Spaces:
Sleeping
Sleeping
| """ | |
| EthicalAIGovernance - Ethical Decision Framework | |
| Ensures transparency, fairness, and respect in all AI responses | |
| """ | |
| import re | |
| from typing import Dict, List, Any | |
| class EthicalAIGovernance: | |
| """ | |
| Ethical AI Governance Module | |
| Enforces: | |
| - Transparency in decision-making | |
| - Fairness and bias mitigation | |
| - Privacy respect | |
| - Explainable AI principles | |
| - Harmful content filtering | |
| """ | |
| def __init__(self, config: Dict[str, Any] = None): | |
| self.config = config or {} | |
| self.ethical_principles = self.config.get("ethical_considerations", | |
| "Always act with transparency, fairness, and respect for privacy.") | |
| # Harmful content patterns to filter | |
| self.harmful_patterns = [ | |
| r'\b(hate|violence|harm|kill|destroy)\b', | |
| r'\b(discriminat|racist|sexist|bigot)\b', | |
| # Add more patterns as needed | |
| ] | |
| # Audit log | |
| self.audit_log = [] | |
| def enforce_policies(self, response: str) -> Dict[str, Any]: | |
| """ | |
| Enforce ethical policies on a response | |
| Args: | |
| response: AI-generated response | |
| Returns: | |
| Dict with enforcement result and filtered response | |
| """ | |
| result = { | |
| "original_length": len(response), | |
| "passed": True, | |
| "warnings": [], | |
| "filtered_response": response, | |
| "ethical_note": self.ethical_principles | |
| } | |
| # Check for harmful content | |
| for pattern in self.harmful_patterns: | |
| if re.search(pattern, response, re.IGNORECASE): | |
| result["warnings"].append(f"Potentially harmful content detected: {pattern}") | |
| result["passed"] = False | |
| # Check for bias indicators | |
| bias_check = self._check_bias(response) | |
| if bias_check["has_bias"]: | |
| result["warnings"].extend(bias_check["warnings"]) | |
| # Add ethical note to response | |
| if self.config.get("append_ethical_note", True): | |
| result["filtered_response"] += f"\n\n**Ethical Note:** {self.ethical_principles}" | |
| # Log the enforcement | |
| self._log_enforcement(result) | |
| return result | |
| def validate_query(self, query: str) -> Dict[str, Any]: | |
| """ | |
| Validate a user query for ethical concerns | |
| Args: | |
| query: User query | |
| Returns: | |
| Validation result | |
| """ | |
| result = { | |
| "valid": True, | |
| "warnings": [], | |
| "suggestions": [] | |
| } | |
| # Check for harmful intent | |
| for pattern in self.harmful_patterns: | |
| if re.search(pattern, query, re.IGNORECASE): | |
| result["valid"] = False | |
| result["warnings"].append("Query contains potentially harmful language") | |
| result["suggestions"].append("Please rephrase your question respectfully") | |
| return result | |
| def _check_bias(self, text: str) -> Dict[str, Any]: | |
| """ | |
| Check text for potential bias | |
| Args: | |
| text: Text to check | |
| Returns: | |
| Bias check result | |
| """ | |
| result = { | |
| "has_bias": False, | |
| "warnings": [] | |
| } | |
| # Gender bias patterns | |
| gendered_terms = [ | |
| (r'\bhe\b.*\bstrong\b', "Gender stereotype detected"), | |
| (r'\bshe\b.*\bemotional\b', "Gender stereotype detected"), | |
| ] | |
| for pattern, warning in gendered_terms: | |
| if re.search(pattern, text, re.IGNORECASE): | |
| result["has_bias"] = True | |
| result["warnings"].append(warning) | |
| return result | |
| def get_ethical_guidelines(self) -> List[str]: | |
| """ | |
| Get list of ethical guidelines | |
| Returns: | |
| List of ethical principles | |
| """ | |
| return [ | |
| "Transparency: All decisions must be explainable", | |
| "Fairness: No discrimination based on protected characteristics", | |
| "Privacy: Respect user data and confidentiality", | |
| "Safety: Prevent harmful outputs", | |
| "Accountability: Log all decisions for audit", | |
| "Beneficence: Act in the best interest of users" | |
| ] | |
| def _log_enforcement(self, result: Dict[str, Any]): | |
| """ | |
| Log enforcement action | |
| Args: | |
| result: Enforcement result | |
| """ | |
| self.audit_log.append({ | |
| "timestamp": str(os.times()) if 'os' in dir() else "unknown", | |
| "passed": result["passed"], | |
| "warnings": result["warnings"] | |
| }) | |
| def get_audit_log(self, recent: int = 10) -> List[Dict]: | |
| """ | |
| Get recent audit log entries | |
| Args: | |
| recent: Number of recent entries | |
| Returns: | |
| Recent audit log | |
| """ | |
| return self.audit_log[-recent:] | |
| def clear_audit_log(self): | |
| """Clear the audit log""" | |
| self.audit_log = [] | |
| if __name__ == "__main__": | |
| # Test EthicalAIGovernance | |
| governance = EthicalAIGovernance() | |
| print("=== Ethical Guidelines ===") | |
| for guideline in governance.get_ethical_guidelines(): | |
| print(f"- {guideline}") | |
| print("\n=== Test Response Enforcement ===") | |
| test_response = "This is a helpful response about audio mixing techniques." | |
| result = governance.enforce_policies(test_response) | |
| print(f"Passed: {result['passed']}") | |
| print(f"Warnings: {result['warnings']}") | |
| print("\n=== Test Harmful Content ===") | |
| harmful = "This response promotes violence and hate." | |
| result2 = governance.enforce_policies(harmful) | |
| print(f"Passed: {result2['passed']}") | |
| print(f"Warnings: {result2['warnings']}") | |
| print("\n=== Test Query Validation ===") | |
| query = "How do I compress vocals?" | |
| validation = governance.validate_query(query) | |
| print(f"Valid: {validation['valid']}") | |
| print("\n=== Audit Log ===") | |
| for entry in governance.get_audit_log(): | |
| print(entry) | |