알고리즘 영향평가

# 알고리즘 영향평가(AIA) 프레임워크 예제
from dataclasses import dataclass, field
from datetime import datetime
from typing import List, Dict, Optional
from enum import Enum

class ImpactCategory(Enum):
    FAIRNESS = "fairness"
    TRANSPARENCY = "transparency"
    PRIVACY = "privacy"
    SAFETY = "safety"
    ACCOUNTABILITY = "accountability"
    HUMAN_RIGHTS = "fundamental_rights"

class RiskLevel(Enum):
    MINIMAL = 1
    LIMITED = 2
    HIGH = 3
    UNACCEPTABLE = 4

@dataclass
class ImpactDimension:
    """개별 영향 차원 평가"""
    category: ImpactCategory
    description: str
    severity: int  # 1-10
    likelihood: int  # 1-10
    affected_groups: List[str]
    mitigation_measures: List[str]

    @property
    def risk_score(self) -> float:
        return self.severity * self.likelihood / 10

@dataclass
class AlgorithmicImpactAssessment:
    """EU AI Act 제27조 기반 알고리즘 영향평가"""
    assessment_id: str
    ai_system_name: str
    ai_system_version: str
    assessor: str
    assessment_date: datetime
    use_case_description: str
    affected_population: str
    impact_dimensions: List[ImpactDimension] = field(default_factory=list)

    def calculate_overall_risk(self) -> RiskLevel:
        """종합 위험 수준 산정"""
        if not self.impact_dimensions:
            return RiskLevel.MINIMAL

        max_score = max(dim.risk_score for dim in self.impact_dimensions)
        if max_score >= 8:
            return RiskLevel.UNACCEPTABLE
        elif max_score >= 5:
            return RiskLevel.HIGH
        elif max_score >= 3:
            return RiskLevel.LIMITED
        return RiskLevel.MINIMAL

    def generate_fria_report(self) -> Dict:
        """기본권 영향평가(FRIA) 보고서 생성 - 제27조"""
        return {
            "assessment_reference": self.assessment_id,
            "ai_system": f"{self.ai_system_name} v{self.ai_system_version}",
            "assessor": self.assessor,
            "date": self.assessment_date.isoformat(),
            "use_case": self.use_case_description,
            "overall_risk_level": self.calculate_overall_risk().name,
            "impact_summary": [
                {
                    "category": dim.category.value,
                    "risk_score": dim.risk_score,
                    "affected_groups": dim.affected_groups,
                    "mitigations": len(dim.mitigation_measures)
                }
                for dim in self.impact_dimensions
            ],
            "legal_basis": "EU AI Act Article 9 (Risk Management), Article 27 (FRIA)"
        }

# 사용 예시
aia = AlgorithmicImpactAssessment(
    assessment_id="AIA-2025-0089",
    ai_system_name="HR-Screening-AI",
    ai_system_version="2.1",
    assessor="AI Ethics Team",
    assessment_date=datetime.now(),
    use_case_description="채용 서류 심사 자동화",
    affected_population="연간 5만명 지원자"
)

# 영향 차원 추가
aia.impact_dimensions.append(ImpactDimension(
    category=ImpactCategory.FAIRNESS,
    description="성별, 연령, 출신 학교에 따른 차별 가능성",
    severity=7,
    likelihood=4,
    affected_groups=["여성 지원자", "40대 이상", "비수도권 대학 출신"],
    mitigation_measures=["편향 테스트 월간 실시", "보호 속성 블라인드 처리", "인간 검토 필수화"]
))

report = aia.generate_fria_report()
print(f"종합 위험 수준: {report['overall_risk_level']}")