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The central conclusion is that the legal status of using copyrighted works to train AI models remains unresolved globally, creating a complex tension between innovation and intellectual property rights. As generative AI becomes highly commercialized, creatives argue that data scraping for training violates their copyrights. Consequently, the path to resolution is uncertain, potentially shaped by divergent approaches including strict regulation, judicial litigation, or voluntary commercial agreements, rather than a single unified international standard. Regionally, strategies vary significantly. The European Union is implementing transparency mandates under the AI Act, requiring developers to disclose training data to empower copyright holders to opt out. Japan, initially permissive toward AI developers, appears to be shifting toward stricter interpretations that may deem unauthorized mining a violation. In the United States, the issue is being addressed simultaneously through executive inquiries, pending congressional legislation focused on transparency, and high-stakes lawsuits like the New York Times case, which could set critical precedents for fair use. This article is vital to open data because it highlights the friction between the open data ethos of unrestricted information access for analysis and the emerging legal requirements for transparency and consent. If regulations mandate disclosure or restrict text and data mining, the foundational assumptions of open data practices for AI development may change. It underscores the necessity for open data communities to engage with these governance debates, as future open data ecosystems will likely need to incorporate mechanisms for opt-outs and provenance tracking to comply with evolving copyright laws.

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Published on 2024-01-12

Open Data

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