Abstract
The proliferation of generative artificial intelligence models capable of producing highly sophisticated textual, visual, and auditory outputs has ignited a foundational crisis in international copyright jurisprudence. This article rigorously examines the dual legal controversies defining the generative AI era: the copyrightability of machine-generated outputs and the legality of the massive datasets required to train these underlying models. First, we critically assess the "human authorship" requirement deeply entrenched in the United States Copyright Office guidelines and international treaties such as the Berne Convention. By analyzing recent administrative denials of copyright registration for AI-generated visual art, the authors argue that strictly anthropogenic frameworks are increasingly incompatible with an economy where cognitive labor is heavily subsidized by machine collaboration. We propose a nuanced standard for "algorithmic co-authorship" that quantifies the degree of human editorial prompt-engineering necessary to establish legal protectability. Secondly, the paper delves into the contentious issue of data scraping. Tech conglomerates uniformly assert that ingesting copyrighted material to train neural networks constitutes a highly transformative "fair use," generating entirely new economic value without directly substituting the original works. The authors rigorously dissect this defense, arguing that it fundamentally ignores the latent economic displacement of original creators. We evaluate ongoing class-action litigation and compare the US approach with the European Union's recent AI Act and the Text and Data Mining (TDM) exceptions in the Digital Single Market Directive. The article concludes that the unchecked exploitation of copyrighted works for model training constitutes a systemic market failure, necessitating the urgent legislative creation of a compulsory licensing regime tailored specifically for large-scale machine learning, thereby ensuring equitable remuneration for human creators in the algorithmic age.
Keywords: Generative AI, Copyright Law, Fair Use, Intellectual Property, Machine Learning, Berne Convention, Data Scraping