Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to
  Inform GenAI Copyright Disputes
Uri Hacohen Adi Haviv Shahar Sarfaty Bruria Friedman Niva Elkin-Koren Roi Livni Amit H Bermano
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.