AI-Generated Works and the Public Domain: Normative Boundaries of Authorship without Human Intervention; [Obras geradas por inteligência artificial e domínio público: limites normativos da autoria sem intervenção humana]

Ramos-Zaga F.A. · Revista Iberoamericana de la Propiedad Intelectual (2026) · DOI: 10.26422/ripi.2026.espia.zag

Resumen

The growing sophistication of artificial intelligence systems in content generation presents unprecedented legal challenges to copyright law, whose normative framework has historically been grounded in the concept of human authorship. Within this context, the present article critically examines the legal viability of recognizing copyright over works produced without significant human intervention, proposing a normative delineation consistent with the foundational principles of intellectual property. Through a systematic review of the philosophical and historical foundations of authorship, the liberal, romantic, and utilitarian theories are analyzed as interpretive frameworks to establish minimal thresholds for legitimate attribution. Contemporary regulatory models are assessed through a comparative analysis of case law and scholarly doctrine, highlighting the normative tensions arising from creative automation. The findings demonstrate that traditional conceptual frameworks are structurally incompatible with the attribution of authorship to non-human entities, and that the absence of precise criteria regarding the required degree of human intervention contributes to legal uncertainty while facilitating the concentration of rights in corporate hands. It is concluded that, in the absence of meaningful human contribution, AI-generated works should be deemed part of the public domain, insofar as this approach preserves the balance between incentive and access and upholds the core values of equity, openness, and informational pluralism that underpin the legitimacy of the intellectual property regime. © (2026), (Austral University). All rights reserved.

Instituciones: Universidad Privada del Norte