Pirated Data for AI Training: Copyright Risks?

• Japan's statutory exception framework (Article 30-4) requires that AI training use not 'unjustly harm legitimate interests' of copyright holders—a narrowly interpreted standard that shifts burden to demonstrating fair use rather than obtaining prior consent[1] • Arbitration panels evaluating AI training disputes will consider: balance of contract clauses versus statutory rights, whether training harms legitimate interests, and interpretation of copyright law in light of generative AI capabilities[1] • Technical access restrictions (e.g., robots.txt) are frequently bypassed in large-scale training operations, creating enforcement challenges[1] • AI service providers argue training uses qualify as exempt under statutory exceptions or contract terms, and outputs lack infringement due to transformation or lack of direct replication[1] • Source-level attribution remains technically infeasible for large-scale models that do not store discrete records of individual training works, complicating transparency mandates[3]

Japan's regulatory approach creates ongoing tension between innovation incentives and creator protections. The non-binding 'Principles and Code' guidance (expected shortly after January 2026 consultation) will likely shape industry norms without legal mandate, potentially influencing arbitration outcomes in disputes like CODA v. OpenAI. Mandatory disclosure requirements and prior-consent frameworks could increase compliance costs for AI developers but may accelerate licensing frameworks and revenue-sharing models. The AI Safety Institute's evaluation capacity will provide technical assessment of generative AI systems, though without enforcement authority. Japan's permissive legal environment for AI development may attract continued investment, but growing artist opposition (89% view AI as serious threat) could pressure government toward stronger IP protections in future amendments.