The advent of Generative Artificial Intelligence (GenAI) models, including
GitHub Copilot, OpenAI GPT, and Stable Diffusion, has revolutionized content
creation, enabling non-professionals to produce high-quality content across
various domains. This transformative technology has led to a surge of synthetic
content and sparked legal disputes over copyright infringement. To address
these challenges, this paper introduces a novel approach that leverages the
learning capacity of GenAI models for copyright legal analysis, demonstrated
with GPT2 and Stable Diffusion models. Copyright law distinguishes between
original expressions and generic ones (Sc\`enes \`a faire), protecting the
former and permitting reproduction of the latter. However, this distinction has
historically been challenging to make consistently, leading to over-protection
of copyrighted works. GenAI offers an unprecedented opportunity to enhance this
legal analysis by revealing shared patterns in preexisting works. We propose a
data-driven approach to identify the genericity of works created by GenAI,
employing "data-driven bias" to assess the genericity of expressive
compositions. This approach aids in copyright scope determination by utilizing
the capabilities of GenAI to identify and prioritize expressive elements and
rank them according to their frequency in the model's dataset. The potential
implications of measuring expressive genericity for copyright law are profound.
Such scoring could assist courts in determining copyright scope during
litigation, inform the registration practices of Copyright Offices, allowing
registration of only highly original synthetic works, and help copyright owners
signal the value of their works and facilitate fairer licensing deals. More
generally, this approach offers valuable insights to policymakers grappling
with adapting copyright law to the challenges posed by the era of GenAI.

这篇研究论文介绍了一种新方法，利用 GenAI 模型的学习能力进行版权法律分析，通过 GPT2 和 Stable Diffusion 模型进行了演示。该方法利用数据驱动的偏见来评估 GenAI 创建的作品的一般性，帮助确定版权范围，并提供了有关评估版权法律和制定政策的宝贵见解。