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AI Royalties -- an IP Framework to Compensate Artists & IP Holders for AI-Generated Content

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arxiv 2406.11857 v1 pith:HJN5H5UE submitted 2024-04-05 cs.CY cs.AI

Pablo Ducru , Jonathan Raiman , Ronaldo Lemos , Clay Garner , George He , Hanna Balcha , Gabriel Souto , Sergio Branco
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Celina Bottino
This is my paper · ORCID
classification cs.CYcs.AI
keywords ai-generatedholdersartistscompensationcontentcopyrightframeworkimages
verification ladder T0 review T1 audit T2 compute T3 formal
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This article investigates how AI-generated content can disrupt central revenue streams of the creative industries, in particular the collection of dividends from intellectual property (IP) rights. It reviews the IP and copyright questions related to the input and output of generative AI systems. A systematic method is proposed to assess whether AI-generated outputs, especially images, infringe previous copyrights, using a similarity metric (CLIP) between images against historical copyright rulings. An examination (economic and technical feasibility) of previously proposed compensation frameworks reveals their financial implications for creatives and IP holders. Lastly, we propose a novel IP framework for compensation of artists and IP holders based on their published "licensed AIs" as a new medium and asset from which to collect AI royalties.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A LoRA is Worth a Thousand Pictures

    cs.CV 2024-12 conditional novelty 6.0 of 10

    LoRA weight vectors, projected with PCA and a per-PC calibration, cluster and retrieve artistic styles more accurately than CLIP, DINO, and style-specialized image features.

  2. Lost in Edits? A $\lambda$-Compass for AIGC Provenance

    cs.CV 2025-02 conditional novelty 4.0 of 10

    Applying an adaptive Box-Cox transformation to reconstruction losses improves binary detection of edited AI images over the Latent Tracer baseline.

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