BEYOND THE PROMPT: RECONCILING TRAINING DATA INGESTION AND MUSIC COPYRIGHT IN THE GENERATIVE AI ERA
DOI:
https://doi.org/10.58898/famedia.v1.4Keywords:
Generative AI, Music Copyright, Algorithmic Black Box, Training Data Attribution, Fair Use, Text and Data Mining (TDM), Intellectual PropertyAbstract
This paper examines the unprecedented regulatory restructuring in the global music industry driven by the rapid commercialization of generative artificial intelligence (GenAI) and its fundamental challenge to traditional copyright frameworks. Through a comparative analysis of emerging litigation and divergent legal approaches across the US, EU, and UK, the paper investigates the "algorithmic black box" of AI training data ingestion, the complexities surrounding AI authorship, and the highly debated "fair use" defense. Conclusions indicate that the unauthorized ingestion of copyrighted musical compositions and sound recordings threatens to dilute the market and depreciate foundational cultural capital, exposing systemic inadequacies in current legislation. To prevent irreversible damage to the creative sector, the paper concludes that AI models must not be legally equated to human learners. It recommends implementing scalable technological and regulatory solutions, specifically Training Data Attribution (TDA) coupled with a user-centric blanket licensing frameworks or an opt-in closed-universe database. These mechanisms would ensure fair remuneration for rightsholders, fostering a sustainable ecosystem where algorithmic innovation and human artistry can coexist.
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