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.

翻译：生成式人工智能模型（包括GitHub Copilot、OpenAI GPT和Stable Diffusion）的出现彻底改变了内容创作方式，使非专业人士也能在多个领域生成高质量内容。这项变革性技术催生了大量合成内容，并引发了关于版权侵权的法律争议。为应对这些挑战，本文提出了一种创新方法，利用GenAI模型的学习能力进行版权法律分析，并以GPT2和Stable Diffusion模型为例进行论证。版权法区分独创性表达与通用性表达（场景原则），保护前者而允许复制后者。然而，这一区分在历史上始终难以一致实现，导致对版权作品的过度保护。GenAI通过揭示既有作品中的共享模式，为强化这种法律分析提供了前所未有的机遇。我们提出了一种数据驱动方法，通过运用"数据驱动偏差"评估表达性构成的通用性，从而识别GenAI所生成作品的通用程度。该方法利用GenAI识别并优先处理表达性要素、根据其在模型数据集中的出现频率进行排序的能力，辅助确定版权范围。对表达通用性进行量化的潜在法律影响十分深远：这种评分可协助法院在诉讼中确定版权范围，指导版权局的登记实践（仅允许高度原创的合成作品进行登记），并帮助版权所有者标示作品价值以促成更公平的许可协议。更广泛而言，本方法为正在努力调整版权法以适应GenAI时代挑战的政策制定者提供了宝贵见解。