The rapid advancement of generative AI is poised to disrupt the creative
industry. Amidst the immense excitement for this new technology, its future
development and applications in the creative industry hinge crucially upon two
copyright issues: 1) the compensation to creators whose content has been used
to train generative AI models (the fair use standard); and 2) the eligibility
of AI-generated content for copyright protection (AI-copyrightability). While
both issues have ignited heated debates among academics and practitioners, most
analysis has focused on their challenges posed to existing copyright doctrines.
In this paper, we aim to better understand the economic implications of these
two regulatory issues and their interactions. By constructing a dynamic model
with endogenous content creation and AI model development, we unravel the
impacts of the fair use standard and AI-copyrightability on AI development, AI
company profit, creators income, and consumer welfare, and how these impacts
are influenced by various economic and operational factors. For example, while
generous fair use (use data for AI training without compensating the creator)
benefits all parties when abundant training data exists, it can hurt creators
and consumers when such data is scarce. Similarly, stronger AI-copyrightability
(AI content enjoys more copyright protection) could hinder AI development and
reduce social welfare. Our analysis also highlights the complex interplay
between these two copyright issues. For instance, when existing training data
is scarce, generous fair use may be preferred only when AI-copyrightability is
weak. Our findings underscore the need for policymakers to embrace a dynamic,
context-specific approach in making regulatory decisions and provide insights
for business leaders navigating the complexities of the global regulatory
environment.

通过建立一个带有内生内容创作和 AI 模型发展的动态模型，我们研究了公平使用标准和 AI 版权可保护性对人工智能发展、AI 公司利润、创作者收入和消费者福利的影响，并揭示了这些影响如何受到各种经济和运营因素的影响，为政策制定者提供了动态、具体背景的决策方法和为全球监管环境中的商业领导者提供了见解。

生成人工智能和版权：动态视角

Generative AI and Copyright: A Dynamic Perspective

This is the first survey of the active area of AI research that focuses on
privacy issues in Large Language Models (LLMs). Specifically, we focus on work
that red-teams models to highlight privacy risks, attempts to build privacy
into the training or inference process, enables efficient data deletion from
trained models to comply with existing privacy regulations, and tries to
mitigate copyright issues. Our focus is on summarizing technical research that
develops algorithms, proves theorems, and runs empirical evaluations. While
there is an extensive body of legal and policy work addressing these challenges
from a different angle, that is not the focus of our survey. Nevertheless,
these works, along with recent legal developments do inform how these technical
problems are formalized, and so we discuss them briefly in Section 1. While we
have made our best effort to include all the relevant work, due to the fast
moving nature of this research we may have missed some recent work. If we have
missed some of your work please contact us, as we will attempt to keep this
survey relatively up to date. We are maintaining a repository with the list of
papers covered in this survey and any relevant code that was publicly available
at this https URL

在大型语言模型中，该研究对隐私问题进行了第一次调查，重点关注对模型进行红队测试以凸显隐私风险、试图将隐私性纳入训练或推理过程、实现从训练模型中高效删除数据以遵守现有隐私法规，并试图缓解版权问题。

大型语言模型中的隐私问题：综述

Privacy Issues in Large Language Models: A Survey

Generative diffusion models, including Stable Diffusion and Midjourney, can
generate visually appealing, diverse, and high-resolution images for various
applications. These models are trained on billions of internet-sourced images,
raising significant concerns about the potential unauthorized use of
copyright-protected images. In this paper, we examine whether it is possible to
determine if a specific image was used in the training set, a problem known in
the cybersecurity community and referred to as a membership inference attack.
Our focus is on Stable Diffusion, and we address the challenge of designing a
fair evaluation framework to answer this membership question. We propose a
methodology to establish a fair evaluation setup and apply it to Stable
Diffusion, enabling potential extensions to other generative models. Utilizing
this evaluation setup, we execute membership attacks (both known and newly
introduced). Our research reveals that previously proposed evaluation setups do
not provide a full understanding of the effectiveness of membership inference
attacks. We conclude that the membership inference attack remains a significant
challenge for large diffusion models (often deployed as black-box systems),
indicating that related privacy and copyright issues will persist in the
foreseeable future.

本文研究生成扩散模型的会员推断攻击，通过提出一种公正的评估框架，发现之前提出的评估机制无法充分了解该问题的有效性，因此揭示了该攻击仍然是大型扩散模型的难点，难以保护相关的隐私和版权问题。