Content ARCs: Decentralized Content Rights in the Age of Generative AI

• URL: http://arxiv.org/abs/2503.14519v1
• Date: Fri, 14 Mar 2025 11:57:08 GMT
• Title: Content ARCs: Decentralized Content Rights in the Age of Generative AI
• Authors: Kar Balan, Andrew Gilbert, John Collomosse,
• Abstract summary: This paper proposes a framework called emphContent ARCs (Authenticity, Rights, Compensation)<n>By combining open standards for provenance and dynamic licensing with data attribution, Content ARCs create a mechanism for managing rights and compensating creators for using their work in AI training.<n>We characterize several nascent works in the AI data licensing space within Content ARCs and identify where challenges remain to fully implement the end-to-end framework.
• Score: 14.208062688463524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of Generative AI (GenAI) has sparked significant debate over balancing the interests of creative rightsholders and AI developers. As GenAI models are trained on vast datasets that often include copyrighted material, questions around fair compensation and proper attribution have become increasingly urgent. To address these challenges, this paper proposes a framework called \emph{Content ARCs} (Authenticity, Rights, Compensation). By combining open standards for provenance and dynamic licensing with data attribution, and decentralized technologies, Content ARCs create a mechanism for managing rights and compensating creators for using their work in AI training. We characterize several nascent works in the AI data licensing space within Content ARCs and identify where challenges remain to fully implement the end-to-end framework.

Related papers

• Who Owns the Output? Bridging Law and Technology in LLMs Attribution [0.0]
  Large language models (LLMs) and Large Multimodal Models (LMM) have transformed content creation. The chances offered by generative AI models are endless and are drastically reducing the time required to generate content. However, considering the complexity and the difficult traceability of the generated content, the use of these tools provides challenges in attributing AI-generated content.
  arXiv  Detail & Related papers  (2025-03-29T18:08:04Z)
• SoK: Watermarking for AI-Generated Content [112.9218881276487]
  Watermarking schemes embed hidden signals within AI-generated content to enable reliable detection. Watermarks can play a crucial role in enhancing AI safety and trustworthiness by combating misinformation and deception. This work aims to guide researchers in advancing watermarking methods and applications, and support policymakers in addressing the broader implications of GenAI.
  arXiv  Detail & Related papers  (2024-11-27T16:22:33Z)
• Do Responsible AI Artifacts Advance Stakeholder Goals? Four Key Barriers Perceived by Legal and Civil Stakeholders [59.17981603969404]
  The responsible AI (RAI) community has introduced numerous processes and artifacts to facilitate transparency and support the governance of AI systems. We conduct semi-structured interviews with 19 government, legal, and civil society stakeholders who inform policy and advocacy around responsible AI efforts. We organize these beliefs into four barriers that help explain how RAI artifacts may (inadvertently) reconfigure power relations across civil society, government, and industry.
  arXiv  Detail & Related papers  (2024-08-22T00:14:37Z)
• Trustworthy AIGC Copyright Management with Full Lifecycle Recording and Multi-party Supervision in Blockchain [10.015046490760199]
  The current legal system for copyright is built around human creators, yet in the realm of AIGC, the role of humans in content creation has diminished. It is necessary to meticulously record contributions of all entities involved in the generation of AIGC to achieve a fair distribution of copyright. This study thoroughly records the intermediate data generated throughout the full lifecycle of AIGC and deposits them into a decentralized blockchain system for secure multi-party supervision.
  arXiv  Detail & Related papers  (2024-06-21T08:22:39Z)
• Data Shapley in One Training Run [88.59484417202454]
  Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. Existing approaches require re-training models on different data subsets, which is computationally intensive. This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest.
  arXiv  Detail & Related papers  (2024-06-16T17:09:24Z)
• An Economic Solution to Copyright Challenges of Generative AI [35.37023083413299]
  Generative artificial intelligence systems are trained to generate new pieces of text, images, videos, and other media. There is growing concern that such systems may infringe on the copyright interests of training data contributors. We propose a framework that compensates copyright owners proportionally to their contributions to the creation of AI-generated content.
  arXiv  Detail & Related papers  (2024-04-22T08:10:38Z)
• Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes [20.237329910319293]
  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.
  arXiv  Detail & Related papers  (2024-03-26T13:32:32Z)
• Generative AI and Copyright: A Dynamic Perspective [0.0]
  generative AI is poised to disrupt the creative industry. The compensation to creators whose content has been used to train generative AI models (the fair use standard) and the eligibility of AI-generated content for copyright protection (AI-copyrightability) are key issues. This paper aims to better understand the economic implications of these two regulatory issues and their interactions.
  arXiv  Detail & Related papers  (2024-02-27T07:12:48Z)
• 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)
• DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection [57.51313366337142]
  There has been growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery and data poisoning. We introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection.
  arXiv  Detail & Related papers  (2023-06-02T05:11:27Z)
• Guiding AI-Generated Digital Content with Wireless Perception [69.51950037942518]
  We introduce an integration of wireless perception with AI-generated content (AIGC) to improve the quality of digital content production. The framework employs a novel multi-scale perception technology to read user's posture, which is difficult to describe accurately in words, and transmits it to the AIGC model as skeleton images. Since the production process imposes the user's posture as a constraint on the AIGC model, it makes the generated content more aligned with the user's requirements.
  arXiv  Detail & Related papers  (2023-03-26T04:39:03Z)
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT [63.58711128819828]
  ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC) The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
  arXiv  Detail & Related papers  (2023-03-07T20:36:13Z)
• Data-centric AI: Perspectives and Challenges [51.70828802140165]
  Data-centric AI (DCAI) advocates a fundamental shift from model advancements to ensuring data quality and reliability. We bring together three general missions: training data development, inference data development, and data maintenance.
  arXiv  Detail & Related papers  (2023-01-12T05:28:59Z)

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.