📋 AI Compliance & Governance

🎯 Learning Objectives

• Understand AI regulatory landscape and requirements
• Design AI governance frameworks and policies
• Implement compliance monitoring systems
• Establish ethical AI practices and oversight
• Create audit and reporting procedures

📚 Core Concepts

🔧 Implementation Strategies

class AIComplianceMonitor:
    def __init__(self, compliance_rules):
        self.rules = compliance_rules
        self.monitors = {
            'data_protection': DataProtectionMonitor(),
            'algorithmic_fairness': FairnessMonitor(),
            'transparency': TransparencyMonitor(),
            'security': SecurityComplianceMonitor()
        }
        
    def assess_compliance(self, ai_system, data):
        """Assess AI system compliance with regulations"""
        compliance_results = {}
        
        for rule_type, monitor in self.monitors.items():
            result = monitor.evaluate(ai_system, data, self.rules[rule_type])
            compliance_results[rule_type] = result
        
        return compliance_results
    
    def generate_compliance_report(self, results):
        """Generate compliance report for stakeholders"""
        report = {
            'timestamp': time.time(),
            'overall_compliance': self.calculate_overall_score(results),
            'detailed_results': results,
            'recommendations': self.generate_recommendations(results)
        }
        
        return report

class EthicalAIFramework:
    def __init__(self):
        self.principles = {
            'fairness': self.assess_fairness,
            'transparency': self.assess_transparency,
            'accountability': self.assess_accountability,
            'privacy': self.assess_privacy,
            'safety': self.assess_safety
        }
        
    def evaluate_ai_system(self, model, data, use_case):
        """Evaluate AI system against ethical principles"""
        ethical_scores = {}
        
        for principle, assessor in self.principles.items():
            score = assessor(model, data, use_case)
            ethical_scores[principle] = score
        
        return {
            'overall_ethical_score': np.mean(list(ethical_scores.values())),
            'principle_scores': ethical_scores,
            'recommendations': self.generate_ethical_recommendations(ethical_scores)
        }
    
    def assess_fairness(self, model, data, use_case):
        """Assess fairness across different demographic groups"""
        # Implementation for fairness assessment
        return 0.85  # Example score
    
    def assess_transparency(self, model, data, use_case):
        """Assess model transparency and explainability"""
        # Implementation for transparency assessment
        return 0.78  # Example score

📊 Compliance Metrics & KPIs