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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() |