🚨 [AI RESEARCH] As ⚖️ AI copyright lawsuits ⚖️ pile up, the paper "The Globalization of Copyright Exceptions for AI Training" by Matthew Sag & Peter K. Yu is a MUST-READ to understand the ongong debate. Quotes:
"Although the copyright implications of machine learning seemed indistinguishable from TDM prior to generative AI, there are differences between machine learning and generative AI worth noting. First, generative AI does not simply analyze training data to derive useful information; it can produce digital artifacts in the same form as its training data. Even if the outputs do not approach substantial similarity—the threshold for copyright infringement—they may nonetheless compete directly with the works used to train AI models or with the copyright holders of those works. In the United States, this prospect of indirect substitution could complicate the fair use analysis with respect to the fourth factor, “the effect of the use upon the potential market for or value of the copyrighted work. (...)" (p. 10)
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"The generative AI plaintiffs’ most promising paths are fact specific. For instance, the consolidated cases in Tremblay v. OpenAI present a plausible argument under the fourth fair use factor that commercial AI developers undermine the basic incentive structure of copyright by training on sites of known infringement and thus bypassing the market for access without a compelling justification. The Tremblay plaintiffs alleged that OpenAI and other developers had obtained access to over 100,000 books and other works through shadow libraries, such as Library Genesis, Z-Library, Sci-Hub, and Bibliotik. This relatively novel argument is bolstered by its resonance with copyright laws in other jurisdictions. In the European Union, for example, “lawful access” to the relevant copyrighted works is an essential condition under the TDM exceptions in the DSM Directive.(...)" (p. 37)
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"Because AI technology will continue to evolve in the near future, sparking further legal, regulatory, technological, and business developments, it remains to be seen whether and how this equilibrium will be maintained. Regardless of the outcome, scrutinizing international copyright law developments in the area of AI training will deepen our understanding of how to better harness the copyright system to advance AI, including generative AI technology. Because some copyright legislation will provide greater affordances for machine learning and AI training than others, policymakers and commentators should pay greater attention to these relative strengths and weaknesses. Like the design of AI models and training processes, the design of the copyright system can play a very important role in the age of generative AI." (p. 47)
➡️ Read the paper below.
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