Dict[str, Any]) -> List[str]:
"""Generate rationale for ethical decision"""
rationale = []
try:
# Add overall compliance statement
rationale.append(
f"Overall ethical compliance: {compliance['overall_score']:.2f}"
)
# Add specific policy results
for result in compliance.get("results", []):
if result["compliance_score"] < self.ethics_threshold:
rationale.append(
f"Policy '{result['policy_name']}' needs attention "
f"(score: {result['compliance_score']:.2f})"
)
except Exception as e:
logger.error(f"Error generating rationale: {e}")
rationale.append("Error generating detailed rationale")
return rationale
def _apply_principles(self,
analysis: Dict[str, Any]) -> Dict[str, Any]:
"""Apply ethical principles to analysis"""
try:
# Calculate principle scores
scores = {}
concerns = []
for principle, data in self.ethical_principles.items():
score = self._evaluate_principle_impact(
principle,
analysis,
data["weight"]
)
scores[principle] = score
if score < self.ethics_threshold:
concerns.append(f"Low {principle} score: {score:.2f}")
# Calculate overall score
if np is not None:
overall_score = float(np.average(
list(scores.values()),
weights=[d["weight"] for d in self.ethical_principles.values()]
))
else:
vals = list(scores.values())
weights = [d["weight"] for d in self.ethical_principles.values()]
total_w = sum(weights)
overall_score = float(sum(v*w for v,w in zip(vals, weights))/total_w) if total_w else 0.0
return {
"score": overall_score,
"principle_scores": scores,
"concerns": concerns,
"confidence": self._calculate_confidence(scores),
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error applying principles: {e}")
return {
"score": 0.0,
"principle_scores": {},
"concerns": ["Error in principle application"],
"confidence": 0.0
}
def _generate_recommendations(self,
evaluation: Dict[str, Any]) -> List[str]:
"""Generate ethical recommendations"""
recommendations = []
try:
# Generate based on compliance level
if isinstance(evaluation.get("overall_score"), (int, float)):
score = evaluation["overall_score"]
if score < self.ethics_threshold:
recommendations.extend(
self._generate_improvement_recommendations(evaluation)
)
# Add principle-specific recommendations
if "principle_scores" in evaluation:
for principle, score in evaluation["principle_scores"].items():
if score < self.ethics_threshold:
recommendations.extend(
self._get_principle_recommendations(principle)
)
# Prioritize and limit recommendations
recommendations = sorted(
recommendations,
key=lambda x: len(x)
)[:5] # Limit to top 5
except Exception as e:
logger.error(f"Error generating recommendations: {e}")
recommendations.append(
"Unable to generate specific recommendations"
)
return recommendations
def _evaluate_criterion(self,
criterion: Dict[str, Any],
analysis: Dict[str, Any]) -> Dict[str, Any]:
"""Evaluate a single policy criterion"""
try:
criterion_type = criterion.get("type", "")
if criterion_type == "threshold":
score = self._evaluate_threshold_criterion(criterion, analysis)
elif criterion_type == "pattern":
score = self._evaluate_pattern_criterion(criterion, analysis)
else:
score = 0.0
return {
"criterion_name": criterion.get("name", "unnamed"),
"score": score,
"type": criterion_type,
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error evaluating criterion: {e}")
return {"score": 0.0, "error": str(e)}
def _evaluate_principle_impact(self,
principle: str,
analysis: Dict[str, Any],
weight: float) -> float:
"""Evaluate impact on a specific ethical principle"""
try:
# Get relevant components
components = analysis.get("components", {})
stakeholder_impact = analysis.get("stakeholder_impact", {})
# Calculate base score
base_score = self._calculate_principle_score(
principle,
components
)
# Adjust for stakeholder impact
stakeholder_factor = stakeholder_impact.get(
principle,
0.5 # Neutral if not specified
)
# Apply weight
weighted_score = (base_score + stakeholder_factor) / 2 * weight
return min(1.0, max(0.0, weighted_score))
except Exception as e:
logger.error(f"Error evaluating principle impact: {e}")
return 0.0
def _evaluate_feature_impact(self, feature: Any) -> float:
"""Evaluate ethical impact of a feature"""
try:
if isinstance(feature, (int, float)):
return min(1.0, abs(feature))
elif isinstance(feature, str):
return len(feature) / 100 # Normalize
else:
return 0.5 # Neutral for unknown types
except Exception as e:
logger.error(f"Error evaluating feature impact: {e}")
return 0.0
def _calculate_confidence(self, scores: Dict[str, float]) -> float:
"""Calculate confidence in ethical evaluation"""
try:
if not scores:
return 0.0
# Calculate based on score consistency
values = list(scores.values())
consistency = 1.0 - np.std(values) if np is not None else 1.0 - sum((x - (sum(values)/len(values))) ** 2 for x in values) / len(values) if values else 0.0
coverage = len(scores) / len(self.ethical_principles)
return min(1.0, (consistency + coverage) / 2)
except Exception as e:
logger.error(f"Error calculating confidence: {e}")
return 0.0
def _extract_scenario_components(self,
scenario: Dict[str, Any]) -> Dict[str, Any]:
"""Extract components from an ethical scenario"""
components = {}
try:
# Extract actions
if "actions" in scenario:
components["actions"] = self._analyze_actions(scenario["actions"])
# Extract consequences
if "consequences" in scenario:
components["consequences"] = self._analyze_consequences(
scenario["consequences"]
)
# Extract stakeholders
if "stakeholders" in scenario:
components["stakeholders"] = self._analyze_stakeholders(
scenario["stakeholders"]
)
except Exception as e:
logger.error(f"Error extracting scenario components: {e}")
return components
def _analyze_stakeholder_impact(self,
components: Dict[str, Any]) -> Dict[str, float]:
"""Analyze impact on stakeholders"""
impact = {}
try:
stakeholders = components.get("stakeholders", {})
consequences = components.get("consequences", {})
for stakeholder, data in stakeholders.items():
impact[stakeholder] = self._calculate_stakeholder_impact(
stakeholder,
data,
consequences
)
except Exception as e:
logger.error(f"Error analyzing stakeholder impact: {e}")
return impact
def _generate_improvement_recommendations(self,
evaluation: Dict[str, Any]) -> List[str]:
"""Generate recommendations for ethical improvements"""
recommendations = []
try:
if "principle_scores" in evaluation:
for principle, score in evaluation["principle_scores"].items():
if score < self.ethics_threshold:
recommendations.extend(
self._get_principle_recommendations(principle)
)
except Exception as e:
logger.error(f"Error generating improvement recommendations: {e}")
return recommendations
def get_state(self) -> Dict[str, Any]:
"""Get current state of the ethical governance system"""
return self.current_state.copy()
def get_memory(self) -> List[Dict[str, Any]]:
"""Get ethical memory"""
return self.ethical_memory.copy()
def get_violation_history(self) -> List[Dict[str, Any]]:
"""Get history of policy violations"""
return self.policy_violations.copy() |