South Korea to Adopt AI Fair Use, Ease Self-Driving Rules by November
South Korea moves to ease AI rules, with fair-use for copyrighted data and looser AV testing as early as November. Government teams should prep policies, pilots, and procurement.
South Korea Fast-Tracks AI Deregulation: What Government Teams Need to Do Now
South Korea plans to lower regulatory barriers for AI development by introducing fair-use guidelines for copyrighted content and revising related laws as early as November. The government will also ease rules for developing and test-operating AI-powered self-driving vehicles. These plans were announced following the first presidential meeting on deregulation since President Lee Jae Myung took office three months ago.
The goal is clear: speed up AI deployment across society while giving developers clearer access to copyrighted and public data. For public agencies, this is a signal to prepare policies, pilots, and procurement guardrails ahead of the legal changes.
What's Changing
- Copyright and data use: Adoption of fair-use guidelines and legal revisions to reduce friction when training and deploying AI with copyrighted and public datasets.
- Autonomous vehicles: Looser rules for development and test operations of AI-based self-driving systems, likely via expanded sandboxing and simplified approvals.
- Timeline: Legal updates as early as November, with broader measures targeted within the year.
Why This Matters for Government Teams
- Procurement: Clearer IP rules affect contracts for AI tools, datasets, and model training services.
- Data strategy: Expanded use of public data requires updated sharing policies, documentation, and quality standards.
- Risk and compliance: Fair-use is not a free pass. Agencies must define acceptable use, retention, and auditability from day one.
- Mobility pilots: Eased AV testing rules open space for city- and province-level trials with measurable public safety criteria.
Immediate Actions for Ministries and Public Agencies
- Inventory data and content use: Map where copyrighted materials and public data are used in AI training, fine-tuning, and inference. Flag high-risk areas.
- Draft "fair-use in AI" internal guidance: Specify permitted sources, attribution practices, dispute response, and model/data lineage requirements.
- Strengthen vendor requirements: Require provenance, license terms, dataset documentation, and model risk reports in RFPs and contracts.
- Prepare for AV pilots: Define local test zones, incident reporting, human oversight protocols, and emergency handoff procedures.
- Stand up governance: Create or empower an AI steering group with legal, data, safety, and security leads to approve pilots and resolve trade-offs.
Guardrails to Maintain Public Trust
- Conduct privacy impact assessments and maintain audit logs for training data, model versions, and inference decisions.
- Test models for bias, safety, and reliability; document test plans and outcomes.
- Use human-in-the-loop for high-stakes decisions; publish clear escalation paths.
- For AV tests, enforce transparent incident disclosure and third-party safety reviews.
What to Watch Next
- Publication of fair-use guidelines and draft legal text by relevant ministries.
- Details on how public datasets can be accessed, documented, and shared for AI use.
- Specific criteria for AV testing approvals, data reporting, and emergency protocols.
Helpful References
- OECD AI Principles for high-level guidance on trustworthy AI.
- WIPO: AI and Intellectual Property for international perspectives on copyright and AI.
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