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Record STMT-WBCC133Advisory

Intrinsic Risks of Artificial Intelligence and Legislative & Policy Tasks - Focusing on Data, Technology, and Users -

인공지능의 내재적 위험과 입법·정책 과제 - 데이터·기술·이용자를 중심으로 -

Report Institutional policyEastern Asia · KRin force

This research report by the National Assembly Research Service (NARS) examines the intrinsic risks of artificial intelligence, focusing on data, technology, and users. It analyzes domestic and international legal frameworks in the US and the EU, including South Korea's proposed AI basic law and AI ethics guidelines. Finally, it outlines legislative and policy recommendations to address data, technical, and user risks.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

National Assembly Research ServiceGovernment
Lead issuer
Legal forceAdvisory
Under whose authorityInstitutional policy
Judged fromfull text

Who it governs

LegislatorsNational governmentResearchers and academics

Legislators, policymakers, and researchers interested in artificial intelligence policy and legislation.

Sectors
Cross-sectorResearch and science · information technology

What the document contains

What's covered

The document addresses the intrinsic risks associated with artificial intelligence across data, technology, and users. It surveys current legislative approaches in South Korea, the US, and the EU, and proposes detailed legislative and policy tasks to mitigate these risks.

What's missing

The report is a research and policy analysis publication rather than a binding legal statute or regulation; it does not contain direct enforcement penalties or binding administrative mandates.

Key takeaways
  • The report categorizes intrinsic AI risks into data risks, technical risks, and user risks.
  • It reviews overseas legal systems in the United States and the European Union, alongside South Korea's AI basic law bill and AI ethics guidelines.
  • Data risk recommendations include enhancing learning data transparency, organizing personal information and copyright laws, and supporting data accumulation.
  • Technical risk recommendations focus on expanding AI technical documentation and user manuals alongside risk assessments.
  • User risk measures emphasize improving user literacy and regulating malicious use and cyber attacks.
Outline
· Ⅰ. 서론
  1. 인공지능의 등장과 발전
  2. 인공지능의 내재적 위험과 대응 필요성
· Ⅱ. 인공지능의 내재적 위험
  1. 인공지능의 내재적 위험 유형
  2. 위험의 내용 (가. 데이터 위험, 나. 기술적 위험, 다. 이용자 위험)
· Ⅲ. 인공지능의 내재적 위험에 대응하는 국내외 법제도
  1. 외국 법제도 (가. 미국, 나. 유럽연합)
  2. 국내 법제도 (가. 인공지능기본법안, 나. 인공지능 윤리기준)
  3. 소결
· Ⅳ. 입법·정책 과제
  1. 데이터 위험 대응 (가. 학습데이터에 대한 설명 강화, 나. 개인정보 관련 법률 정비, 다. 저작권 관련 법률 정비, 라. 데이터 축적 지원)
  2. 기술적 위험 대응 (가. AI 기술문서 대상 확대와 사용설명서 작성, 나. AI 위험성 평가 확대)
  3. 이용자 위험 대응 (가. 이용자 리터러시 개선, 나. 악의적 이용과 사이버 공격의 규제)
· Ⅴ. 결론
Issued31 December 2024
Length294 words
FormatNot recorded
Scopenational

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Accountability

0/5

Clear responsibility for AI outcomes

N/A

screening score · 2026-10-24

Fairness

0/5

Non-discrimination and equitable treatment

N/A

screening score · 2026-10-24

Human Dignity

0/5

Respect for the inherent worth of persons

N/A

screening score · 2026-10-24

Human Oversight

0/5

Humans remain in control of consequential decisions

N/A

screening score · 2026-10-24

Privacy

3/5

Protection of personal data and private life

나. 개인정보 관련 법률 정비

Ⅳ. 입법·정책 과제

screening score · 2026-10-24

Transparency

3/5

Openness about how AI systems work and are used

가. 학습데이터에 대한 설명 강화

Ⅳ. 입법·정책 과제

screening score · 2026-10-24

Enforcement

0/5

Consequences and compliance mechanisms with real force

N/A

screening score · 2026-10-24

Topics

artificial_intelligence ai_governance data_protection legislative_policy risk_assessment