""" Batch audit tools for TRuCAL's ethical reasoning system. Analyzes ledger entries to surface ethical blind spots and patterns. """ import json import re import csv from pathlib import Path from typing import List, Dict, Any, Tuple, Optional from collections import Counter, defaultdict from datetime import datetime class BatchAudit: """ Analyze batches of ledger entries to identify ethical patterns and blind spots. Args: ledger_path: Path to the ledger JSONL file output_dir: Directory to store audit reports (default: 'audit_reports') """ def __init__(self, ledger_path: str, output_dir: str = 'audit_reports'): self.ledger_path = Path(ledger_path) self.output_dir = Path(output_dir) self.output_dir.mkdir(parents=True, exist_ok=True) # Ethical terms to track self.ethics_keywords = [ 'ethic', 'moral', 'right', 'wrong', 'fair', 'unfair', 'bias', 'discriminat', 'harm', 'risk', 'safe', 'privacy', 'consent', 'accountab', 'transparen', 'trust', 'justice' ] # Sensitive topics to flag self.sensitive_topics = [ 'race', 'gender', 'religion', 'politics', 'health', 'finance', 'legal', 'violence', 'harassment' ] def load_ledger(self) -> List[Dict[str, Any]]: """Load and parse the ledger entries from JSONL file.""" entries = [] try: with open(self.ledger_path, 'r', encoding='utf-8') as f: for line in f: try: entry = json.loads(line.strip()) entries.append(entry) except json.JSONDecodeError as e: print(f"Error parsing JSON line: {e}") except FileNotFoundError: print(f"Ledger file not found: {self.ledger_path}") return entries def analyze_ethical_coverage(self, entries: List[Dict[str, Any]]) -> Dict[str, Any]: """Analyze the coverage of ethical considerations in the ledger.""" stats = { 'total_entries': len(entries), 'with_ethical_considerations': 0, 'with_rights_assertions': 0, 'with_vulnerability_assessment': 0, 'ethics_keyword_matches': Counter(), 'sensitive_topics': Counter(), 'missing_ethical_considerations': [] } for idx, entry in enumerate(entries): # Check for explicit ethical considerations has_ethics = bool(entry.get('ethical_considerations')) if has_ethics: stats['with_ethical_considerations'] += 1 # Check for rights assertions if entry.get('rights'): stats['with_rights_assertions'] += 1 # Check for vulnerability assessments if entry.get('vulnerabilities'): stats['with_vulnerability_assessment'] += 1 # Track missing ethical considerations if not has_ethics: stats['missing_ethical_considerations'].append({ 'id': entry.get('id', idx), 'prompt': entry.get('prompt', '')[:100] + '...' if 'prompt' in entry else '' }) # Analyze text for ethical keywords and sensitive topics text = json.dumps(entry).lower() # Count ethical keyword matches for keyword in self.ethics_keywords: if keyword in text: stats['ethics_keyword_matches'][keyword] += 1 # Count sensitive topic mentions for topic in self.sensitive_topics: if topic in text: stats['sensitive_topics'][topic] += 1 return stats def generate_audit_report(self, stats: Dict[str, Any]) -> str: """Generate a human-readable audit report.""" report = [ "# TRuCAL Ethical Audit Report", f"Generated: {datetime.utcnow().isoformat()}\n", "## Summary", f"Total entries analyzed: {stats['total_entries']}", f"Entries with ethical considerations: {stats['with_ethical_considerations']} " f"({stats['with_ethical_considerations']/stats['total_entries']:.1%})", f"Entries with rights assertions: {stats['with_rights_assertions']} " f"({stats['with_rights_assertions']/stats['total_entries']:.1%})", f"Entries with vulnerability assessments: {stats['with_vulnerability_assessment']} " f"({stats['with_vulnerability_assessment']/stats['total_entries']:.1%})\n", "## Ethical Keyword Frequency" ] # Add top ethical keywords for keyword, count in stats['ethics_keyword_matches'].most_common(10): report.append(f"- {keyword}: {count} occurrences") report.extend(["\n## Sensitive Topics Detected"]) # Add sensitive topics for topic, count in stats['sensitive_topics'].most_common(): report.append(f"- {topic}: {count} occurrences") # Add entries missing ethical considerations if stats['missing_ethical_considerations']: report.extend([ "\n## Entries Missing Ethical Considerations", f"Total: {len(stats['missing_ethical_considerations'])} entries\n" ]) # Add a sample of missing entries (up to 5) sample = stats['missing_ethical_considerations'][:5] for entry in sample: report.append(f"- ID: {entry['id']}, Prompt: {entry['prompt']}") if len(stats['missing_ethical_considerations']) > 5: report.append(f"... and {len(stats['missing_ethical_considerations']) - 5} more") return "\n".join(report) def export_to_csv(self, stats: Dict[str, Any], filename: str) -> None: """Export audit statistics to a CSV file.""" csv_path = self.output_dir / filename # Prepare data for CSV rows = [] # Add summary stats rows.append(['Metric', 'Count', 'Percentage']) rows.extend([ ['Total Entries', stats['total_entries'], '100.0%'], ['With Ethical Considerations', stats['with_ethical_considerations'], f"{stats['with_ethical_considerations']/stats['total_entries']:.1%}"], ['With Rights Assertions', stats['with_rights_assertions'], f"{stats['with_rights_assertions']/stats['total_entries']:.1%}"], ['With Vulnerability Assessments', stats['with_vulnerability_assessment'], f"{stats['with_vulnerability_assessment']/stats['total_entries']:.1%}"] ]) # Add ethical keywords rows.append(['', '', '']) rows.append(['Top Ethical Keywords', 'Count', '']) for keyword, count in stats['ethics_keyword_matches'].most_common(10): rows.append([keyword, count, '']) # Add sensitive topics rows.append(['', '', '']) rows.append(['Sensitive Topics', 'Count', '']) for topic, count in stats['sensitive_topics'].most_common(): rows.append([topic, count, '']) # Write to CSV with open(csv_path, 'w', newline='', encoding='utf-8') as f: writer = csv.writer(f) writer.writerows(rows) def run_audit(self, export_csv: bool = True) -> Dict[str, Any]: """ Run a complete audit of the ledger. Args: export_csv: Whether to export results to CSV Returns: Dictionary containing audit statistics """ print(f"Starting audit of {self.ledger_path}...") # Load and analyze the ledger entries = self.load_ledger() if not entries: print("No entries found in the ledger.") return {} # Generate statistics stats = self.analyze_ethical_coverage(entries) # Generate and save the report report = self.generate_audit_report(stats) report_path = self.output_dir / 'audit_report.md' with open(report_path, 'w', encoding='utf-8') as f: f.write(report) # Export to CSV if requested if export_csv: self.export_to_csv(stats, 'audit_results.csv') print(f"Audit complete. Report saved to {report_path}") return stats def main(): """Example usage of the BatchAudit class.""" import argparse parser = argparse.ArgumentParser(description='Run an ethical audit on a TRuCAL ledger.') parser.add_argument('--ledger', type=str, default='logs/audit_log.jsonl', help='Path to the ledger JSONL file') parser.add_argument('--output-dir', type=str, default='audit_reports', help='Directory to save audit reports') args = parser.parse_args() auditor = BatchAudit(ledger_path=args.ledger, output_dir=args.output_dir) auditor.run_audit() if __name__ == '__main__': main()