2.3.4. South Korea’s Approach- Balancing Innovation with Responsibility

South Korea’s Approach: Balancing Innovation with Responsibility

South Korea presents a middle-path governance model that blends regulatory foresight with operational flexibility. Unlike the more rigid, top-down structure of the EU or the market-led approach of the U.S., Korea’s framework emphasizes public-private collaboration, gradual enforcement, and adaptive regulation.

At the center of this approach is the AI Basic Act (2024), Korea’s foundational law for AI governance. It establishes national roles and responsibilities, defines high-impact AI categories, and mandates organizational obligations for risk assessment, oversight, and transparency. This is inline with participatory mechanisms like the AIGA Framework, which integrates governance assessment into government, corporate, and developer environments.

Implementation Tools

• Korea’s AI Basic Act formalizes governance responsibilities across the AI lifecycle. It includes enforceable obligations such as:

  • Article 31 (Transparency): Requires all high-impact and generative AI services to clearly inform users when AI is involved, including when outputs are artificially generated or manipulated (e.g., deepfakes).

  • Article 32 (Safety Requirements): Mandates providers of large-scale AI systems to identify, assess, and mitigate risks throughout the lifecycle, and to submit results to government authorities.

  • Article 33 (Impact Confirmation): Requires companies to conduct preliminary classification assessments to determine whether their systems qualify as high-impact AI.

  • Article 34 (Reliability): Obligates businesses to implement user protection plans, human oversight mechanisms, and safety documentation for high-impact AI systems.

  • Article 35 (Fundamental Rights Assessment): Encourages impact assessments of how high-impact AI systems may affect individual rights, especially for public-sector use.

  • Article 36 (Local Representation): Foreign companies above a regulatory threshold must appoint a domestic representative responsible for local compliance.

• Organizational Requirements:

  • Conduct risk assessments across all lifecycle stages, including deployment and monitoring, as required for large-scale systems (Art. 32).

  • Explain how AI decisions are derived, including documenting training data and system logic for high-impact systems (Art. 34).

  • Assign human oversight and establish fallback systems to intervene in high-risk decisions (Art. 34).

  • Evaluate the AI system’s impact on fundamental rights, particularly when used in public services (Art. 35).

  • Designate a local compliance representative if operating from outside South Korea and meeting the size threshold (Art. 36).

Governance Impact

Korea’s approach fosters ecosystem-wide accountability without stifling agility. It:

• Promotes transparency and ethical alignment

• Supports early-stage innovation with structured oversight

• Aligns with global standards (e.g., ISO/IEC 42001, EU AI Act) to ensure interoperability

The use of voluntary tools alongside binding obligations allows Korea to build trust without overburdening developers, making it an attractive model for mid-sized economies and digitally advanced nations.

Criticisms or Trade-offs

While South Korea’s AI Basic Act introduces a comprehensive legal framework for responsible AI development, several challenges remain regarding its clarity, enforceability, and long-term sustainability ¹.

• The Act contains terminology that is still broad or interpretive, particularly in defining what constitutes “high-impact AI” or “risk-based obligations.” Without detailed regulatory guidance or sector-specific criteria, companies may struggle to accurately assess whether they are subject to specific requirements, leading to inconsistencies in compliance and enforcement.

• The enforcement infrastructure is still maturing. Although the law outlines obligations for lifecycle risk management and transparency, the mechanisms for oversight—such as audit systems, monitoring bodies, or official compliance pathways—are still under development. This creates uncertainty for both regulators and industry, particularly small and medium-sized enterprises (SMEs) that often lack dedicated legal or governance teams.

• There are concerns about compliance equity. SMEs, in particular, face structural disadvantages in meeting the Act’s technical and documentation requirements, such as maintaining detailed risk assessments, preparing rights impact reports, or assigning governance personnel. Without scaled support mechanisms, the regulatory burden may unintentionally concentrate compliance capacity in larger firms, reinforcing market imbalances and limiting broader participation in Korea’s AI ecosystem.

Global Relevance

Korea’s model has drawn attention across Asia and Europe as a template for agile and collaborative AI governance. Its ability to mix law, ethics, and adaptive regulation positions it as a “translatable model” for countries that seek to balance:

• Innovation

• Rights protection

• Market competitiveness

• Global alignment

Its participation in international governance discussions and adoption of multi-stakeholder tools place South Korea as a policy innovator on the global stage.

As South Korea continues refining its governance strategy, it may serve as a reference for countries seeking a balance between innovation leadership and rights-based accountability.

Governance in Contrast: What We Learn from the Four Models

Different countries adopt different AI governance strategies depending on their national goals, regulatory traditions, and institutional structures. This comparative table 8 summarizes how each model approaches governance and where key risks still remain.

Table 8: Governance Models in Contrast

Country	Primary Goal	Governance Style	Key Risk
EU	Rights-first	Legal + Risk-based	Overregulation, compliance costs
U.S.	Innovation-driven	Decentralized + Sectoral	Inconsistency, regulatory gaps
China	State control	Centralized + Preemptive	Surveillance, lack of oversight
Korea	Balanced trust	Adaptive + Collaborative	Enforcement clarity, SME burden

There is no universal approach to AI governance. Each model reflects different values and trade-offs between innovation and control, rights and efficiency, speed and caution.

As we move forward, the question is no longer which model to follow, but how governance itself must evolve to meet the speed and scale of AI advancement.

TRAI Challenges: Compare and Contrast Governance Models

🧩 Task Overview:
Using the table “Governance Models in Contrast” (Table 2.1):

• Identify two models (e.g.,EU and South Korea)
• Compare their primary goals, governance styles, and accountability mechanisms

💬 Discussion Questions:

• What strengths and limitations are most significant for managing high-risk AI?
• Which model do you believe is more scalable for global use, and why?

Bibliography

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1. https://www.shinkim.com/eng/media/newsletter/2667 ↩