South Korea AI Copyright Guide: Guidebook for Creators

As of May 18, 2026, South Korea’s Ministry of Culture, Sports and Tourism released an English version of its “Guidebook on Fair Use Concerning Copyrighted Works in Generative AI.” The document, first issued in Korean back in February, sets out practical tests for when scraping copyrighted material for AI training counts as fair use. It gives creators and developers something many have been missing: concrete criteria instead of vague threats of litigation. The guide positions Korea as a serious player in the global conversation about data licensing and AI rights. For anyone training models or generating new work, this is the kind of clarity that actually moves the needle.

The core test hinges on four factors. First, the purpose and character of the use — commercial training gets stricter scrutiny than research. Second, the nature of the copyrighted work itself. Third, how much of the original material is used and whether it is transformed. Fourth, the effect on the market for the original. The guidebook is explicit that outputs must show substantial human creative input to avoid infringement claims. It also notes that merely adding prompts is rarely enough on its own. Creators who modify, select, or combine outputs with significant original work stand on firmer ground.

The US Copyright Office has leaned toward case-by-case analysis, with ongoing lawsuits still undecided. The EU’s AI Act focuses more on transparency obligations than clear fair-use carve-outs. Korea’s approach sits somewhere in between: more prescriptive than America, less bureaucratic than Brussels. Honestly, after reading the full text I found myself wondering whether this model will spread. Clearer fair-use guidelines like South Korea’s reduce legal gray areas, letting creators confidently explore advanced AI tools—including those generating realistic video—for every type of content. The direction of travel is toward workable rules rather than outright bans.

Start by reviewing the guidebook’s four-factor test against your current workflows. If you are training on public datasets, document the scale and purpose of that scraping. For output generation, build in deliberate editing stages rather than relying on raw generations. Track how your work differs from source material — this matters more than ever. The Korean guidance rewards creators who treat AI as a tool rather than a replacement. That mindset is becoming the baseline for anyone who wants to stay on the right side of emerging rules worldwide.