Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes

• URL: http://arxiv.org/abs/2403.17691v2
• Date: Tue, 7 May 2024 09:15:01 GMT
• Title: Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes
• Authors: Uri Hacohen, Adi Haviv, Shahar Sarfaty, Bruria Friedman, Niva Elkin-Koren, Roi Livni, Amit H Bermano,
• Abstract summary: This paper introduces a novel approach that leverages the learning capacity of GenAI models for copyright legal analysis. We propose a data-driven approach to identify the genericity of works created by GenAI. The potential implications of measuring expressive genericity for copyright law are profound.
• Score: 20.237329910319293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Evaluating Copyright Takedown Methods for Language Models [100.38129820325497]
  Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material. This paper introduces the first evaluation of the feasibility and side effects of copyright takedowns for LMs. We examine several strategies, including adding system prompts, decoding-time filtering interventions, and unlearning approaches.
  arXiv  Detail & Related papers  (2024-06-26T18:09:46Z)
• Evaluating and Mitigating IP Infringement in Visual Generative AI [54.24196167576133]
  State-of-the-art visual generative models can generate content that bears a striking resemblance to characters protected by intellectual property rights. This happens when the input prompt contains the character's name or even just descriptive details about their characteristics. We develop a revised generation paradigm that can identify potentially infringing generated content and prevent IP infringement.
  arXiv  Detail & Related papers  (2024-06-07T06:14:18Z)
• Tackling GenAI Copyright Issues: Originality Estimation and Genericization [25.703494724823756]
  We propose a genericization method that modifies the outputs of a generative model to make them more generic and less likely to infringe copyright. Experiments demonstrate that our genericization method successfully modifies the output of a text-to-image generative model so that it produces more generic, copyright-compliant images.
  arXiv  Detail & Related papers  (2024-06-05T14:58:32Z)
• ©Plug-in Authorization for Human Content Copyright Protection in Text-to-Image Model [71.47762442337948]
  State-of-the-art models create high-quality content without crediting original creators. We propose the copyright Plug-in Authorization framework, introducing three operations: addition, extraction, and combination. Extraction allows creators to reclaim copyright from infringing models, and combination enables users to merge different copyright plug-ins.
  arXiv  Detail & Related papers  (2024-04-18T07:48:00Z)
• A Legal Risk Taxonomy for Generative Artificial Intelligence [1.3651236252124068]
  This paper presents a taxonomy of legal risks associated with generative AI (GenAI) It provides a common understanding of potential legal challenges for developing and deploying GenAI models.
  arXiv  Detail & Related papers  (2024-04-15T06:05:39Z)
• Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI [2.669847575321326]
  The survey aims to stay abreast of the latest developments and open problems. It will first outline methods of detecting copyright infringement in mediums such as text, image, and video. Next, it will delve an exploration of existing techniques aimed at safeguarding copyrighted works from generative models.
  arXiv  Detail & Related papers  (2024-03-31T22:10:01Z)
• Copyright Protection in Generative AI: A Technical Perspective [58.84343394349887]
  Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep Generative Models (DGMs) have sparked significant copyright concerns. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective.
  arXiv  Detail & Related papers  (2024-02-04T04:00:33Z)
• A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models [52.49582606341111]
  Copyright law confers creators the exclusive rights to reproduce, distribute, and monetize their creative works. Recent progress in text-to-image generation has introduced formidable challenges to copyright enforcement. We introduce a novel pipeline that harmonizes CLIP, ChatGPT, and diffusion models to curate a dataset.
  arXiv  Detail & Related papers  (2024-01-04T11:14:01Z)
• Can Copyright be Reduced to Privacy? [23.639303165101385]
  We argue that while algorithmic stability may be perceived as a practical tool to detect copying, such copying does not necessarily constitute copyright infringement. If adopted as a standard for detecting an establishing copyright infringement, algorithmic stability may undermine the intended objectives of copyright law.
  arXiv  Detail & Related papers  (2023-05-24T07:22:41Z)
• Foundation Models and Fair Use [96.04664748698103]
  In the U.S. and other countries, copyrighted content may be used to build foundation models without incurring liability due to the fair use doctrine. In this work, we survey the potential risks of developing and deploying foundation models based on copyrighted content. We discuss technical mitigations that can help foundation models stay in line with fair use.
  arXiv  Detail & Related papers  (2023-03-28T03:58:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.