docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거

Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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+---
+id: wiki-2026-0508-ethics-ai
+title: Ethics & AI
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [AI ethics, responsible AI, AI safety, alignment, fairness, bias, EU AI Act]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.97
+verification_status: applied
+tags: [ethics, ai-ethics, alignment, safety, bias, fairness, responsibility, eu-ai-act]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Universal
+  applicable_to: [AI Development, Policy, Governance]
+---
+
+# Ethics & AI
+
+## 매 한 줄
+> **"매 AI 의 design / deploy / govern 의 normative consideration"**. 매 fairness, accountability, transparency, safety. 매 modern: EU AI Act, NIST AI RMF, Anthropic Constitutional AI. 매 alignment + capability + governance 의 triad.
+
+## 매 핵심
+
+### 매 pillar
+- **Fairness**: 매 bias 의 mitigate.
+- **Accountability**: 매 who 의 responsible.
+- **Transparency / Explainability**.
+- **Privacy**.
+- **Safety**: 매 harm prevention.
+- **Robustness**.
+- **Human autonomy**.
+
+### 매 alignment
+- **RLHF**: 매 human preference.
+- **Constitutional AI** (Anthropic): 매 principle-based.
+- **DPO / KTO**: 매 RLHF alternative.
+- **Scalable oversight**: 매 debate, IDA.
+- **Honest / harmless / helpful** (HHH).
+
+### 매 framework
+- **EU AI Act** (2024): 매 risk-tier.
+- **NIST AI RMF**: 매 govern, map, measure, manage.
+- **OECD AI Principles**.
+- **ISO/IEC 42001**: 매 AIMS.
+- **GDPR** (privacy).
+- **Algorithmic Accountability Act** (US, proposed).
+
+### 매 risk-tier (EU AI Act)
+- **Unacceptable**: 매 social scoring, mass biometric.
+- **High-risk**: 매 hiring, credit, education, AV.
+- **Limited risk**: 매 chatbot disclose.
+- **Minimal**: 매 spam filter.
+
+### 매 응용 issue
+1. **Hiring**: 매 disparate impact.
+2. **Credit**: 매 redlining.
+3. **Healthcare**: 매 race-based prediction.
+4. **Justice**: 매 COMPAS bias.
+5. **Generative**: 매 deepfake, copyright.
+6. **Surveillance**: 매 mass.
+7. **Autonomous**: 매 trolley.
+
+### 매 modern (2024-2026)
+- **Anthropic RSP** (Responsible Scaling Policy).
+- **OpenAI Preparedness**.
+- **Frontier model evaluations** (METR, Apollo).
+- **AI safety institute** (UK, US).
+- **AI Bill of Rights** (US OSTP).
+
+## 💻 패턴
+
+### Fairness audit (demographic parity)
+```python
+import numpy as np
+
+def demographic_parity_diff(predictions, protected_attr):
+    groups = np.unique(protected_attr)
+    rates = [predictions[protected_attr == g].mean() for g in groups]
+    return max(rates) - min(rates)
+
+# 매 < 0.05 = 80% rule heuristic compliant
+```
+
+### Equalized odds
+```python
+def equalized_odds(predictions, labels, protected):
+    """매 TPR + FPR 의 group 에 의 equal."""
+    groups = np.unique(protected)
+    metrics = {}
+    for g in groups:
+        mask = protected == g
+        tpr = ((predictions == 1) & (labels == 1) & mask).sum() / max(1, ((labels == 1) & mask).sum())
+        fpr = ((predictions == 1) & (labels == 0) & mask).sum() / max(1, ((labels == 0) & mask).sum())
+        metrics[g] = (tpr, fpr)
+    return metrics
+```
+
+### Bias mitigation (reweighing)
+```python
+def reweighing(X, y, protected):
+    """매 Kamiran-Calders 2012."""
+    weights = np.ones(len(y))
+    for g in np.unique(protected):
+        for c in [0, 1]:
+            mask = (protected == g) & (y == c)
+            p_expected = (protected == g).mean() * (y == c).mean()
+            p_observed = mask.mean()
+            weights[mask] = p_expected / max(p_observed, 1e-9)
+    return weights
+```
+
+### Constitutional AI (principle-based)
+```python
+def cai_critique(response, principles):
+    prompt = f"""Critique this response against these principles.
+Principles:
+{format_principles(principles)}
+
+Response: {response}
+
+Output JSON with:
+- violated: list of principle IDs
+- explanation
+- revised_response"""
+    return json.loads(llm.generate(prompt))
+```
+
+### Differential privacy (DP-SGD)
+```python
+import opacus
+from opacus import PrivacyEngine
+
+privacy_engine = PrivacyEngine()
+model, optim, loader = privacy_engine.make_private(
+    module=model,
+    optimizer=optim,
+    data_loader=loader,
+    noise_multiplier=1.1,
+    max_grad_norm=1.0,
+)
+```
+
+### Model card
+```yaml
+model_name: credit-scoring-v3
+intended_use: Adult US credit applications, $1k-$50k unsecured
+out_of_scope:
+  - Outside US
+  - Under 18
+  - Loans > $50k
+training_data:
+  source: 2018-2024 internal
+  size: 2.4M
+  protected_attribute_audit: completed
+  fairness_metrics:
+    demographic_parity_diff: 0.034
+    equalized_odds_diff: 0.041
+limitations:
+  - Decreased performance on thin-file applicants
+  - Quarterly retraining required
+```
+
+### Provenance (C2PA, watermark)
+```python
+from c2pa import Signer
+def attach_provenance(image_path, signer_cert):
+    Signer(signer_cert).sign(image_path, claims={
+        'generator': 'Anthropic Claude Opus 4.7',
+        'timestamp': now(),
+        'training_data_redacted': True,
+    })
+```
+
+### Red-teaming
+```python
+def adversarial_eval(model, attack_categories):
+    attacks = []
+    for cat in attack_categories:  # 매 jailbreak, bias, harmful, hallucination
+        prompts = generate_attacks(cat, n=100)
+        for p in prompts:
+            r = model.generate(p)
+            score = judge(r, cat)
+            attacks.append({'cat': cat, 'prompt': p, 'response': r, 'severity': score})
+    return attacks
+```
+
+### Risk tier classifier (EU AI Act)
+```python
+def eu_risk_tier(use_case):
+    if use_case in {'social_scoring', 'real_time_remote_biometric'}:
+        return 'unacceptable'
+    if use_case in {'hiring', 'credit', 'education', 'critical_infra', 'law_enforcement'}:
+        return 'high'
+    if use_case in {'chatbot', 'deepfake', 'emotion_recognition_workplace'}:
+        return 'limited'
+    return 'minimal'
+```
+
+### Consent (GDPR)
+```python
+def can_process(user, purpose):
+    if user.consent[purpose].is_valid():
+        return True
+    if has_legitimate_interest(purpose):
+        return True
+    return False
+
+def revoke_consent(user, purpose):
+    user.consent[purpose].revoke()
+    delete_data(user, purpose)
+```
+
+### Disclosure (chatbot)
+```typescript
+function chatGreeting() {
+  return "Hi! I'm an AI assistant. I can make mistakes — please verify important info.";
+}
+```
+
+### Incident reporting
+```python
+@dataclass
+class AIIncident:
+    timestamp: datetime
+    model: str
+    severity: Literal['low', 'medium', 'high', 'critical']
+    category: str  # 매 hallucination, bias, jailbreak, harm
+    description: str
+    affected_users: int
+    root_cause: str
+    mitigation: str
+    
+    def report(self):
+        if self.severity in ('high', 'critical'):
+            notify_safety_team(self)
+        log_to_registry(self)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| High-risk EU | Full conformity assessment |
+| Hiring / credit | Fairness audit + monitoring |
+| Generative | Watermark + content provenance |
+| LLM | Constitutional + RLHF + red-team |
+| Privacy-sensitive | DP / federated |
+| Chatbot | Disclosure + safety filter |
+
+**기본값**: 매 model card + 매 fairness audit + 매 red-team + 매 incident reporting + 매 EU AI Act risk-tier compliance.
+
+## 🔗 Graph
+- 부모: [[AI]]
+- 변형: [[AI Safety]] · [[AI_Safety_and_Alignment|AI-Alignment]] · [[Algorithmic Fairness]] · [[Ethics & AI|Ethics of Autonomous Systems]]
+- 응용: [[EU-AI-Act]] · [[NIST-AI-RMF]] · [[AI_Safety_and_Alignment|Constitutional-AI]]
+- Adjacent: [[Differential-Privacy]] · [[RLHF]]
+
+## 🤖 LLM 활용
+**언제**: 매 모든 AI deployment. 매 product launch. 매 governance.
+**언제 X**: 매 academic toy.
+
+## ❌ 안티패턴
+- **Ethics-as-PR**: 매 statement only.
+- **Single fairness metric**: 매 trade-off 의 ignore.
+- **No red-team**: 매 jailbreak 의 surprise.
+- **No incident process**: 매 learning X.
+- **Ignore EU AI Act high-risk**: 매 fines + bans.
+
+## 🧪 검증 / 중복
+- Verified (EU AI Act 2024, NIST AI RMF 1.0, Anthropic RSP, Constitutional AI paper).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-20 | Auto-reinforced |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — pillars + 매 fairness / DP / model card / red-team / risk-tier code |