Open-Source AI And The Tension Between Innovation And Protection
- IJLLR Journal
- 2 hours ago
- 2 min read
Mohammed Salman Siddiqui, Amboji Archana & Ammarah Ishaq
ABSTRACT
The rapid proliferation of Artificial Intelligence has revolutionized global innovation ecosystems, creating a profound tension between the collaborative spirit of open-source AI and the traditional frameworks of Intellectual Property Rights. This research addresses the central question of how legal systems can reconcile the autonomous nature of AI-generated creations with a regulatory landscape designed primarily for human inventors and authors. This study is highly relevant to global digital governance as the rise of machine learning challenges established notions of ownership, authorship, and the social contract between innovators and the public.
This research investigates the limitations of human-centric IP statutes addressing autonomous machine outputs and analyses how open-source collaborative models impact innovation and competition, evaluate international judicial responses to non-human inventorship alongside potential regulatory pathways to balance public knowledge access with private protection incentives. Through doctrinal legal analysis of international treaties, (TRIPS Agreement and Berne Convention), and domestic statutes, specifically the Indian Patents Act, 1970, and Copyright Act, 1957. It also incorporates a comparative study of diverse jurisdictional approaches, including the US, EU, UK, and South Africa, alongside a case law review of landmark disputes like the DABUS applications. The analysis finds that current human-centric requirements for originality and inventive steps create a protection deficit for AI-generated works, potentially forcing innovators toward trade secrecy. Open-source models significantly influence competition by lowering barriers to entry, yet they remain vulnerable to algorithmic collusion and unauthorized data scraping. Furthermore, the lack of legal personhood for AI systems creates a liability vacuum. The research argues that while most jurisdictions reject AI as a sole inventor, recognizing distal labour, the human effort in designing the machine that creates, is vital for maintaining economic incentives.
The study concludes that resolving the tension between innovation and protection requires a forward-looking, flexible IPR system that moves beyond anthropocentric biases. Implementing sui generis rights for machine- generated works or a human guardian mechanism for AI IP administration provides a viable path to foster technological growth while ensuring legal accountability and equitable access.
