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.

Training generative AI models requires extensive amounts of data. A common practice is to collect such data through web scraping. Yet, much of what has been and is collected is copyright protected. Its use may be copyright infringement. In the USA, AI developers rely on "fair use" and in Europe, the prevailing view is that the exception for "Text and Data Mining" (TDM) applies. In a recent interdisciplinary tandem-study, we have argued in detail that this is actually not the case because generative AI training fundamentally differs from TDM. In this article, we share our main findings and the implications for both public and corporate research on generative models. We further discuss how the phenomenon of training data memorization leads to copyright issues independently from the "fair use" and TDM exceptions. Finally, we outline how the ISMIR could contribute to the ongoing discussion about fair practices with respect to generative AI that satisfy all stakeholders.

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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The U.K. Government is seemingly backtracking on plans that would have allowed text and data mining "for any purpose," plans designed to position the U.K. as a "global AI superpower." The news emerges following months of blowback from creative industries concerned about what impact the rules might have on protected works. Text and data mining, for the uninitiated, is an essential component of just about every AI application, allowing researchers and developers to leverage disparate datasets to train their algorithms. But gaining access to a sufficient amount of data is not a straight-forward endeavor, given that data is often owned by organizations or individuals that might not want third-parties to have access to their data. Or, they may only make said data available under a commercial license, making it prohibitively expensive to harness.

The U.K. is planning to tweak an existing law to allow text and data mining "for any purpose," in a move that's designed to boost artificial intelligence (AI) development across the country. The announcement constitutes part of a broader strategy to "level up" AI and transform the U.K. into what it calls a "global AI superpower" -- and part of this will involve reassessing existing intellectual property (IP) laws. Following a two-month consultation period where stakeholders from across the industrial spectrum were asked for input, including rightsholders, academics, lawyers, trade organisations and businesses, the U.K.'s Intellectual Property Office (IPO) today published its response and confirmed what will (and won't) be changing moving forward. Text and data mining (TDM) is pivotal to the development of new AI applications, allowing researchers and businesses to copy and harness disparate datasets to train their algorithms. However, gaining access to enough relevant data has inherent challenges -- the data is often owned by third-parties that may only want to make data available under a commercial license, if they make it available at all.

The U.K. is planning to tweak an existing law to allow text and data mining "for any purpose," in a move that's designed to boost artificial intelligence (AI) development across the country. The announcement constitutes part of a broader strategy to "level up" AI and transform the U.K. into what it calls a "global AI superpower" -- and part of this will involve reassessing existing intellectual property (IP) laws. Following a two-month consultation period where stakeholders from across the industrial spectrum were asked for input, including rightsholders, academics, lawyers, trade organisations and businesses, the U.K.'s Intellectual Property Office (IPO) today published its response and confirmed what will (and won't) be changing moving forward. Text and data mining (TDM) is pivotal to the development of new AI applications, allowing researchers and businesses to copy and harness disparate datasets to train their algorithms. However, gaining access to enough relevant data has inherent challenges -- the data is often owned by third-parties that may only want to make data available under a commercial license, if they make it available at all.