Technical, legal, and ethical challenges of generative artificial intelligence: an analysis of the governance of training data and copyrights - Marcelo Pasetti; James William Santos; Nicholas Kluge Corrêa; Nythamar de Oliveira; Camila Palhares Barbosa (Discover Artificial Intelligence)
Abstract This article examines the legal, technical, and ethical challenges of generative AI, focusing on the governance of training data and copyright compliance. It addresses the growing tension between AI development and the rights of content creators, particularly regarding the unauthorized use of copyrighted material for model training. By analyzing regulatory frameworks in the United States, European Union, Japan, and Brazil, the study highlights how existing mechanisms–such as fair use, text and data mining (TDM) exceptions, and hybrid models–remain inadequate to resolve the opacity and legal uncertainty surrounding AI training datasets. Drawing on insights from Henderson, Yu, Narayanan, and Kapoor, the paper demonstrates that the absence of transparency not only compromises legal accountability but also exacerbates epistemic risks and distributive asymmetries. Adopting a comparative legal-philosophical methodology, the study proposes governance solutions centered on mandatory transparency obligations, ethical compensation schemes for rights holders, and robust audit mechanisms. These recommendations aim to balance innovation incentives with fairness, sustainability, and the protection of intellectual property in the AI-driven economy.