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+---
+id: wiki-2026-0508-policy-surveillance
+title: Policy Surveillance
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Legal Mapping, Policy Tracking, Regulatory Monitoring]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.85
+verification_status: applied
+tags: [public-policy, governance, compliance, legal-tech]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: legal-mapping
+---
+
+# Policy Surveillance
+
+## 매 한 줄
+> **"매 you can't evaluate what you can't measure — start by mapping the law."**. 매 Burris (Temple) 가 정립한 **Policy Surveillance** = 매 systematic, scientific tracking of laws/policies as data 의 개념. 매 2026 AI governance (EU AI Act enforcement, Korea AI Basic Act, US state AI laws) 시대에 매 polyjurisdictional compliance 의 핵심 도구.
+
+## 매 핵심
+
+### 매 정의 vs adjacent
+- **Policy Surveillance**: 매 ongoing, systematic, scientific 매 monitoring of policies as 매 quantifiable data.
+- **vs Legal Research**: 매 case-driven, episodic.
+- **vs Compliance Audit**: 매 organization-internal, point-in-time.
+- **vs Regulatory Tracking**: 매 news-driven, qualitative.
+
+### 매 5단계 method (Burris)
+1. 매 frame the question — what behavior does the law target?
+2. 매 define jurisdictional + temporal scope.
+3. 매 collect primary sources (statutes, regs).
+4. 매 code into structured variables (binary, ordinal, categorical).
+5. 매 publish + maintain — 매 LawAtlas-style open data.
+
+### 매 응용
+1. AI Act compliance: 매 27 EU 회원국 + 미국 50주의 AI law variation 추적.
+2. Public health: 매 LawAtlas COVID closure tracking, opioid policies.
+3. Privacy: 매 GDPR vs CPRA vs PIPL 의 cross-walk.
+
+## 💻 패턴
+
+### Pattern 1: Coding scheme YAML
+```yaml
+# 매 ai_law_codes.yaml
+variables:
+  - id: requires_impact_assessment
+    type: binary
+    question: "매 Does law require AI impact assessment?"
+  - id: penalty_max
+    type: numeric
+    unit: USD
+  - id: covered_systems
+    type: categorical
+    values: [foundation_models, biometric, hiring, healthcare, all_high_risk]
+jurisdictions: [EU, US-CA, US-CO, KR, UK, CN]
+effective_dates: required
+```
+
+### Pattern 2: Cross-walk matrix
+```python
+import pandas as pd
+
+def crosswalk(jurisdictions, variables, codes_df):
+    matrix = codes_df.pivot(index="jurisdiction",
+                            columns="variable",
+                            values="value")
+    matrix.to_csv("crosswalk.csv")
+    return matrix
+```
+
+### Pattern 3: Diff over time
+```python
+def policy_diff(snapshot_old, snapshot_new):
+    changes = []
+    for jur in snapshot_new.index:
+        for var in snapshot_new.columns:
+            if snapshot_old.at[jur, var] != snapshot_new.at[jur, var]:
+                changes.append({
+                    "jurisdiction": jur, "variable": var,
+                    "from": snapshot_old.at[jur, var],
+                    "to": snapshot_new.at[jur, var],
+                })
+    return changes
+```
+
+### Pattern 4: LLM-assisted coding (with human verification)
+```python
+import anthropic
+client = anthropic.Anthropic()
+
+def code_statute(statute_text, scheme):
+    resp = client.messages.create(
+        model="claude-opus-4-7",
+        max_tokens=2048,
+        system=f"Code the statute against this scheme: {scheme}. Return JSON.",
+        messages=[{"role": "user", "content": statute_text}],
+    )
+    # 매 ALWAYS human-verify legal coding
+    return {"draft": resp.content[0].text, "needs_review": True}
+```
+
+### Pattern 5: Effective-date timeline
+```python
+def timeline_view(codes_df):
+    return codes_df.sort_values("effective_date")[
+        ["jurisdiction", "variable", "value", "effective_date"]
+    ]
+```
+
+### Pattern 6: Citation chain (provenance)
+```python
+def store_with_provenance(code, value, statute_section, source_url, retrieved_at):
+    return {
+        "code": code, "value": value,
+        "citation": {"section": statute_section, "url": source_url, "retrieved": retrieved_at},
+    }
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 single-org compliance | Standard compliance audit |
+| 매 multi-jurisdiction policy comparison | Policy Surveillance |
+| 매 academic causal inference (does law X cause outcome Y?) | Policy Surveillance + econometrics |
+| 매 real-time regulatory news | News tracker (NOT surveillance) |
+| 매 AI Act multi-state US tracking | Policy Surveillance + LLM-draft + lawyer review |
+
+**기본값**: 매 LawAtlas-style codebook + git versioning + LLM-draft + human verification.
+
+## 🔗 Graph
+- 응용: [[AI 거버넌스 정책(AI Usage Policy)|AI Governance]] · [[GDPR Compliance]]
+- Adjacent: [[EU AI Act]]
+
+## 🤖 LLM 활용
+**언제**: 매 first-pass coding of large statute corpus, 매 cross-walk drafting, 매 diff summarization.
+**언제 X**: 매 final legal coding without human lawyer — 매 hallucination risk too high for compliance use.
+
+## ❌ 안티패턴
+- **No version control**: 매 statutes 가 amend 되는데 snapshot 없으면 매 useless for trend analysis.
+- **Coding without scheme**: 매 ad-hoc tags — 매 inter-coder reliability ~0.
+- **LLM-only coding**: 매 hallucinated citations — 매 catastrophic for legal use.
+- **Single jurisdiction silo**: 매 policy surveillance 의 가치 = comparison.
+
+## 🧪 검증 / 중복
+- Verified (Burris et al., Temple Center for Public Health Law Research; LawAtlas.org).
+- 신뢰도 A (academic + practitioner standard).
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Burris method, AI Act 응용, LLM augmentation |