[论文解读] Training Is Everything: Artificial Intelligence, Copyright, and Fair Training

Andrew W. Torrance, Bill Tomlinson|arXiv (Cornell University)|May 4, 2023

Law, AI, and Intellectual Property被引用 12

一句话总结

本文分析在AI训练中使用受版权保护的作品是否构成合理使用，权衡双方论点，并将辩论置于更广泛的社会成本与收益之中。

ABSTRACT

To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many of which fall within the core subject matter of copyright law. To some, the use of copyrighted works as training sets for AI is merely a transitory and non-consumptive use that does not materially interfere with owners' content or copyrights protecting it. Companies that use such content to train their AI engine often believe such usage should be considered "fair use" under United States law (sometimes known as "fair dealing" in other countries). By contrast, many copyright owners, as well as their supporters, consider the incorporation of copyrighted works into training sets for AI to constitute misappropriation of owners' intellectual property, and, thus, decidedly not fair use under the law. This debate is vital to the future trajectory of AI and its applications. In this article, we analyze the arguments in favor of, and against, viewing the use of copyrighted works in training sets for AI as fair use. We call this form of fair use "fair training". We identify both strong and spurious arguments on both sides of this debate. In addition, we attempt to take a broader perspective, weighing the societal costs (e.g., replacement of certain forms of human employment) and benefits (e.g., the possibility of novel AI-based approaches to global issues such as environmental disruption) of allowing AI to make easy use of copyrighted works as training sets to facilitate the development, improvement, adoption, and diffusion of AI. Finally, we suggest that the debate over AI and copyrighted works may be a tempest in a teapot when placed in the wider context of massive societal challenges such as poverty, equality, climate change, and loss of biodiversity, to which AI may be part of the solution.

研究动机与目标

• 分析将用于AI训练的受版权保护作品是否应视为合理使用（公平训练）的论点。
• 识别在公平训练辩论中双方的有力与牵强论点。
• 在评估在受版权保护内容上训练AI的社会成本（如工作岗位置换）与利益（如AI驱动的解决方案）时进行权衡。
• 将AI训练辩论置于贫困、气候变化和生物多样性挑战的更广泛背景之中。

提出的方法

• 在AI训练背景下对合理使用进行概念性与规范性分析。
• 识别并分类支持与反对公平训练的论点。
• 评估宽松AI训练做法潜在的社会成本与收益。
• 提供将AI训练与全球社会挑战联系起来的广泛、情境性讨论。

实验结果

研究问题

• RQ1 将用于AI训练的受版权保护作品是否应视为合理使用（公平训练）的核心论点是什么？
• RQ2 政策制定者应如何权衡使用受版权保护内容来训练AI的社会成本与收益？
• RQ3 在全球挑战如贫困、气候变化和生物多样性丧失的更广泛背景下，关于AI训练的辩论能否被理解？

主要发现

• 作者在公平训练辩论的任一侧都确定了强有力和牵强的论点。
• 他们主张在评估AI训练实践时应权衡社会成本与收益。
• 他们认为在考虑更大的全球挑战时，AI 与版权辩论可能不那么关键。
• 本文讨论了AI 驱动的方法在全球问题上的更广泛社会影响与潜在益处。
• 分析强调在贫困、平等、气候变化和生物多样性方面的情境化辩论。