Algorithmic Authorship, Data Sovereignty, And Intellectual Property Rights: Navigating the Intersection of Law, Science, And Society in the

Abstract

Abstract The rapid convergence of artificial intelligence (AI), data science, and legal frameworks has created a profound crisis within global and domestic Intellectual Property Rights (IPR) regimes. Traditionally, copyright and patent laws were constructed around the central premise of human agency, recognizing intellectual labor as an extension of human dignity and personality. However, the rise of Generative AI platforms, machine learning models, and autonomous algorithmic systems disrupts foundational legal principles including authorship, inventiveness, originality, and infringement. This paper examines the multidisciplinary intersection of law, computer science, and social sciences regarding IPR. It deconstructs three critical dilemmas: (1) the legal status of AI-generated works and the "human author" requirement under copyright law; (2) the patentability of AI-invented subject matter and the doctrine of the "Person Having Ordinary Skill in the Art" (PHOSITA); and (3) the socio-economic implications of training data scraping, digital commons, and data sovereignty. By analyzing statutory provisions, recent judicial precedents across jurisdictions, and socio-legal frameworks, this study highlights the inadequacy of existing legal doctrines to address non-human innovation. The paper proposes a balanced normative framework incorporating a sui generis legal model for AI outputs, compulsory licensing for dataset training, and transparent algorithmic disclosure to foster technological innovation while protecting human creators and public domain integrity.