Feat:Create the Basic Ai Governance Package to use has a guide

ai_governance/risk_analyzer.py

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+"""
+Risk Analyzer Module
+Assesses privacy and ethical risks in AI models
+"""
+
+import pandas as pd
+import numpy as np
+import re
+from datetime import datetime
+
+class RiskAnalyzer:
+    """Analyze privacy and ethical risks"""
+    
+    def __init__(self, df, model_results, bias_results, protected_attributes, target_column):
+        self.df = df
+        self.model_results = model_results
+        self.bias_results = bias_results
+        self.protected_attributes = protected_attributes
+        self.target_column = target_column
+        self.results = {}
+    
+    def analyze(self):
+        """Perform comprehensive risk analysis"""
+        self.results = {
+            'privacy_risks': self._analyze_privacy_risks(),
+            'ethical_risks': self._analyze_ethical_risks(),
+            'model_performance_risks': self._analyze_model_performance_risks(),
+            'compliance_risks': self._analyze_compliance_risks(),
+            'data_quality_risks': self._analyze_data_quality_risks(),
+            'risk_categories': {},
+            'overall_risk_score': 0.0,
+            'risk_level': 'UNKNOWN'
+        }
+        
+        # Aggregate risk categories
+        self.results['risk_categories'] = self._aggregate_risk_categories()
+        
+        # Calculate overall risk score
+        self.results['overall_risk_score'] = self._calculate_overall_risk_score()
+        
+        # Determine risk level
+        self.results['risk_level'] = self._determine_risk_level()
+        
+        return self.results
+    
+    def _analyze_privacy_risks(self):
+        """Analyze privacy-related risks"""
+        privacy_risks = {
+            'pii_detected': [],
+            'sensitive_attributes': self.protected_attributes,
+            'data_minimization_score': 0.0,
+            'anonymization_level': 'NONE',
+            'exposure_risks': [],
+            'gdpr_compliance': {},
+            'recommendations': []
+        }
+        
+        # Detect PII columns
+        pii_patterns = {
+            'email': r'^.*email.*$',
+            'phone': r'^.*(phone|mobile|tel).*$',
+            'address': r'^.*(address|street|city|zip|postal).*$',
+            'name': r'^.*(name|firstname|lastname).*$',
+            'ssn': r'^.*(ssn|social.*security).*$',
+            'id': r'^.*(id|identifier|passport|license).*$',
+            'dob': r'^.*(dob|birth|birthday).*$',
+            'age': r'^.*age.*$',
+            'gender': r'^.*gender.*$'
+        }
+        
+        for col in self.df.columns:
+            col_lower = col.lower()
+            for pii_type, pattern in pii_patterns.items():
+                if re.match(pattern, col_lower):
+                    privacy_risks['pii_detected'].append({
+                        'column': col,
+                        'type': pii_type,
+                        'severity': 'HIGH' if pii_type in ['ssn', 'email', 'phone'] else 'MEDIUM'
+                    })
+                    break
+        
+        # Check data minimization
+        total_cols = len(self.df.columns)
+        essential_cols = len([col for col in self.df.columns if col != self.target_column])
+        privacy_risks['data_minimization_score'] = 1.0 - (essential_cols / total_cols) if total_cols > 0 else 0.0
+        
+        # Assess anonymization level
+        if len(privacy_risks['pii_detected']) > 5:
+            privacy_risks['anonymization_level'] = 'NONE'
+            privacy_risks['exposure_risks'].append("High number of PII columns detected without anonymization")
+        elif len(privacy_risks['pii_detected']) > 0:
+            privacy_risks['anonymization_level'] = 'PARTIAL'
+            privacy_risks['exposure_risks'].append("Some PII columns detected - consider anonymization")
+        else:
+            privacy_risks['anonymization_level'] = 'FULL'
+        
+        # GDPR compliance checks
+        privacy_risks['gdpr_compliance'] = {
+            'has_consent_mechanism': False,  # Cannot determine from data alone
+            'data_portability': True,  # CSV format allows export
+            'right_to_erasure': False,  # Cannot determine from data alone
+            'data_protection_by_design': len(privacy_risks['pii_detected']) == 0,
+            'compliance_score': 0.25
+        }
+        
+        # Recommendations
+        if len(privacy_risks['pii_detected']) > 0:
+            privacy_risks['recommendations'].append("Implement data anonymization techniques (hashing, tokenization)")
+            privacy_risks['recommendations'].append("Remove unnecessary PII columns")
+            privacy_risks['recommendations'].append("Implement access controls for sensitive data")
+        
+        privacy_risks['recommendations'].append("Implement data encryption at rest and in transit")
+        privacy_risks['recommendations'].append("Establish data retention and deletion policies")
+        privacy_risks['recommendations'].append("Conduct regular privacy impact assessments")
+        
+        return privacy_risks
+    
+    def _analyze_ethical_risks(self):
+        """Analyze ethical risks"""
+        ethical_risks = {
+            'fairness_issues': [],
+            'transparency_score': 0.0,
+            'transparency_notes': '',
+            'accountability_measures': [],
+            'social_impact_assessment': {},
+            'bias_amplification_risk': 'UNKNOWN',
+            'recommendations': []
+        }
+        
+        # Fairness issues from bias analysis
+        violations = self.bias_results.get('fairness_violations', [])
+        for violation in violations:
+            ethical_risks['fairness_issues'].append(
+                f"{violation['attribute']}: {violation['message']} (Severity: {violation['severity']})"
+            )
+        
+        # Transparency score based on model complexity
+        model_type = self.model_results.get('model_type', 'Unknown')
+        if model_type in ['LogisticRegression', 'DecisionTreeClassifier']:
+            ethical_risks['transparency_score'] = 0.9
+            ethical_risks['transparency_notes'] = "Model is highly interpretable"
+        elif model_type in ['RandomForestClassifier', 'GradientBoostingClassifier']:
+            ethical_risks['transparency_score'] = 0.6
+            ethical_risks['transparency_notes'] = "Model has moderate interpretability - feature importance available"
+        else:
+            ethical_risks['transparency_score'] = 0.3
+            ethical_risks['transparency_notes'] = "Model has low interpretability - consider using SHAP/LIME"
+        
+        # Accountability measures
+        ethical_risks['accountability_measures'] = [
+            "Model versioning and tracking",
+            "Prediction logging for audit trail",
+            "Regular bias monitoring",
+            "Human review for high-stakes decisions"
+        ]
+        
+        # Social impact assessment
+        ethical_risks['social_impact_assessment'] = {
+            'affected_groups': self.protected_attributes,
+            'potential_harms': [
+                "Unfair denial of opportunities for protected groups",
+                "Reinforcement of historical biases",
+                "Lack of recourse for affected individuals"
+            ],
+            'mitigation_strategies': [
+                "Regular fairness audits",
+                "Diverse dataset collection",
+                "Stakeholder engagement",
+                "Appeal and review mechanisms"
+            ]
+        }
+        
+        # Bias amplification risk
+        overall_bias = self.bias_results.get('overall_bias_score', 0)
+        if overall_bias > 0.5:
+            ethical_risks['bias_amplification_risk'] = 'HIGH'
+        elif overall_bias > 0.3:
+            ethical_risks['bias_amplification_risk'] = 'MEDIUM'
+        else:
+            ethical_risks['bias_amplification_risk'] = 'LOW'
+        
+        # Recommendations
+        ethical_risks['recommendations'] = [
+            "Implement regular fairness audits and monitoring",
+            "Use explainable AI techniques (SHAP, LIME) for transparency",
+            "Establish ethics review board for model deployment",
+            "Create feedback mechanisms for affected individuals",
+            "Document decision-making processes and limitations",
+            "Provide clear communication about model capabilities and limitations"
+        ]
+        
+        return ethical_risks
+    
+    def _analyze_model_performance_risks(self):
+        """Analyze risks related to model performance"""
+        risks = {
+            'performance_gaps': [],
+            'overfitting_risk': 'UNKNOWN',
+            'underfitting_risk': 'UNKNOWN',
+            'reliability_score': 0.0,
+            'recommendations': []
+        }
+        
+        metrics = self.model_results.get('metrics', {})
+        accuracy = metrics.get('accuracy', 0)
+        precision = metrics.get('precision', 0)
+        recall = metrics.get('recall', 0)
+        
+        # Check for performance issues
+        if accuracy < 0.7:
+            risks['performance_gaps'].append("Low overall accuracy - model may not be reliable")
+            risks['underfitting_risk'] = 'HIGH'
+        
+        if precision < 0.6:
+            risks['performance_gaps'].append("Low precision - high false positive rate")
+        
+        if recall < 0.6:
+            risks['performance_gaps'].append("Low recall - missing many positive cases")
+        
+        # Calculate reliability score
+        risks['reliability_score'] = (accuracy + precision + recall) / 3
+        
+        # Recommendations
+        if accuracy < 0.7:
+            risks['recommendations'].append("Consider more complex models or feature engineering")
+            risks['recommendations'].append("Collect more training data")
+        
+        if precision < 0.6 or recall < 0.6:
+            risks['recommendations'].append("Adjust classification threshold")
+            risks['recommendations'].append("Address class imbalance")
+        
+        risks['recommendations'].append("Implement continuous monitoring of model performance")
+        risks['recommendations'].append("Set up alerts for performance degradation")
+        
+        return risks
+    
+    def _analyze_compliance_risks(self):
+        """Analyze regulatory compliance risks"""
+        risks = {
+            'regulatory_frameworks': [],
+            'compliance_gaps': [],
+            'audit_readiness': 'LOW',
+            'documentation_completeness': 0.0,
+            'recommendations': []
+        }
+        
+        # Identify applicable frameworks
+        risks['regulatory_frameworks'] = [
+            'GDPR (General Data Protection Regulation)',
+            'CCPA (California Consumer Privacy Act)',
+            'AI Act (EU)',
+            'Fair Credit Reporting Act (if applicable)'
+        ]
+        
+        # Identify compliance gaps
+        privacy_risks = self.results.get('privacy_risks', {}) if 'privacy_risks' in self.results else {}
+        
+        if len(privacy_risks.get('pii_detected', [])) > 0:
+            risks['compliance_gaps'].append("Unprotected PII may violate GDPR/CCPA requirements")
+        
+        if len(self.bias_results.get('fairness_violations', [])) > 0:
+            risks['compliance_gaps'].append("Fairness violations may violate anti-discrimination laws")
+        
+        if not privacy_risks.get('gdpr_compliance', {}).get('data_protection_by_design', False):
+            risks['compliance_gaps'].append("Lack of privacy by design principles")
+        
+        # Assess audit readiness
+        if len(risks['compliance_gaps']) == 0:
+            risks['audit_readiness'] = 'HIGH'
+        elif len(risks['compliance_gaps']) <= 2:
+            risks['audit_readiness'] = 'MEDIUM'
+        else:
+            risks['audit_readiness'] = 'LOW'
+        
+        # Documentation completeness (placeholder - would need more info)
+        risks['documentation_completeness'] = 0.4
+        
+        # Recommendations
+        risks['recommendations'] = [
+            "Conduct comprehensive privacy impact assessment",
+            "Document data lineage and processing activities",
+            "Implement data subject rights (access, deletion, portability)",
+            "Establish regular compliance audits",
+            "Create model cards documenting intended use and limitations",
+            "Implement model monitoring and incident response procedures"
+        ]
+        
+        return risks
+    
+    def _analyze_data_quality_risks(self):
+        """Analyze data quality risks"""
+        risks = {
+            'missing_data': {},
+            'data_imbalance': {},
+            'outlier_risk': 'UNKNOWN',
+            'data_quality_score': 0.0,
+            'recommendations': []
+        }
+        
+        # Missing data analysis
+        missing_counts = self.df.isnull().sum()
+        missing_pct = (missing_counts / len(self.df)) * 100
+        
+        for col in self.df.columns:
+            if missing_pct[col] > 5:
+                risks['missing_data'][col] = {
+                    'count': int(missing_counts[col]),
+                    'percentage': float(missing_pct[col])
+                }
+        
+        # Class imbalance
+        if self.target_column in self.df.columns:
+            target_dist = self.df[self.target_column].value_counts()
+            imbalance_ratio = target_dist.max() / target_dist.min() if len(target_dist) > 1 else 1.0
+            
+            risks['data_imbalance'] = {
+                'ratio': float(imbalance_ratio),
+                'distribution': target_dist.to_dict(),
+                'severe': imbalance_ratio > 5
+            }
+        
+        # Calculate data quality score
+        missing_score = 1.0 - (len(risks['missing_data']) / len(self.df.columns))
+        imbalance_score = 1.0 / (1.0 + np.log1p(risks['data_imbalance'].get('ratio', 1) - 1))
+        risks['data_quality_score'] = (missing_score + imbalance_score) / 2
+        
+        # Recommendations
+        if len(risks['missing_data']) > 0:
+            risks['recommendations'].append("Address missing data through imputation or removal")
+        
+        if risks['data_imbalance'].get('severe', False):
+            risks['recommendations'].append("Use resampling techniques (SMOTE) to address class imbalance")
+            risks['recommendations'].append("Consider adjusting class weights in model training")
+        
+        risks['recommendations'].append("Implement data validation pipelines")
+        risks['recommendations'].append("Monitor data drift over time")
+        
+        return risks
+    
+    def _aggregate_risk_categories(self):
+        """Aggregate risks into categories with scores"""
+        categories = {}
+        
+        # Privacy risks
+        privacy = self.results.get('privacy_risks', {})
+        privacy_score = self._calculate_privacy_risk_score(privacy)
+        categories['privacy_risks'] = {
+            'score': privacy_score,
+            'level': self._score_to_level(privacy_score),
+            'issues': [
+                f"{len(privacy['pii_detected'])} PII columns detected",
+                f"Anonymization level: {privacy['anonymization_level']}"
+            ],
+            'recommendations': privacy['recommendations'][:3]
+        }
+        
+        # Ethical risks
+        ethical = self.results.get('ethical_risks', {})
+        ethical_score = self._calculate_ethical_risk_score(ethical)
+        categories['ethical_risks'] = {
+            'score': ethical_score,
+            'level': self._score_to_level(ethical_score),
+            'issues': ethical['fairness_issues'][:3],
+            'recommendations': ethical['recommendations'][:3]
+        }
+        
+        # Model performance risks
+        performance = self.results.get('model_performance_risks', {})
+        performance_score = 1.0 - performance.get('reliability_score', 0.5)
+        categories['model_performance_risks'] = {
+            'score': performance_score,
+            'level': self._score_to_level(performance_score),
+            'issues': performance['performance_gaps'],
+            'recommendations': performance['recommendations'][:3]
+        }
+        
+        # Compliance risks
+        compliance = self.results.get('compliance_risks', {})
+        compliance_score = len(compliance['compliance_gaps']) / 10.0
+        categories['compliance_risks'] = {
+            'score': min(compliance_score, 1.0),
+            'level': self._score_to_level(min(compliance_score, 1.0)),
+            'issues': compliance['compliance_gaps'],
+            'recommendations': compliance['recommendations'][:3]
+        }
+        
+        # Data quality risks
+        data_quality = self.results.get('data_quality_risks', {})
+        data_quality_score = 1.0 - data_quality.get('data_quality_score', 0.5)
+        categories['data_quality_risks'] = {
+            'score': data_quality_score,
+            'level': self._score_to_level(data_quality_score),
+            'issues': [
+                f"{len(data_quality['missing_data'])} columns with missing data",
+                f"Class imbalance ratio: {data_quality['data_imbalance'].get('ratio', 1):.2f}"
+            ],
+            'recommendations': data_quality['recommendations'][:3]
+        }
+        
+        return categories
+    
+    def _calculate_privacy_risk_score(self, privacy_risks):
+        """Calculate privacy risk score (0-1, higher is worse)"""
+        pii_count = len(privacy_risks.get('pii_detected', []))
+        pii_score = min(pii_count / 10, 1.0)
+        
+        anon_level = privacy_risks.get('anonymization_level', 'NONE')
+        anon_score = {'FULL': 0.0, 'PARTIAL': 0.5, 'NONE': 1.0}.get(anon_level, 0.5)
+        
+        gdpr_score = 1.0 - privacy_risks.get('gdpr_compliance', {}).get('compliance_score', 0)
+        
+        return (pii_score * 0.4 + anon_score * 0.3 + gdpr_score * 0.3)
+    
+    def _calculate_ethical_risk_score(self, ethical_risks):
+        """Calculate ethical risk score (0-1, higher is worse)"""
+        fairness_score = len(ethical_risks.get('fairness_issues', [])) / 10
+        transparency_score = 1.0 - ethical_risks.get('transparency_score', 0.5)
+        bias_amp = ethical_risks.get('bias_amplification_risk', 'MEDIUM')
+        bias_score = {'LOW': 0.2, 'MEDIUM': 0.5, 'HIGH': 0.9}.get(bias_amp, 0.5)
+        
+        return (fairness_score * 0.4 + transparency_score * 0.3 + bias_score * 0.3)
+    
+    def _calculate_overall_risk_score(self):
+        """Calculate overall risk score"""
+        category_scores = []
+        
+        for category, details in self.results.get('risk_categories', {}).items():
+            category_scores.append(details['score'])
+        
+        overall = np.mean(category_scores) if category_scores else 0.5
+        return float(min(overall, 1.0))
+    
+    def _determine_risk_level(self):
+        """Determine overall risk level"""
+        score = self.results.get('overall_risk_score', 0.5)
+        return self._score_to_level(score)
+    
+    def _score_to_level(self, score):
+        """Convert score to risk level"""
+        if score >= 0.7:
+            return 'HIGH'
+        elif score >= 0.4:
+            return 'MEDIUM'
+        else:
+            return 'LOW'