2nd/10_Wiki/Topics/Other/Policy-Surveillance.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-policy-surveillance	Policy Surveillance	10_Wiki/Topics	verified	self	Legal Mapping Policy Tracking Regulatory Monitoring	none	A	0.85	applied	public-policy governance compliance legal-tech		2026-05-10	pending	language framework python 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

# 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

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

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)

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

def timeline_view(codes_df):
    return codes_df.sort_values("effective_date")[
        ["jurisdiction", "variable", "value", "effective_date"]
    ]

Pattern 6: Citation chain (provenance)

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) · 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