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

TL;DR

The paper tackles how AI-generated content threatens traditional IP revenue streams and proposes a multi-part framework to address compensation and governance. It develops a $CLIP$-based metric to assess whether AI outputs infringe prior copyrights, tests it on a dataset of rulings, and articulates threshold-based interpretations for fair use versus infringement. It then analyzes three compensation schemes (Windfall Clause, pay-to-train, pay-to-train-and-inspire) across artist types, highlighting economic implications and practical challenges. Finally, it proposes a novel 'licensed AIs' framework with contractual AI royalties that aligns IP owners and AI firms through dedicated models, supported by enforcement mechanisms and a forward-looking agenda to safeguard human creativity and market viability in AI-driven content creation.

Abstract

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.

Figures (2)

• Figure 1: Copyright cases and CLIP-based metric
• Figure 2: CLIP Metric and Copyright Infringement