AI2030 Responsible AI Tools Marketplace

Key Takeaways

Current copyright laws have significant gaps: The paper identifies that existing regulatory frameworks in the US, EU, China, and UK fail to adequately address how copyrighted works and open science outputs are used to train AI systems, leaving creators without sufficient protection.
Fair use doctrine is insufficient protection: While current legal doctrine treats AI training as potentially fair use, the author argues this mechanism inadequately protects copyright holders and proposes that authors should have explicit opt-out rights to prevent their work from being used for AI training, regardless of fair use considerations.
Creative Commons licenses weren't designed for AI: Widely adopted licensing mechanisms like Creative Commons fail to address the specific nuances of how AI systems use copyrighted material, creating a mismatch between creator intent and actual AI training practices.
AI systems lack meaningful attribution: The paper highlights that the pervasive lack of attribution within AI systems fundamentally challenges established notions of originality and intellectual property, making it difficult to trace whose work contributed to AI outputs.
Universities should lead responsible AI governance: Rather than waiting for international legislation, the author argues that universities are positioned to play a leading role in establishing responsible AI governance practices, suggesting institutional action may be necessary before comprehensive legal frameworks emerge.

Abstract

The integration of generative artificial intelligence (GenAI) and large language models (LLMs) into scientific research and higher education presents a paradigm shift, offering revolutionizing opportunities while simultaneously raising profound ethical, legal, and regulatory questions. This study examines the complex intersection of AI and science, with a specific focus on the challenges posed to copyright law and the principles of open science. The author argues that current regulatory frameworks in key jurisdictions like the United States, China, the European Union, and the United Kingdom, while aiming to foster innovation, contain significant gaps, particularly concerning the use of copyrighted works and open science outputs for AI training. Widely adopted licensing mechanisms, such as Creative Commons, fail to adequately address the nuances of AI training, and the pervasive lack of attribution within AI systems fundamentally challenges established notions of originality. While current doctrine treats AI training as potentially fair use, this paper argues such mechanisms are inadequate and that copyright holders should retain explicit opt-out rights regardless of fair use doctrine. Instead, the author advocates for upholding authors' rights to refuse the use of their works for AI training and proposes that universities assume a leading role in shaping responsible AI governance. The conclusion is that a harmonized international legislative effort is urgently needed to ensure transparency, protect intellectual property, and prevent the emergence of an oligopolistic market structure that could prioritize commercial profit over scientific integrity and equitable knowledge production. This is a substantially expanded and revised version of a work originally presented at the 20th International Conference on Scientometrics & Informetrics (Kochetkov, 2025).

The second version version substantially revises the original preprint through expanded legal analysis, representation of the new technical standard (RSL 1.0), and removing substantial material lacking direct relevance to copyright and AI training

Key Takeaways

Current copyright laws have significant gaps: The paper identifies that existing regulatory frameworks in the US, EU, China, and UK fail to adequately address how copyrighted works and open science outputs are used to train AI systems, leaving creators without sufficient protection.
Fair use doctrine is insufficient protection: While current legal doctrine treats AI training as potentially fair use, the author argues this mechanism inadequately protects copyright holders and proposes that authors should have explicit opt-out rights to prevent their work from being used for AI training, regardless of fair use considerations.
Creative Commons licenses weren't designed for AI: Widely adopted licensing mechanisms like Creative Commons fail to address the specific nuances of how AI systems use copyrighted material, creating a mismatch between creator intent and actual AI training practices.
AI systems lack meaningful attribution: The paper highlights that the pervasive lack of attribution within AI systems fundamentally challenges established notions of originality and intellectual property, making it difficult to trace whose work contributed to AI outputs.
Universities should lead responsible AI governance: Rather than waiting for international legislation, the author argues that universities are positioned to play a leading role in establishing responsible AI governance practices, suggesting institutional action may be necessary before comprehensive legal frameworks emerge.

Abstract

Who Owns the Knowledge? Copyright, GenAI, and the Future of Academic Publishing

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Who Owns the Knowledge? Copyright, GenAI, and the Future of Academic Publishing

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

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