""" 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()