AI 监管政策与立法趋势：从民意调查到全球立法框架

#!/usr/bin/env python3
"""
AI 系统合规自动评估工具
支持欧盟 AI 法案、美国州级法规和中国管理办法的初步评估

注意：此工具仅用于教育目的，不构成法律建议。
"""

from dataclasses import dataclass
from enum import Enum
from typing import Optional

class Region(Enum):
    EU = "eu"
    US = "us"
    CN = "cn"

class RiskLevel(Enum):
    UNACCEPTABLE = "不可接受风险"
    HIGH = "高风险"
    LIMITED = "有限风险"
    MINIMAL = "最小风险"

@dataclass
class AIComplianceCheck:
    """单个合规检查项"""
    name: str
    description: str
    region: Region
    applicable: bool = False
    status: str = "待评估"  # 待评估 / 通过 / 不通过 / 不适用

@dataclass
class AIClassifier:
    """AI 系统分类器"""
    system_name: str
    application_domains: list[str]  # 应用领域
    uses_biometrics: bool           # 是否使用生物识别
    affects_fundamental_rights: bool  # 是否影响基本权利
    is_real_time_surveillance: bool   # 是否实时监控
    affects_vulnerable_groups: bool   # 是否影响弱势群体
    generates_content: bool           # 是否生成内容
    is_chatbot: bool                  # 是否为聊天机器人
    uses_recommendation: bool         # 是否使用推荐算法

    def classify_eu_risk(self) -> RiskLevel:
        """欧盟 AI 法案风险分类"""
        # 不可接受风险
        if self.is_real_time_surveillance:
            return RiskLevel.UNACCEPTABLE
        if self.affects_vulnerable_groups and self.uses_biometrics:
            return RiskLevel.UNACCEPTABLE

        # 高风险
        high_risk_domains = [
            "教育", "就业", "医疗", "执法",
            "关键基础设施", "社会福利", "司法"
        ]
        if any(d in self.application_domains for d in high_risk_domains):
            return RiskLevel.HIGH
        if self.affects_fundamental_rights:
            return RiskLevel.HIGH

        # 有限风险
        if self.is_chatbot or self.generates_content:
            return RiskLevel.LIMITED

        return RiskLevel.MINIMAL

    def generate_compliance_checklist(self, region: Region) -> list[AIComplianceCheck]:
        """生成合规检查清单"""
        checks = []
        
        if region == Region.EU:
            risk = self.classify_eu_risk()
            
            checks.extend([
                AIComplianceCheck(
                    name="风险等级评估",
                    description=f"系统风险等级：{risk.value}",
                    region=Region.EU,
                    applicable=True,
                    status="已评估"
                ),
            ])
            
            if risk == RiskLevel.HIGH:
                checks.extend([
                    AIComplianceCheck(
                        name="风险管理框架",
                        description="建立并维护风险管理框架",
                        region=Region.EU,
                        applicable=True
                    ),
                    AIComplianceCheck(
                        name="技术文档",
                        description="维护完整的技术文档",
                        region=Region.EU,
                        applicable=True
                    ),
                    AIComplianceCheck(
                        name="人类监督机制",
                        description="实施人类监督机制",
                        region=Region.EU,
                        applicable=True
                    ),
                    AIComplianceCheck(
                        name="数据质量保障",
                        description="确保训练数据质量和代表性",
                        region=Region.EU,
                        applicable=True
                    ),
                    AIComplianceCheck(
                        name="合格性评估",
                        description="通过合格性评估并获得 CE 标志",
                        region=Region.EU,
                        applicable=True
                    ),
                ])
            
            elif risk == RiskLevel.LIMITED:
                checks.append(
                    AIComplianceCheck(
                        name="透明度告知",
                        description="告知用户正在与 AI 系统交互",
                        region=Region.EU,
                        applicable=True
                    )
                )
        
        elif region == Region.US:
            checks.extend([
                AIComplianceCheck(
                    name="NIST AI RMF 评估",
                    description="参照 NIST AI 风险管理框架进行自评",
                    region=Region.US,
                    applicable=True
                ),
                AIComplianceCheck(
                    name="偏见审计",
                    description="对 AI 系统进行偏见和公平性审计",
                    region=Region.US,
                    applicable=True
                ),
                AIComplianceCheck(
                    name="消费者告知",
                    description="向消费者披露 AI 的使用情况",
                    region=Region.US,
                    applicable=True
                ),
            ])
        
        elif region == Region.CN:
            checks.extend([
                AIComplianceCheck(
                    name="内容安全审核",
                    description="建立生成内容安全审核机制",
                    region=Region.CN,
                    applicable=self.generates_content
                ),
                AIComplianceCheck(
                    name="AI 生成内容标识",
                    description="对 AI 生成内容进行显著标识",
                    region=Region.CN,
                    applicable=self.generates_content
                ),
                AIComplianceCheck(
                    name="训练数据合规",
                    description="确保训练数据来源合法",
                    region=Region.CN,
                    applicable=True
                ),
                AIComplianceCheck(
                    name="算法备案",
                    description="如适用，完成算法备案",
                    region=Region.CN,
                    applicable=True
                ),
            ])
        
        return checks

def run_compliance_assessment(classifier: AIClassifier, regions: list[Region]):
    """运行完整的合规评估"""
    print(f"=== AI 系统合规评估：{classifier.system_name} ===\n")
    
    # 欧盟风险分类
    eu_risk = classifier.classify_eu_risk()
    print(f"欧盟风险等级：{eu_risk.value}")
    print(f"应用领域：{', '.join(classifier.application_domains)}")
    print()
    
    for region in regions:
        print(f"--- {region.value.upper()} 合规检查清单 ---")
        checks = classifier.generate_compliance_checklist(region)
        for i, check in enumerate(checks, 1):
            status = check.status if check.status != "待评估" else "需要评估"
            applicability = "✅ 适用" if check.applicable else "⏭️ 不适用"
            print(f"  {i}. [{applicability}] {check.name}: {check.description}")
            if check.status == "待评估" and check.applicable:
                print(f"     ⚠️  状态：{status}")
        print()

# 使用示例
if __name__ == "__main__":
    # 示例：一个用于招聘筛选的 AI 系统
    recruitment_ai = AIClassifier(
        system_name="智能招聘筛选系统",
        application_domains=["就业", "人力资源"],
        uses_biometrics=False,
        affects_fundamental_rights=True,
        is_real_time_surveillance=False,
        affects_vulnerable_groups=False,
        generates_content=False,
        is_chatbot=False,
        uses_recommendation=True,
    )
    
    run_compliance_assessment(recruitment_ai, [Region.EU, Region.US, Region.CN])

#!/usr/bin/env python3
"""
AI 生成内容水印与标识工具
满足欧盟透明度义务和中国深度合成管理规定
"""

import hashlib
import json
import base64
from datetime import datetime

class ContentWatermarker:
    """AI 生成内容水印和标识工具"""
    
    def __init__(self, provider_name: str, model_name: str):
        self.provider = provider_name
        self.model = model_name
    
    def generate_metadata(self) -> dict:
        """生成 AI 生成内容的元数据标识"""
        return {
            "ai_generated": True,
            "provider": self.provider,
            "model": self.model,
            "timestamp": datetime.now().isoformat(),
            "watermark_id": self._generate_watermark_id(),
        }
    
    def _generate_watermark_id(self) -> str:
        """生成唯一水印标识符"""
        seed = f"{self.provider}:{self.model}:{datetime.now().isoformat()}"
        return hashlib.sha256(seed.encode()).hexdigest()[:16]
    
    def embed_metadata_in_image(self, image_data: bytes, metadata: dict) -> bytes:
        """在图像元数据中嵌入 AI 生成标识
        实际实现应使用 EXIF 或 PNG tEXt chunk
        这里仅提供框架示例
        """
        metadata_json = json.dumps(metadata, ensure_ascii=False)
        # 在实际实现中，将 metadata_json 编码并嵌入图像
        # 推荐使用 C2PA 标准或类似的可验证凭证方案
        return image_data
    
    def generate_disclosure_notice(self, language: str = "zh") -> str:
        """生成 AI 生成内容的披露声明"""
        disclosures = {
            "zh": f"本内容由人工智能系统（{self.provider} {self.model}）生成，仅供参考。",
            "en": f"This content was generated by an AI system ({self.provider} {self.model}) and is for reference only.",
        }
        return disclosures.get(language, disclosures["en"])

# 使用示例
if __name__ == "__main__":
    watermarker = ContentWatermarker("MyAI Corp", "GenModel-v3.2")
    
    # 生成元数据
    metadata = watermarker.generate_metadata()
    print("AI 生成内容标识：")
    print(json.dumps(metadata, indent=2, ensure_ascii=False))
    
    # 生成披露声明
    print("\n中文披露声明：")
    print(watermarker.generate_disclosure_notice("zh"))
    print("\n英文披露声明：")
    print(watermarker.generate_disclosure_notice("en"))