def validate_document_format(document):
    validation_results = {
        "format_score": 0,
        "errors": [],
        "warnings": []
    }
    
    # 必須セクション確認
    required_sections = ["概要", "システム構成", "機能仕様"]
    for section in required_sections:
        if section not in document.sections:
            validation_results["errors"].append(
                f"必須セクション '{section}' が不足"
            )
    
    # 文字数チェック
    if len(document.content) < 2000:
        validation_results["warnings"].append(
            "文書が短すぎる可能性（2000文字未満）"
        )
    
    # 禁止用語チェック
    prohibited_terms = ["推測", "おそらく", "可能性"]
    for term in prohibited_terms:
        if term in document.content:
            validation_results["errors"].append(
                f"禁止用語 '{term}' が含まれています"
            )
    
    return validation_results

class ExpertVerificationSystem:
    def __init__(self):
        self.expert_db = ExpertDatabase()
        self.fact_extractor = FactExtractor()
    
    def request_verification(self, content, domain):
        """専門家への確認依頼"""
        # 重要事実の抽出
        key_facts = self.fact_extractor.extract_key_facts(content)
        
        # 適切な専門家の選定
        expert = self.expert_db.find_best_expert(domain, key_facts)
        
        # 構造化された確認依頼
        verification_request = {
            'facts_to_verify': key_facts,
            'context': content,
            'urgency': 'normal',
            'expected_response_time': '2時間',
            'format': 'structured_feedback'
        }
        
        return expert.request_verification(verification_request)

class InformationFreshnessManager:
    def __init__(self):
        self.update_schedules = {
            'realtime': timedelta(minutes=5),
            'daily': timedelta(days=1),
            'monthly': timedelta(days=30),
            'yearly': timedelta(days=365)
        }
    
    def check_information_freshness(self, content):
        """情報の鮮度チェック"""
        
        stale_information = []
        
        for fact in content.facts:
            category = self.categorize_information(fact)
            max_age = self.update_schedules[category]
            
            if fact.last_updated + max_age < datetime.now():
                stale_information.append({
                    'fact': fact,
                    'category': category,
                    'staleness': datetime.now() - fact.last_updated
                })
        
        return stale_information
    
    def auto_update_stale_information(self, stale_info):
        """古い情報の自動更新"""
        
        for item in stale_info:
            try:
                # 最新情報の取得
                latest_data = self.fetch_latest_data(item['fact'])
                
                # 情報の更新
                item['fact'].update(latest_data)
                item['fact'].last_updated = datetime.now()
                
                # 変更の記録
                self.log_information_update(item['fact'], latest_data)
                
            except Exception as e:
                # 更新失敗時の処理
                self.handle_update_failure(item['fact'], e)

class InformationClassifier:
    def __init__(self):
        self.classification_rules = {
            'personal_info': {
                'patterns': [
                    r'\d{4}-\d{4}-\d{4}-\d{4}',  # クレジットカード
                    r'\d{3}-\d{4}-\d{4}',        # 電話番号
                    r'[\w\.-]+@[\w\.-]+\.\w+'    # メールアドレス
                ],
                'level': 'TOP_SECRET'
            },
            'financial_data': {
                'keywords': ['売上', '利益', '原価', '予算'],
                'level': 'CONFIDENTIAL'
            },
            'strategic_info': {
                'keywords': ['戦略', '計画', 'M&A', '買収'],
                'level': 'CONFIDENTIAL'
            }
        }
    
    def classify_content(self, content):
        """コンテンツの機密度分類"""
        max_level = 'PUBLIC'
        detected_patterns = []
        
        for category, rules in self.classification_rules.items():
            if self.matches_rules(content, rules):
                if self.get_level_priority(rules['level']) > self.get_level_priority(max_level):
                    max_level = rules['level']
                detected_patterns.append(category)
        
        return {
            'classification_level': max_level,
            'detected_patterns': detected_patterns,
            'protection_requirements': self.get_protection_requirements(max_level)
        }

class DataSanitizer:
    def __init__(self):
        self.sensitive_patterns = {
            'credit_card': {
                'pattern': r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b',
                'replacement': '[クレジットカード番号]'
            },
            'phone_number': {
                'pattern': r'\b\d{2,4}[-\s]?\d{2,4}[-\s]?\d{4}\b',
                'replacement': '[電話番号]'
            },
            'email': {
                'pattern': r'\b[\w\.-]+@[\w\.-]+\.\w+\b',
                'replacement': '[メールアドレス]'
            },
            'financial_amount': {
                'pattern': r'\b\d{1,3}(,\d{3})*円\b',
                'replacement': '[金額]',
                'condition': 'amount_over_threshold'
            }
        }
    
    def sanitize_input(self, text, classification_level):
        """入力データのサニタイゼーション"""
        
        if classification_level in ['CONFIDENTIAL', 'TOP_SECRET']:
            # 機密情報の完全マスキング
            for pattern_name, pattern_info in self.sensitive_patterns.items():
                text = re.sub(
                    pattern_info['pattern'],
                    pattern_info['replacement'],
                    text
                )
        
        elif classification_level == 'INTERNAL':
            # 部分的なマスキング（末尾数桁のみ表示等）
            text = self.partial_masking(text)
        
        return {
            'sanitized_text': text,
            'masking_applied': True,
            'original_classification': classification_level
        }
    
    def partial_masking(self, text):
        """部分マスキング（内部情報レベル）"""
        # クレジットカード番号の末尾4桁のみ表示
        text = re.sub(
            r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?(\d{4})\b',
            r'****-****-****-\1',
            text
        )
        return text

class OutputFilter:
    def __init__(self):
        self.filter_rules = {
            'remove_personal_info': True,
            'mask_financial_data': True,
            'redact_strategic_info': True,
            'anonymize_company_names': False  # 設定により調整
        }
    
    def filter_output(self, ai_output, target_audience):
        """出力データのフィルタリング"""
        
        filtered_output = ai_output
        applied_filters = []
        
        # 対象読者に応じたフィルタリング
        if target_audience == 'external':
            # 外部向けは最も厳格なフィルタリング
            filtered_output = self.apply_external_filters(filtered_output)
            applied_filters.extend(['external_filter'])
        
        elif target_audience == 'internal':
            # 内部向けは中程度のフィルタリング
            filtered_output = self.apply_internal_filters(filtered_output)
            applied_filters.extend(['internal_filter'])
        
        # 機密情報の完全除去
        if self.contains_top_secret_info(filtered_output):
            filtered_output = self.remove_top_secret_info(filtered_output)
            applied_filters.append('top_secret_removal')
        
        return {
            'filtered_content': filtered_output,
            'applied_filters': applied_filters,
            'safety_level': self.calculate_safety_level(filtered_output)
        }

class KAnonymizer:
    def __init__(self, k=5):
        self.k = k  # 最小グループサイズ
        self.quasi_identifiers = [
            'age', 'gender', 'zipcode', 'occupation'
        ]
    
    def anonymize_dataset(self, dataset):
        """データセットのk-匿名化"""
        
        anonymized_data = []
        
        # 準識別子に基づくグループ化
        groups = self.group_by_quasi_identifiers(dataset)
        
        for group in groups:
            if len(group) >= self.k:
                # k以上のグループはそのまま使用
                anonymized_group = self.generalize_group(group)
                anonymized_data.extend(anonymized_group)
            else:
                # k未満のグループは他のグループと統合
                merged_group = self.merge_with_similar_group(group, groups)
                anonymized_group = self.generalize_group(merged_group)
                anonymized_data.extend(anonymized_group)
        
        return anonymized_data
    
    def generalize_group(self, group):
        """グループ内のデータ一般化"""
        generalized_group = []
        
        for record in group:
            generalized_record = record.copy()
            
            # 年齢を年代に変換
            age = record['age']
            generalized_record['age'] = f"{(age // 10) * 10}代"
            
            # 郵便番号を地域に変換
            zipcode = record['zipcode']
            generalized_record['zipcode'] = zipcode[:3] + "****"
            
            generalized_group.append(generalized_record)
        
        return generalized_group

使用例：
元データ：
[
    {'age': 25, 'gender': '男', 'zipcode': '1000001', 'occupation': '会社員'},
    {'age': 27, 'gender': '男', 'zipcode': '1000002', 'occupation': '会社員'},
    {'age': 23, 'gender': '女', 'zipcode': '1000003', 'occupation': '会社員'}
]

k-匿名化後：
[
    {'age': '20代', 'gender': '男', 'zipcode': '100****', 'occupation': '会社員'},
    {'age': '20代', 'gender': '男', 'zipcode': '100****', 'occupation': '会社員'},
    {'age': '20代', 'gender': '女', 'zipcode': '100****', 'occupation': '会社員'}
]

import numpy as np
from scipy import stats

class DifferentialPrivacy:
    def __init__(self, epsilon=1.0):
        self.epsilon = epsilon  # プライバシー予算
        self.sensitivity = 1.0  # 関数の感度
    
    def add_laplace_noise(self, true_value):
        """ラプラスノイズの追加"""
        scale = self.sensitivity / self.epsilon
        noise = np.random.laplace(0, scale)
        return true_value + noise
    
    def private_count(self, dataset, condition):
        """プライベートカウント"""
        true_count = sum(1 for record in dataset if condition(record))
        noisy_count = self.add_laplace_noise(true_count)
        return max(0, int(round(noisy_count)))  # 負の値を防ぐ
    
    def private_mean(self, values, min_val, max_val):
        """プライベート平均値"""
        if not values:
            return 0
        
        # 値の正規化（[0,1]区間に変換）
        normalized_values = [
            (v - min_val) / (max_val - min_val) for v in values
        ]
        
        true_mean = sum(normalized_values) / len(normalized_values)
        noisy_mean = self.add_laplace_noise(true_mean)
        
        # 元のスケールに戻す
        return noisy_mean * (max_val - min_val) + min_val
    
    def private_histogram(self, dataset, bins):
        """プライベートヒストグラム"""
        histogram = {}
        
        for bin_name in bins:
            count = sum(1 for record in dataset if record['category'] == bin_name)
            noisy_count = self.add_laplace_noise(count)
            histogram[bin_name] = max(0, int(round(noisy_count)))
        
        return histogram

使用例：
# 年収データの差分プライベート統計
dp = DifferentialPrivacy(epsilon=0.5)

# プライベートカウント
high_income_count = dp.private_count(
    employee_data, 
    lambda x: x['salary'] > 10000000
)

# プライベート平均年収
avg_salary = dp.private_mean(
    [emp['salary'] for emp in employee_data],
    min_val=3000000,
    max_val=20000000
)

print(f"高所得者数: {high_income_count}名（概算）")
print(f"平均年収: {avg_salary:,.0f}円（概算）")

class GDPRComplianceSystem:
    def __init__(self):
        self.data_processing_log = DataProcessingLog()
        self.personal_data_store = PersonalDataStore()
    
    def handle_access_request(self, data_subject_id):
        """アクセス権への対応"""
        
        # 個人データの特定
        personal_data = self.personal_data_store.get_data(data_subject_id)
        
        # 処理活動の記録取得
        processing_activities = self.data_processing_log.get_activities(
            data_subject_id
        )
        
        # 回答書の生成
        access_response = {
            'data_subject_id': data_subject_id,
            'personal_data': personal_data,
            'processing_purposes': [
                activity['purpose'] for activity in processing_activities
            ],
            'data_categories': list(personal_data.keys()),
            'storage_period': self.get_storage_period(data_subject_id),
            'third_party_recipients': self.get_third_party_recipients(data_subject_id),
            'response_date': datetime.now(),
            'response_format': 'structured_data'
        }
        
        return access_response

def handle_erasure_request(self, data_subject_id, erasure_reason):
    """削除権（忘れられる権利）への対応"""
    
    # 削除可能性の評価
    erasure_assessment = self.assess_erasure_eligibility(
        data_subject_id, erasure_reason
    )
    
    if erasure_assessment['eligible']:
        # データ削除の実行
        deletion_result = self.execute_data_deletion(data_subject_id)
        
        # 第三者への通知
        self.notify_third_parties_of_deletion(data_subject_id)
        
        # 削除記録の保持
        self.log_deletion_activity(data_subject_id, deletion_result)
        
        return {
            'status': 'completed',
            'deletion_date': datetime.now(),
            'deleted_data_categories': deletion_result['categories'],
            'third_party_notifications': deletion_result['notifications']
        }
    else:
        return {
            'status': 'declined',
            'reason': erasure_assessment['decline_reason'],
            'legal_basis': erasure_assessment['legal_basis']
        }

def handle_portability_request(self, data_subject_id, target_format='json'):
    """データポータビリティ権への対応"""
    
    # ポータブルデータの特定
    portable_data = self.get_portable_data(data_subject_id)
    
    # 指定形式での出力
    if target_format == 'json':
        formatted_data = json.dumps(portable_data, ensure_ascii=False, indent=2)
    elif target_format == 'csv':
        formatted_data = self.convert_to_csv(portable_data)
    elif target_format == 'xml':
        formatted_data = self.convert_to_xml(portable_data)
    
    # セキュアな配信準備
    secure_package = self.create_secure_data_package(
        data=formatted_data,
        recipient=data_subject_id,
        format=target_format
    )
    
    return {
        'package_id': secure_package['id'],
        'download_url': secure_package['secure_url'],
        'expiry_date': secure_package['expiry'],
        'format': target_format,
        'file_size': len(formatted_data)
    }

class JapanPrivacyLawCompliance:
    def __init__(self):
        self.consent_manager = ConsentManager()
        self.pseudonymization_engine = PseudonymizationEngine()
        self.breach_notification_system = BreachNotificationSystem()
    
    def handle_personal_related_info(self, info_data, purpose):
        """個人関連情報の適切な取扱い"""
        
        # 個人関連情報の該当性確認
        if self.is_personal_related_info(info_data):
            # 本人同意の確認
            consent_status = self.consent_manager.check_consent(
                info_data['identifier'], purpose
            )
            
            if not consent_status['valid']:
                return {
                    'processing_allowed': False,
                    'reason': 'insufficient_consent',
                    'required_action': 'obtain_explicit_consent'
                }
        
        return {
            'processing_allowed': True,
            'compliance_notes': 'personal_related_info_handled_properly'
        }
    
    def create_pseudonymized_data(self, personal_data, purpose):
        """仮名加工情報の作成"""
        
        # 仮名加工の実行
        pseudonymized_data = self.pseudonymization_engine.process(
            data=personal_data,
            purpose=purpose,
            deletion_info=True  # 復元情報の削除
        )
        
        # 取扱い記録の作成
        handling_record = {
            'creation_date': datetime.now(),
            'original_data_volume': len(personal_data),
            'pseudonymization_method': 'k_anonymity_with_suppression',
            'purpose': purpose,
            'retention_period': '5年間',
            'security_measures': ['暗号化', 'アクセス制御', '監査ログ']
        }
        
        return {
            'pseudonymized_data': pseudonymized_data,
            'handling_record': handling_record,
            'compliance_status': 'compliant'
        }
    
    def handle_data_breach(self, breach_details):
        """漏えい等事案への対応"""
        
        # 重大性の評価
        severity_assessment = self.assess_breach_severity(breach_details)
        
        if severity_assessment['requires_notification']:
            # 個人情報保護委員会への報告
            ppc_notification = self.notify_privacy_commission(
                breach_details, severity_assessment
            )
            
            # 本人への通知
            individual_notification = self.notify_affected_individuals(
                breach_details['affected_individuals']
            )
            
            return {
                'ppc_notification': ppc_notification,
                'individual_notification': individual_notification,
                'compliance_status': 'notifications_completed'
            }
        
        return {
            'notification_required': False,
            'internal_handling': 'documented_and_addressed'
        }

class InternationalDataTransferManager:
    def __init__(self):
        self.adequacy_decisions = {
            'EU': ['アンドラ', 'アルゼンチン', 'カナダ', 'フェロー諸島'],
            'Japan': ['EU', 'UK', '韓国'],
            'UK': ['EU', 'アンドラ', 'アルゼンチン', 'カナダ']
        }
        self.sccs_templates = SCCTemplateManager()
    
    def assess_transfer_legality(self, source_country, destination_country, data_type):
        """データ移転の適法性評価"""
        
        # 十分性認定の確認
        if destination_country in self.adequacy_decisions.get(source_country, []):
            return {
                'transfer_allowed': True,
                'legal_basis': 'adequacy_decision',
                'additional_safeguards_required': False
            }
        
        # 適切な保護措置の要件確認
        required_safeguards = self.determine_required_safeguards(
            source_country, destination_country, data_type
        )
        
        return {
            'transfer_allowed': True,
            'legal_basis': 'appropriate_safeguards',
            'required_safeguards': required_safeguards,
            'additional_safeguards_required': True
        }
    
    def implement_transfer_safeguards(self, transfer_details):
        """移転保護措置の実装"""
        
        safeguards = []
        
        # 標準契約条項（SCC）の適用
        if 'scc' in transfer_details['required_safeguards']:
            scc_contract = self.sccs_templates.generate_contract(
                exporter=transfer_details['source_entity'],
                importer=transfer_details['destination_entity'],
                data_categories=transfer_details['data_categories']
            )
            safeguards.append({
                'type': 'standard_contractual_clauses',
                'contract_id': scc_contract['id'],
                'execution_date': datetime.now()
            })
        
        # 技術的保護措置
        if 'technical_safeguards' in transfer_details['required_safeguards']:
            encryption_config = self.implement_encryption(
                transfer_details['data']
            )
            safeguards.append({
                'type': 'encryption',
                'algorithm': encryption_config['algorithm'],
                'key_management': encryption_config['key_management']
            })
        
        return {
            'implemented_safeguards': safeguards,
            'transfer_authorized': True,
            'monitoring_requirements': self.get_monitoring_requirements(transfer_details)
        }

class BiasDetectionSystem:
    def __init__(self):
        self.protected_attributes = [
            'gender', 'age', 'race', 'religion', 'nationality'
        ]
        self.fairness_metrics = FairnessMetrics()
    
    def detect_statistical_bias(self, predictions, ground_truth, protected_attrs):
        """統計的バイアスの検出"""
        
        bias_analysis = {}
        
        for attr in self.protected_attributes:
            if attr in protected_attrs:
                # 群間での予測結果比較
                group_analysis = self.analyze_group_differences(
                    predictions, protected_attrs[attr]
                )
                
                # 複数の公平性指標での評価
                fairness_scores = {
                    'statistical_parity': self.fairness_metrics.statistical_parity(
                        predictions, protected_attrs[attr]
                    ),
                    'equal_opportunity': self.fairness_metrics.equal_opportunity(
                        predictions, ground_truth, protected_attrs[attr]
                    ),
                    'predictive_equality': self.fairness_metrics.predictive_equality(
                        predictions, ground_truth, protected_attrs[attr]
                    )
                }
                
                bias_analysis[attr] = {
                    'group_differences': group_analysis,
                    'fairness_scores': fairness_scores,
                    'bias_detected': any(score < 0.8 for score in fairness_scores.values())
                }
        
        return bias_analysis
    
    def analyze_group_differences(self, predictions, group_labels):
        """群間差異の詳細分析"""
        
        unique_groups = set(group_labels)
        group_stats = {}
        
        for group in unique_groups:
            group_predictions = [
                pred for pred, label in zip(predictions, group_labels) 
                if label == group
            ]
            
            group_stats[group] = {
                'positive_rate': sum(group_predictions) / len(group_predictions),
                'sample_size': len(group_predictions),
                'confidence_interval': self.calculate_confidence_interval(group_predictions)
            }
        
        # 最大群間格差の計算
        positive_rates = [stats['positive_rate'] for stats in group_stats.values()]
        max_difference = max(positive_rates) - min(positive_rates)
        
        return {
            'group_statistics': group_stats,
            'max_group_difference': max_difference,
            'statistically_significant': max_difference > 0.1  # 10%以上の差
        }

実使用例：
# 採用スクリーニングでのバイアス検出
bias_detector = BiasDetectionSystem()

# 性別・年齢でのバイアス分析
bias_results = bias_detector.detect_statistical_bias(
    predictions=hiring_predictions,
    ground_truth=actual_performance,
    protected_attrs={
        'gender': candidate_genders,
        'age': candidate_ages
    }
)

# 結果の解釈
for attribute, analysis in bias_results.items():
    if analysis['bias_detected']:
        print(f"⚠ {attribute}によるバイアスが検出されました")
        print(f"最大群間格差: {analysis['group_differences']['max_group_difference']:.2%}")

class LanguageBiasDetector:
    def __init__(self):
        self.occupation_gendered_words = {
            'nurse': {'expected_neutral': 0.5, 'threshold': 0.3},
            'engineer': {'expected_neutral': 0.5, 'threshold': 0.3},
            'teacher': {'expected_neutral': 0.5, 'threshold': 0.3},
            'ceo': {'expected_neutral': 0.5, 'threshold': 0.3}
        }
        
        self.sentiment_analyzer = SentimentAnalyzer()
        self.stereotype_detector = StereotypeDetector()
    
    def detect_occupational_bias(self, generated_texts):
        """職業関連バイアスの検出"""
        
        occupation_gender_analysis = {}
        
        for occupation in self.occupation_gendered_words:
            # 職業に関連する文章を抽出
            relevant_texts = self.extract_occupation_contexts(
                generated_texts, occupation
            )
            
            # 性別代名詞の使用傾向分析
            gender_usage = self.analyze_gender_pronoun_usage(
                relevant_texts, occupation
            )
            
            # バイアス判定
            expected_ratio = self.occupation_gendered_words[occupation]['expected_neutral']
            threshold = self.occupation_gendered_words[occupation]['threshold']
            
            bias_detected = abs(gender_usage['male_ratio'] - expected_ratio) > threshold
            
            occupation_gender_analysis[occupation] = {
                'male_pronoun_ratio': gender_usage['male_ratio'],
                'female_pronoun_ratio': gender_usage['female_ratio'],
                'neutral_pronoun_ratio': gender_usage['neutral_ratio'],
                'bias_detected': bias_detected,
                'bias_direction': 'male' if gender_usage['male_ratio'] > expected_ratio else 'female',
                'sample_size': len(relevant_texts)
            }
        
        return occupation_gender_analysis
    
    def detect_sentiment_bias(self, generated_texts, demographic_groups):
        """感情的バイアスの検出"""
        
        sentiment_analysis = {}
        
        for group in demographic_groups:
            # グループに関連する文章の抽出
            group_texts = self.extract_demographic_contexts(
                generated_texts, group
            )
            
            # 感情分析の実行
            sentiment_scores = [
                self.sentiment_analyzer.analyze(text) 
                for text in group_texts
            ]
            
            # 統計的分析
            avg_sentiment = sum(sentiment_scores) / len(sentiment_scores)
            sentiment_variance = np.var(sentiment_scores)
            
            sentiment_analysis[group] = {
                'average_sentiment': avg_sentiment,
                'sentiment_variance': sentiment_variance,
                'positive_ratio': sum(1 for s in sentiment_scores if s > 0.1) / len(sentiment_scores),
                'negative_ratio': sum(1 for s in sentiment_scores if s < -0.1) / len(sentiment_scores),
                'sample_size': len(group_texts)
            }
        
        # 群間比較でバイアス検出
        sentiment_differences = self.calculate_sentiment_differences(sentiment_analysis)
        
        return {
            'group_analysis': sentiment_analysis,
            'bias_indicators': sentiment_differences,
            'overall_bias_detected': sentiment_differences['max_difference'] > 0.2
        }

使用例：
# 人事評価文書でのバイアス検出
language_bias_detector = LanguageBiasDetector()

# 職業バイアスの検出
occupational_bias = language_bias_detector.detect_occupational_bias(
    generated_performance_reviews
)

# 感情バイアスの検出
sentiment_bias = language_bias_detector.detect_sentiment_bias(
    generated_performance_reviews,
    demographic_groups=['male', 'female', 'young', 'senior']
)

# アラート生成
if occupational_bias['engineer']['bias_detected']:
    print("⚠ エンジニア職において性別バイアスが検出されました")
    print(f"男性代名詞使用率: {occupational_bias['engineer']['male_pronoun_ratio']:.1%}")

class DiversityInclusionMetrics:
    def __init__(self):
        self.diversity_dimensions = {
            'demographic': ['gender', 'age', 'ethnicity', 'nationality'],
            'socioeconomic': ['education', 'income_level', 'geographic_region'],
            'professional': ['industry_experience', 'role_level', 'specialization'],
            'cognitive': ['thinking_style', 'problem_solving_approach', 'communication_style']
        }
        
        self.inclusion_indicators = [
            'representation_rate',
            'participation_rate', 
            'influence_rate',
            'satisfaction_rate'
        ]
    
    def calculate_diversity_index(self, population_data):
        """多様性指数の計算"""
        
        diversity_scores = {}
        
        for dimension, attributes in self.diversity_dimensions.items():
            dimension_score = 0
            
            for attribute in attributes:
                if attribute in population_data:
                    # シャノン多様性指数の計算
                    attribute_diversity = self.calculate_shannon_diversity(
                        population_data[attribute]
                    )
                    dimension_score += attribute_diversity
            
            # 次元内平均
            diversity_scores[dimension] = dimension_score / len(attributes)
        
        # 総合多様性指数
        overall_diversity = sum(diversity_scores.values()) / len(diversity_scores)
        
        return {
            'overall_diversity_index': overall_diversity,
            'dimension_scores': diversity_scores,
            'diversity_level': self.categorize_diversity_level(overall_diversity)
        }
    
    def calculate_shannon_diversity(self, attribute_distribution):
        """シャノン多様性指数の計算"""
        
        total = sum(attribute_distribution.values())
        diversity_index = 0
        
        for count in attribute_distribution.values():
            if count > 0:
                proportion = count / total
                diversity_index -= proportion * math.log2(proportion)
        
        return diversity_index
    
    def assess_inclusion_quality(self, interaction_data, outcome_data):
        """包括性の質的評価"""
        
        inclusion_assessment = {}
        
        for dimension in self.diversity_dimensions:
            # 参加率の分析
            participation_analysis = self.analyze_participation_patterns(
                interaction_data, dimension
            )
            
            # 成果への影響分析
            outcome_analysis = self.analyze_outcome_patterns(
                outcome_data, dimension
            )
            
            # 満足度分析
            satisfaction_analysis = self.analyze_satisfaction_patterns(
                interaction_data, dimension
            )
            
            inclusion_assessment[dimension] = {
                'participation_equity': participation_analysis['equity_score'],
                'outcome_equity': outcome_analysis['equity_score'],
                'satisfaction_equity': satisfaction_analysis['equity_score'],
                'overall_inclusion_score': (
                    participation_analysis['equity_score'] * 0.4 +
                    outcome_analysis['equity_score'] * 0.4 +
                    satisfaction_analysis['equity_score'] * 0.2
                )
            }
        
        return inclusion_assessment

実装例：
# AI招聘システムの多様性評価
diversity_metrics = DiversityInclusionMetrics()

# 候補者プールの多様性分析
candidate_diversity = diversity_metrics.calculate_diversity_index({
    'gender': {'male': 120, 'female': 80, 'other': 5},
    'age': {'20s': 85, 'ƒ30s': 90, '40s': 25, '50+': 5},
    'ethnicity': {'asian': 130, 'white': 45, 'hispanic': 20, 'black': 10}
})

# 選考プロセスの包括性評価
inclusion_quality = diversity_metrics.assess_inclusion_quality(
    interaction_data=interview_data,
    outcome_data=hiring_outcomes
)

print(f"全体多様性指数: {candidate_diversity['overall_diversity_index']:.2f}")
print(f"包括性スコア: {inclusion_quality['demographic']['overall_inclusion_score']:.2f}")

class FairnessMonitoringSystem:
    def __init__(self):
        self.fairness_thresholds = {
            'statistical_parity': 0.8,
            'equal_opportunity': 0.8,
            'predictive_equality': 0.8,
            'demographic_parity': 0.1  # 最大許容差
        }
        
        self.alert_system = AlertSystem()
        self.bias_mitigation = BiasMitigationEngine()
    
    def continuous_fairness_monitoring(self, predictions_stream, metadata_stream):
        """継続的公平性監視"""
        
        monitoring_results = {
            'timestamp': datetime.now(),
            'fairness_violations': [],
            'trend_analysis': {},
            'mitigation_recommendations': []
        }
        
        # リアルタイム公平性チェック
        current_fairness = self.calculate_realtime_fairness(
            predictions_stream, metadata_stream
        )
        
        # 閾値違反の検出
        for metric, score in current_fairness.items():
            if score < self.fairness_thresholds.get(metric, 0.8):
                violation = {
                    'metric': metric,
                    'current_score': score,
                    'threshold': self.fairness_thresholds[metric],
                    'severity': self.calculate_severity(score, metric),
                    'affected_groups': self.identify_affected_groups(metric, predictions_stream, metadata_stream)
                }
                monitoring_results['fairness_violations'].append(violation)
        
        # トレンド分析
        monitoring_results['trend_analysis'] = self.analyze_fairness_trends()
        
        # 自動的な軽減策の提案
        if monitoring_results['fairness_violations']:
            monitoring_results['mitigation_recommendations'] = (
                self.bias_mitigation.recommend_interventions(
                    monitoring_results['fairness_violations']
                )
            )
        
        # アラート送信
        if monitoring_results['fairness_violations']:
            self.send_fairness_alerts(monitoring_results)
        
        return monitoring_results
    
    def automated_bias_correction(self, detected_violations):
        """自動バイアス補正"""
        
        correction_actions = []
        
        for violation in detected_violations:
            if violation['severity'] == 'high':
                # 高重要度：即座の介入
                if violation['metric'] == 'statistical_parity':
                    # 統計的パリティ違反の補正
                    correction = self.bias_mitigation.apply_threshold_adjustment(
                        affected_groups=violation['affected_groups'],
                        target_parity=self.fairness_thresholds['statistical_parity']
                    )
                    correction_actions.append(correction)
                    
            elif violation['severity'] == 'medium':
                # 中重要度：警告付きで継続監視
                self.alert_system.send_warning(violation)
                
        return {
            'applied_corrections': correction_actions,
            'monitoring_enhanced': True,
            'next_review_scheduled': datetime.now() + timedelta(hours=4)
        }

monitoring_dashboard = {
    'real_time_metrics': {
        'statistical_parity': 0.82,
        'equal_opportunity': 0.79,  # 閾値違反
        'demographic_parity_diff': 0.12  # 閾値違反
    },
    'alerts': [
        {
            'type': 'fairness_violation',
            'metric': 'equal_opportunity',
            'affected_group': 'age_50_plus',
            'severity': 'medium',
            'timestamp': '2024-04-15 14:30:00'
        }
    ],
    'trend_status': 'declining_fairness',
    'recommended_actions': [
        'Adjust age-related feature weights',
        'Increase representation in training data',
        'Implement post-processing calibration'
    ]
}

class FairnessTrendAnalyzer:
    def __init__(self):
        self.historical_data = FairnessHistoryDB()
        self.trend_detector = TrendDetectionEngine()
        self.forecasting_model = FairnessForecaster()
    
    def analyze_long_term_trends(self, time_period='12_months'):
        """長期公平性トレンドの分析"""
        
        # 履歴データの取得
        historical_fairness = self.historical_data.get_fairness_metrics(
            period=time_period
        )
        
        trend_analysis = {}
        
        for metric in ['statistical_parity', 'equal_opportunity', 'demographic_parity']:
            # トレンド検出
            trend_info = self.trend_detector.detect_trend(
                historical_fairness[metric]['timeseries']
            )
            
            # 季節性・周期性の分析
            seasonality = self.analyze_seasonality(
                historical_fairness[metric]['timeseries']
            )
            
            # 変化点の検出
            changepoints = self.trend_detector.detect_changepoints(
                historical_fairness[metric]['timeseries']
            )
            
            trend_analysis[metric] = {
                'overall_trend': trend_info['direction'],  # 'improving', 'declining', 'stable'
                'trend_strength': trend_info['strength'],
                'seasonal_patterns': seasonality,
                'significant_changes': changepoints,
                'prediction_next_quarter': self.forecasting_model.predict(
                    historical_fairness[metric]['timeseries'], periods=3
                )
            }
        
        return {
            'trend_summary': trend_analysis,
            'overall_fairness_trajectory': self.calculate_overall_trajectory(trend_analysis),
            'risk_assessment': self.assess_future_risks(trend_analysis),
            'intervention_recommendations': self.recommend_interventions(trend_analysis)
        }
    
    def detect_fairness_degradation_patterns(self, recent_data):
        """公平性劣化パターンの検出"""
        
        degradation_indicators = {
            'rapid_decline': False,
            'gradual_erosion': False,
            'cyclical_degradation': False,
            'group_specific_issues': []
        }
        
        # 急激な劣化の検出
        for metric, values in recent_data.items():
            if len(values) >= 7:  # 最低1週間のデータ
                recent_avg = np.mean(values[-7:])
                previous_avg = np.mean(values[-14:-7]) if len(values) >= 14 else np.mean(values[:-7])
                
                if (previous_avg - recent_avg) > 0.1:  # 10%以上の急激な劣化
                    degradation_indicators['rapid_decline'] = True
        
        # 漸進的劣化の検出
        for metric, values in recent_data.items():
            if len(values) >= 30:  # 最低1ヶ月のデータ
                slope, _, r_value, _, _ = stats.linregress(range(len(values)), values)
                if slope < -0.001 and abs(r_value) > 0.7:  # 有意な下降トレンド
                    degradation_indicators['gradual_erosion'] = True
        
        return degradation_indicators
    
    def generate_fairness_report(self, analysis_results):
        """公平性分析レポートの生成"""
        
        report = {
            'executive_summary': self.create_executive_summary(analysis_results),
            'detailed_analysis': analysis_results,
            'risk_matrix': self.create_risk_matrix(analysis_results),
            'action_plan': self.create_action_plan(analysis_results),
            'monitoring_recommendations': self.create_monitoring_plan(analysis_results)
        }
        
        return report

使用例：
# 年次公平性レビュー
trend_analyzer = FairnessTrendAnalyzer()

# 12ヶ月間のトレンド分析
annual_trends = trend_analyzer.analyze_long_term_trends('12_months')

# 劣化パターンの検出
recent_fairness_data = get_recent_fairness_metrics(days=30)
degradation_patterns = trend_analyzer.detect_fairness_degradation_patterns(
    recent_fairness_data
)

# 包括的レポートの生成
fairness_report = trend_analyzer.generate_fairness_report({
    'trends': annual_trends,
    'degradation_patterns': degradation_patterns,
    'current_status': get_current_fairness_status()
})

print("=== 年次公平性レポート ===")
print(f"全体的傾向: {annual_trends['overall_fairness_trajectory']}")
print(f"リスクレベル: {annual_trends['risk_assessment']['overall_risk']}")
if degradation_patterns['rapid_decline']:
    print("⚠ 急激な公平性劣化が検出されました")

class AdaptiveFairnessSystem:
    def __init__(self):
        self.mitigation_strategies = {
            'preprocessing': PreprocessingMitigation(),
            'inprocessing': InprocessingMitigation(),
            'postprocessing': PostprocessingMitigation()
        }
        self.effectiveness_tracker = EffectivenessTracker()
    
    def auto_bias_mitigation(self, detected_bias, system_context):
        """自動バイアス軽減"""
        
        mitigation_plan = self.design_mitigation_strategy(detected_bias, system_context)
        
        implemented_mitigations = []
        
        for strategy in mitigation_plan['strategies']:
            if strategy['auto_implementable']:
                # 自動実装可能な対策の実行
                result = self.implement_mitigation(strategy)
                implemented_mitigations.append(result)
                
                # 効果の即座評価
                effectiveness = self.evaluate_immediate_impact(result)
                
                if effectiveness['improvement'] < 0.05:  # 5%未満の改善
                    # 効果不十分な場合は次の戦略を試行
                    continue
                else:
                    # 効果的な場合は一時的に適用継続
                    self.schedule_effectiveness_review(result, hours=24)
        
        return {
            'implemented_mitigations': implemented_mitigations,
            'pending_manual_interventions': [
                s for s in mitigation_plan['strategies'] 
                if not s['auto_implementable']
            ],
            'next_evaluation_scheduled': datetime.now() + timedelta(hours=4)
        }
    
    def implement_threshold_adjustment(self, bias_info):
        """閾値調整による公平性改善"""
        
        affected_groups = bias_info['affected_groups']
        target_metric = bias_info['metric']
        
        if target_metric == 'equal_opportunity':
            # 機会均等の改善：グループ別閾値調整
            new_thresholds = self.calculate_equalized_odds_thresholds(
                affected_groups
            )
            
            adjustment_result = self.apply_threshold_adjustments(new_thresholds)
            
        elif target_metric == 'statistical_parity':
            # 統計的パリティの改善：確率調整
            adjustment_result = self.apply_probability_adjustments(
                affected_groups, target_parity=0.8
            )
        
        return adjustment_result
    
    def implement_representation_balancing(self, underrepresented_groups):
        """代表性バランシング"""
        
        balancing_actions = []
        
        for group in underrepresented_groups:
            # データ拡張による代表性向上
            augmentation_result = self.augment_group_representation(group)
            balancing_actions.append(augmentation_result)
            
            # 重み調整による影響力向上
            weight_adjustment = self.adjust_group_weights(group)
            balancing_actions.append(weight_adjustment)
        
        return {
            'balancing_actions': balancing_actions,
            'expected_improvement': self.estimate_improvement_impact(balancing_actions),
            'implementation_timeline': '2-4 weeks'
        }
    
    def continuous_fairness_optimization(self):
        """継続的公平性最適化"""
        
        # 現在の公平性状態評価
        current_state = self.assess_current_fairness_state()
        
        # 最適化目標の設定
        optimization_targets = self.set_optimization_targets(current_state)
        
        # 段階的改善計画
        improvement_plan = self.create_improvement_roadmap(
            current_state, optimization_targets
        )
        
        # 自動実行可能な改善の即座実装
        immediate_improvements = self.implement_immediate_improvements(
            improvement_plan['immediate_actions']
        )
        
        return {
            'current_fairness_state': current_state,
            'optimization_targets': optimization_targets,
            'improvement_roadmap': improvement_plan,
            'immediate_improvements': immediate_improvements,
            'long_term_strategy': improvement_plan['long_term_actions']
        }

# 実装例：公平性自動最適化システム
adaptive_fairness = AdaptiveFairnessSystem()

# バイアス検出時の自動対応
bias_detection_result = {
    'metric': 'equal_opportunity',
    'affected_groups': ['age_50_plus', 'female'],
    'severity': 'medium',
    'current_score': 0.72
}

# 自動軽減措置の実行
mitigation_result = adaptive_fairness.auto_bias_mitigation(
    bias_detection_result, 
    system_context='hiring_screening'
)

# 継続的最適化の実行
optimization_result = adaptive_fairness.continuous_fairness_optimization()

print("=== 自動公平性改善結果 ===")
print(f"実装された対策: {len(mitigation_result['implemented_mitigations'])}件")
print(f"最適化目標: {optimization_result['optimization_targets']}")