Under Japan's Copyright Act, works require "creative expression of thoughts or feelings" (Art. 2(1)(i)). AI-generated content may not qualify as a copyrightable work due to lack of human creative contribution (Agency for Cultural Affairs 2023 guidance). However, detailed human direction may create copyrightable output. AI training on copyrighted works is generally lawful for information analysis (Art. 30-4), unless it unduly harms the copyright holder's interests. Risk: AI-generated content may infringe existing copyrights through similarity (Arts. 21-28). Key issues: proving "dependence" on training data. Corporate guidelines should address usage scope, confidential data restrictions, similarity checking, and contractual copyright allocation.
International Regulatory Developments
EU AI Act (Enacted 2024)
The EU AI Act, enacted in 2024, imposes transparency obligations on providers of general-purpose AI systems. Under Article 53, providers must disclose a summary of copyrighted content used in training data. AI-generated content must also be labeled as AI-generated. These rules affect Japanese companies operating in European markets.
U.S. Copyright Office AI Guidance
The U.S. Copyright Office issued guidance in 2023–2024 reaffirming that human creative authorship is an indispensable requirement for copyright protection. Purely AI-generated content is not protectable, but human selection, arrangement, or modification of AI output may qualify. The 2024 Copyright Office report explicitly states: "AI cannot be an author under U.S. copyright law."
China: Beijing Internet Court Recognizes AI-Assisted Copyright (2023)
In 2023, the Beijing Internet Court issued a landmark ruling recognizing copyright in an AI-generated image, where the user had made creative decisions through prompt iteration, compositional choices, and repeated refinements. This decision — while not binding on Japanese courts — is widely cited as evidence that prompt-level creative involvement can support a copyright claim.
Dependency and Similarity: Decision Framework
Whether AI-generated content infringes an existing work hinges on two key requirements: "dependency" (依拠性) and "substantial similarity" (類似性).
| Requirement | Standard | Example |
|---|---|---|
| Dependency (依拠性) | Was the existing work included in training data? Did the AI "base" its output on it? | Instructing AI to mimic a specific author's style — likely dependent if that author's works were in training data |
| Substantial Similarity (類似性) | Can the essential characteristics of the original expression be directly perceived? Idea vs. expression: only expression is protected | Shared sentence structure, word choices, or distinctive phrasing overlapping with the original |
| Proving Dependency is Difficult | Training data contents are typically undisclosed; proving dependency is practically challenging for users | Users rarely have access to AI training data manifests |
| Pre-publication Verification | Companies and users are expected to conduct similarity checks before publishing AI output | Use plagiarism checkers, reverse image search, or similarity detection tools |
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Try for free →How to Establish Creative Contribution Through Prompting
To support a copyright claim over AI-generated content, document your creative involvement from the prompting stage.
1. Provide Detailed, Specific Instructions
Rather than generic prompts like "write an article about X," specify structure, tone, key arguments, and exclusions. The more specific and deliberate the instruction, the stronger the case for human creative judgment.
2. Document an Iterative Revision Process
Don't use first-draft output directly. Run multiple prompt iterations, compare alternatives, and log your choices. The Agency for Cultural Affairs specifically cited "repeated modifications" as supporting creative contribution — keeping a log of this process serves as evidence.
3. Add Human Selection, Arrangement, and Editing
Selecting among multiple AI outputs, reordering sections, and rewriting in your own voice all constitute human editorial work that strengthens the case for copyrightability.
4. Save Prompts and Outputs Together
Preserve prompts paired with their corresponding outputs to demonstrate what instructions produced what results. This record becomes critical evidence if copyright ownership is later disputed.