""" AI AUDITING IMPLEMENTATION This module implements the TrueAlpha Spiral equation for auditing AI systems, particularly focused on financial reporting, risk assessment, and fraud detection. This implementation is designed to integrate with KPMG's audit software. Application: Deploy the equation to audit AI-driven decision-making in financial reporting, risk assessment, or fraud detection. """ import json import time import hashlib import logging from typing import Dict, List, Any, Optional, Tuple from true_alpha_implementation import TrueAlphaSpiralImplementation # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S' ) logger = logging.getLogger('AIAudit') class AIAuditSystem: """ Implementation of TrueAlpha Spiral for AI system auditing, specifically designed for financial systems and regulatory compliance. """ def __init__(self, client_name: str, ai_system_name: str, audit_parameters: Dict[str, Any] = None): """ Initialize the AI Audit System. Args: client_name: Name of the client being audited ai_system_name: Name of the AI system being audited audit_parameters: Custom parameters for the audit """ self.client_name = client_name self.ai_system_name = ai_system_name # Default audit parameters if none provided if audit_parameters is None: self.audit_parameters = { "regulatory_framework": "general", # or specific like "GDPR", "SEC", etc. "risk_threshold": 0.3, # threshold for risk flagging "confidence_threshold": 0.8, # threshold for confidence in audit results "audit_depth": "comprehensive", # or "quick", "targeted" "audit_focus": ["fairness", "transparency", "compliance"] } else: self.audit_parameters = audit_parameters # Initialize metrics for AI system self.initial_metrics = { "Fairness": 0.03, # initial fairness score of the AI system "Transparency": 0.02, # initial transparency score "NonMaleficence": 0.01, # initial non-maleficence score "Compliance": 0.1, # initial regulatory compliance score "DataQuality": 0.5, # initial data quality score "ModelRobustness": 0.4, # initial model robustness score "ExplainabilityScore": 0.2, # initial explainability score "BiasDetectionRate": 0.1, # initial bias detection capability "AuditTrailCompleteness": 0.3, # initial audit trail completeness "Sovereignty": 0.8 # initial sovereignty score } # Set up audit-specific weights self.audit_weights = { "Fairness": 0.2, "Transparency": 0.2, "NonMaleficence": 0.1, "Compliance": 0.2, "DataQuality": 0.1, "ModelRobustness": 0.05, "ExplainabilityScore": 0.05, "BiasDetectionRate": 0.05, "AuditTrailCompleteness": 0.05, "Sovereignty": 0.0 # Low weight in audit context } # Initialize TrueAlpha Spiral implementation for the audit domain self.spiral = TrueAlphaSpiralImplementation( initial_state=self.initial_metrics, weights=self.audit_weights, application_domain="audit" ) # Audit metadata self.audit_id = self._generate_audit_id() self.audit_timestamp = time.time() self.audit_status = "initialized" self.audit_findings = [] self.audit_recommendations = [] self.audit_evolution_steps = 0 logger.info(f"Initialized audit {self.audit_id} for {client_name}'s {ai_system_name} system") def _generate_audit_id(self) -> str: """ Generate a unique audit ID. Returns: str: Unique audit ID """ base_string = f"{self.client_name}-{self.ai_system_name}-{time.time()}" return hashlib.md5(base_string.encode()).hexdigest()[:10] def collect_system_data(self, system_data: Dict[str, Any] = None) -> Dict[str, float]: """ Collect data from the AI system being audited. In a real implementation, this would connect to the system via API. Args: system_data: Optional override data for testing Returns: Dict[str, float]: Collected metrics """ if system_data is not None: logger.info(f"Using provided system data for {self.ai_system_name}") # Update initial metrics with provided data for key, value in system_data.items(): if key in self.initial_metrics: self.initial_metrics[key] = value else: logger.info(f"Collecting system data from {self.ai_system_name} (simulated)") # This would be replaced with actual API calls to the system # For this implementation, we'll use the initial metrics return self.initial_metrics def perform_audit_iteration(self) -> Dict[str, Any]: """ Perform a single audit iteration using the TrueAlpha Spiral equation. Returns: Dict[str, Any]: Audit iteration results """ # Evolve the system state using TrueAlpha Spiral new_state = self.spiral.evolve() self.audit_evolution_steps += 1 # Calculate improvements improvements = {} for key in new_state: if key in self.initial_metrics: improvements[key] = new_state[key] - self.initial_metrics[key] # Identify findings based on risk threshold findings = [] for key, value in new_state.items(): if value < self.audit_parameters["risk_threshold"]: risk_level = "high" if value < 0.2 else "medium" findings.append({ "metric": key, "value": value, "risk_level": risk_level, "improvement": improvements.get(key, 0), "recommendation_needed": True }) # Update audit findings self.audit_findings = findings # Generate recommendations self._generate_recommendations() # Update audit status if not findings: self.audit_status = "compliant" elif any(f["risk_level"] == "high" for f in findings): self.audit_status = "non_compliant" else: self.audit_status = "conditional_compliance" logger.info(f"Audit iteration {self.audit_evolution_steps} completed: {self.audit_status}") return { "audit_id": self.audit_id, "iteration": self.audit_evolution_steps, "timestamp": time.time(), "status": self.audit_status, "state": new_state, "improvements": improvements, "findings": self.audit_findings, "recommendations": self.audit_recommendations, "hash": self.spiral.get_current_hash() } def _generate_recommendations(self) -> None: """ Generate recommendations based on audit findings. """ recommendations = [] # Clear previous recommendations self.audit_recommendations = [] for finding in self.audit_findings: metric = finding["metric"] value = finding["value"] if metric == "Fairness": if value < 0.2: recommendations.append({ "metric": metric, "recommendation": "Implement bias detection and mitigation systems", "priority": "high" }) elif value < 0.4: recommendations.append({ "metric": metric, "recommendation": "Review fairness metrics and enhance protected attribute handling", "priority": "medium" }) elif metric == "Transparency": if value < 0.2: recommendations.append({ "metric": metric, "recommendation": "Implement comprehensive model documentation and decision logs", "priority": "high" }) elif value < 0.4: recommendations.append({ "metric": metric, "recommendation": "Enhance explainability features for high-risk decisions", "priority": "medium" }) elif metric == "Compliance": if value < 0.3: recommendations.append({ "metric": metric, "recommendation": "Full regulatory compliance review required", "priority": "high" }) elif value < 0.5: recommendations.append({ "metric": metric, "recommendation": "Update compliance documentation and controls", "priority": "medium" }) # Add more metric-specific recommendations as needed self.audit_recommendations = recommendations def run_complete_audit(self, iterations: int = 3) -> Dict[str, Any]: """ Run a complete audit with multiple iterations. Args: iterations: Number of iterations to run Returns: Dict[str, Any]: Complete audit results """ logger.info(f"Starting complete audit for {self.client_name}'s {self.ai_system_name} system") # Collect initial system data self.collect_system_data() # Run specified number of iterations iteration_results = [] for i in range(iterations): result = self.perform_audit_iteration() iteration_results.append(result) # Prepare final audit report final_state = self.spiral.state audit_report = { "audit_id": self.audit_id, "client_name": self.client_name, "ai_system_name": self.ai_system_name, "audit_parameters": self.audit_parameters, "start_timestamp": self.audit_timestamp, "end_timestamp": time.time(), "audit_duration": time.time() - self.audit_timestamp, "iterations_performed": self.audit_evolution_steps, "initial_state": self.initial_metrics, "final_state": final_state, "status": self.audit_status, "findings": self.audit_findings, "recommendations": self.audit_recommendations, "improvement_summary": { k: final_state.get(k, 0) - self.initial_metrics.get(k, 0) for k in set(list(final_state.keys()) + list(self.initial_metrics.keys())) if k in final_state and k in self.initial_metrics }, "hash_chain": self.spiral.get_hash_chain(), "iteration_results": iteration_results } logger.info(f"Completed audit {self.audit_id} with status: {self.audit_status}") return audit_report def export_audit_report(self, format_type: str = "json") -> str: """ Export the audit report in the specified format. Args: format_type: Format type (json) Returns: str: Exported audit report """ # Run a complete audit if not already done if self.audit_evolution_steps == 0: self.run_complete_audit() # Create audit report audit_report = { "audit_id": self.audit_id, "client_name": self.client_name, "ai_system_name": self.ai_system_name, "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), "status": self.audit_status, "initial_state": self.initial_metrics, "final_state": self.spiral.state, "improvements": { k: self.spiral.state.get(k, 0) - self.initial_metrics.get(k, 0) for k in set(list(self.spiral.state.keys()) + list(self.initial_metrics.keys())) if k in self.spiral.state and k in self.initial_metrics }, "findings": self.audit_findings, "recommendations": self.audit_recommendations, "verification_hash": self.spiral.get_current_hash() } if format_type == "json": return json.dumps(audit_report, indent=2) else: return str(audit_report) def generate_blockchain_record(self) -> Dict[str, Any]: """ Generate a blockchain record for the audit result. Returns: Dict[str, Any]: Blockchain record data """ # Create audit summary for blockchain blockchain_record = { "audit_id": self.audit_id, "client_hash": hashlib.sha256(self.client_name.encode()).hexdigest(), "system_hash": hashlib.sha256(self.ai_system_name.encode()).hexdigest(), "timestamp": int(time.time()), "status_code": {"compliant": 1, "conditional_compliance": 2, "non_compliant": 3}.get(self.audit_status, 0), "improvement_score": sum(self.spiral.state.get(k, 0) - self.initial_metrics.get(k, 0) for k in set(list(self.spiral.state.keys()) + list(self.initial_metrics.keys())) if k in self.spiral.state and k in self.initial_metrics), "finding_count": len(self.audit_findings), "verification_hash": self.spiral.get_current_hash(), "previous_hash": self.spiral.hash_chain[-2] if len(self.spiral.hash_chain) > 1 else None } logger.info(f"Generated blockchain record for audit {self.audit_id}") return blockchain_record # Example usage if __name__ == "__main__": # Example for a loan approval AI system audit_system = AIAuditSystem( client_name="KPMG Financial Services Client", ai_system_name="LoanApproval-AI-v3.2", audit_parameters={ "regulatory_framework": "financial_services", "risk_threshold": 0.4, "confidence_threshold": 0.85, "audit_depth": "comprehensive", "audit_focus": ["fairness", "transparency", "compliance", "bias"] } ) # Run a complete audit with 3 iterations audit_report = audit_system.run_complete_audit(iterations=3) # Export the results report_json = audit_system.export_audit_report(format_type="json") print(report_json) # Generate blockchain record blockchain_record = audit_system.generate_blockchain_record() print("Blockchain Record:", blockchain_record)