There is an increasing concern that generative AI models may produce outputs
that are remarkably similar to the copyrighted input content on which they are
trained. This worry has escalated as the quality and complexity of generative
models have immensely improved, and the availability of large datasets
containing copyrighted material has increased. Researchers are actively
exploring strategies to mitigate the risk of producing infringing samples, and
a recent line of work suggests to employ techniques such as differential
privacy and other forms of algorithmic stability to safeguard copyrighted
content.
In this work, we examine the question whether algorithmic stability
techniques such as differential privacy are suitable to ensure the responsible
use of generative models without inadvertently violating copyright laws. We
argue that there are fundamental differences between privacy and copyright that
should not be overlooked. In particular we highlight that although algorithmic
stability may be perceived as a practical tool to detect copying, it does not
necessarily equate to copyright protection. Therefore, if it is adopted as
standard for copyright infringement, it may undermine copyright law intended
purposes.

该论文探讨如何使用算法稳定技术保护受版权保护的内容免受生成 AI 模型的影响，并指出只是采用这些技术并不能等同于保护版权内容。

版权是否可以归结于隐私？

Can Copyright be Reduced to Privacy?

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.

本文从伦理和法律的角度分析了将受版权保护的作品加入 AI 训练集中，辩论 AI 会否因此而受到限制，并评估了 AI 影响社会的成本和利益。