The integration of generative artificial intelligence (GenAI) and large language models (LLMs) into scientific research and higher education presents a paradigm shift, offering revolutionizing opportunities while simultaneously raising profound ethical, legal, and regulatory questions. This study examines the complex intersection of AI and science, with a specific focus on the challenges posed to copyright law and the principles of open science. The author argues that current regulatory frameworks in key jurisdictions like the United States, China, the European Union, and the United Kingdom, while aiming to foster innovation, contain significant gaps, particularly concerning the use of copyrighted works and open science outputs for AI training. Widely adopted licensing mechanisms, such as Creative Commons, fail to adequately address the nuances of AI training, and the pervasive lack of attribution within AI systems fundamentally challenges established notions of originality. This paper issues a call to action, contending that AI training should not be shielded under fair use exceptions. Instead, the author advocates for upholding authors' rights to refuse the use of their works for AI training and proposes that universities assume a leading role in shaping responsible AI governance. The conclusion is that a harmonized international legislative effort is urgently needed to ensure transparency, protect intellectual property, and prevent the emergence of an oligopolistic market structure that could prioritize commercial profit over scientific integrity and equitable knowledge production.
翻译:生成式人工智能(GenAI)与大型语言模型(LLMs)融入科学研究及高等教育,标志着一次范式转变,既带来了革命性机遇,也引发了深刻的伦理、法律与监管问题。本研究审视人工智能与科学交汇的复杂性,特别聚焦于其对版权法与开放科学原则构成的挑战。作者指出,当前美国、中国、欧盟及英国等关键司法辖区的监管框架虽旨在促进创新,但在涉及使用受版权保护作品及开放科学成果进行AI训练方面存在显著漏洞。广泛采用的许可机制(如知识共享协议)未能充分应对AI训练的细微差别,而AI系统中普遍存在的归属缺失从根本上挑战了既有的原创性概念。本文呼吁采取行动,主张AI训练不应受合理使用例外条款的保护。相反,作者倡导维护作者拒绝将其作品用于AI训练的权利,并提议大学在构建负责任的AI治理中发挥主导作用。结论是,亟需协调一致的国际立法努力,以确保透明度、保护知识产权,并防止形成可能将商业利益置于科学诚信与公平知识生产之上的寡头垄断市场结构。