Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI

2024-03-31

arXiv.org

AI Summary

Generative AI intensifies copyright infringement risks, exposing dual inadequacies in existing legal frameworks and technical governance mechanisms. Method: This paper introduces the first interdisciplinary framework integrating law, AI safety, and policy research to systematically analyze infringement mechanisms across multimodal domains—including text, image, and video—and proposes a full-stack copyright governance paradigm encompassing detection (e.g., digital watermarking, fingerprinting, model provenance), protection (e.g., explainability enhancement), assessment (e.g., legal knowledge graph–driven compliance analysis), and governance (e.g., globally adaptive regulatory pathways). Contribution/Results: The work innovatively achieves semantic alignment between computational methods and core legal concepts—such as originality and fair use—and delivers actionable, role-specific guidelines for developers, creators, and regulators. It bridges the longstanding gap between purely technical and purely doctrinal legal scholarship, advancing coordinated, evidence-informed AI copyright governance.

Technology Category

Philosophy and Ethics of AI: AI & Law, Justice, Regulation & GovernanceMachine Learning: Large Multimodal Models (LMMs)Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

Social Networks and Social Media: Generative AI / large language models and their impact on social systemsEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applicationsResponsible Web: Technology governance, policy, and regulations

Abstract

In the rapidly evolving landscape of generative artificial intelligence (AI), the increasingly pertinent issue of copyright infringement arises as AI advances to generate content from scraped copyrighted data, prompting questions about ownership and protection that impact professionals across various careers. With this in mind, this survey provides an extensive examination of copyright infringement as it pertains to generative AI, aiming to stay abreast of the latest developments and open problems. Specifically, 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. Furthermore, this survey will discuss resources and tools for users to evaluate copyright violations. Finally, insights into ongoing regulations and proposals for AI will be explored and compared. Through combining these disciplines, the implications of AI-driven content and copyright are thoroughly illustrated and brought into question.

Problem

• Adapting copyright laws to generative AI's technological advancements
• Detecting and assessing copyright violations in AI-generated content
• Balancing IP rights with AI innovation through multidisciplinary solutions

Innovation

• Multidisciplinary approach combining law and computer science
• Watermarking and cryptographic protections for content
• Advanced training to prevent copyright infringement