The AI Copyright Clash: Who Controls Academic Knowledge?

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Author: Denis Avetisyan

The rise of generative AI is forcing a reckoning with copyright law and challenging established norms in academic publishing.

Current legal frameworks are ill-equipped to address the use of scholarly works in AI training, demanding new policies and proactive institutional governance.

The rapid integration of generative AI into academic research presents a paradox: while promising unprecedented advancements, it simultaneously challenges foundational principles of intellectual property and open science. This paper, ‘Who Owns the Knowledge? Copyright, GenAI, and the Future of Academic Publishing’, examines the legal and ethical complexities arising from the use of copyrighted works to train large language models. It argues that current fair use exceptions are insufficient, necessitating a shift toward upholding authors’ rights to control the use of their work for AI training and a more proactive governance role for universities. Will a harmonized international framework emerge to safeguard both scientific integrity and equitable knowledge production in this rapidly evolving landscape?

The Evolving Landscape of AI and Intellectual Property

Academic publishing and research workflows are undergoing a swift and substantial evolution driven by generative artificial intelligence, notably Large Language Models. These models are no longer simply tools for data analysis or literature review; they are increasingly capable of drafting manuscripts, summarizing complex research, and even generating novel hypotheses. Researchers are experimenting with AI to accelerate peer review processes, translate materials for broader accessibility, and personalize learning experiences. This integration extends beyond traditional text-based research, with AI assisting in data visualization, code generation for computational studies, and the analysis of multimedia content. While the potential for increased efficiency and innovation is significant, this rapid adoption necessitates a critical examination of the ethical and legal implications accompanying this technological shift, demanding new standards for transparency and accountability within the scholarly community.

The swift emergence of generative AI tools presents a fundamental challenge to established copyright principles, which historically center on human creation and authorship. Current legal frameworks struggle to accommodate outputs generated by algorithms trained on vast datasets, raising questions about ownership and infringement. The very notion of an ‘author’ becomes blurred when an AI model synthesizes information and produces novel content; traditional copyright law doesn’t readily assign rights to non-human entities. This discordance creates significant legal uncertainty for both developers and users of these technologies, potentially stifling innovation and prompting protracted legal battles over intellectual property rights as the models’ outputs increasingly resemble, or even directly incorporate, copyrighted material.

The legal status of utilizing copyrighted material to train generative AI models remains a central point of contention, with current frameworks struggling to accommodate this novel practice. The question isn’t simply whether AI-generated content infringes, but whether the process of learning from vast datasets of protected works constitutes a violation of copyright. Arguments for ‘fair use’ hinge on the transformative nature of AI – that the models create something new and distinct – however, rights holders rightly question whether this transformation sufficiently justifies the reproduction and analysis of their content without permission. This paper argues that a lack of clear guidance risks concentrating the development and deployment of powerful AI tools in the hands of a few large entities – those with the resources to navigate complex legal battles or proactively secure expansive datasets – potentially leading to an oligopolistic market structure and stifling innovation from smaller players.

Derivative Works in the Age of Machine Learning

AI training processes inherently generate derivative works according to established copyright principles. When an AI model is trained on copyrighted data – text, images, code, or other media – it analyzes and transforms that data to identify patterns and relationships. The subsequent outputs produced by the model, whether text generation, image creation, or code completion, are considered derivatives because they are based upon and fundamentally shaped by the original copyrighted material. This differs from a simple reproduction; the AI doesn’t store or directly replicate the source data, but rather learns from it to create new, original content that still carries the influence of the training set. The legal implications stem from the fact that creating derivative works generally requires permission from the copyright holder of the original work, even when the derivative work is substantially different.

AI-generated outputs are legally distinct from direct copyright infringement due to their transformative nature; however, the extent to which reliance on copyrighted training data constitutes a derivative work remains a key legal question. Current copyright law typically protects original works of authorship, and while AI outputs are novel, their creation is fundamentally dependent on pre-existing copyrighted material. This dependence raises concerns about potential violations, particularly regarding substantial similarity and the potential to displace the market for the original works. The legal analysis centers on whether the AI output is sufficiently different from the source material to be considered a new, independently copyrightable work, or if it remains inextricably linked to, and therefore derivative of, the copyrighted training data.

Demonstrating compliance with copyright regulations increasingly requires detailed documentation of AI training data provenance. This includes not only identifying the sources of data used-such as specific websites, datasets, or licensed materials-but also recording the versions used, the dates of access, and any modifications made during the data preparation process. Maintaining a verifiable audit trail of this information is critical for addressing potential legal challenges related to copyright infringement, as it allows developers to demonstrate a good-faith effort to utilize data legally and to potentially qualify for fair use defenses. Furthermore, clear provenance facilitates the identification and removal of improperly sourced or restricted data, mitigating legal risks and promoting responsible AI development practices.

Existing US and international copyright law, largely formulated before the advent of large-scale machine learning, presents challenges in addressing AI-generated outputs. These legal frameworks traditionally focus on direct reproduction or substantial similarity to pre-existing works, concepts that are difficult to apply to the transformative nature of AI training and derivative generation. Core tenets such as authorship and fair use are being re-examined in light of algorithms that learn from and synthesize data, creating outputs that do not directly replicate any single source material but are nonetheless reliant on copyrighted works. The absence of specific legal precedent and the varying interpretations of existing laws across jurisdictions create uncertainty for developers and rights holders regarding liability and ownership of AI-generated content.

Navigating the Legal Landscape: Openness, Signals, and Fair Use

The Fair Use Doctrine, codified in Section 107 of the US Copyright Act, permits limited use of copyrighted material without permission for purposes such as criticism, comment, news reporting, teaching, scholarship, or research. Its application to AI training is contentious, hinging on a four-factor balancing test: the purpose and character of the use (including whether it is commercial or non-profit), the nature of the copyrighted work, the amount and substantiality of the portion used in relation to the copyrighted work as a whole, and the effect of the use upon the potential market for or value of the copyrighted work. Currently, legal interpretations vary regarding whether AI model training qualifies as transformative use – a key factor favoring fair use – and the extent to which copying entire works for training impacts the market. Consequently, the permissibility of AI training under fair use remains uncertain and is subject to ongoing litigation and evolving legal precedents.

The Open Science movement promotes the availability of data, fostering conditions suitable for legal and ethical AI training. However, simply making data accessible is insufficient; licensing terms are critical. While open access repositories exist, the licenses governing the data-such as those specifying permitted uses, attribution requirements, and restrictions on commercial applications-must be carefully evaluated to ensure compatibility with machine learning workflows. Many existing open science datasets utilize licenses not originally designed for the computational demands of AI, potentially creating ambiguities regarding permissible training practices. Therefore, responsible AI development within the Open Science framework necessitates a thorough understanding and adherence to the specific licensing terms associated with each dataset used.

While Creative Commons (CC) licenses have significantly broadened access to copyrighted works, they were not designed with the specific requirements of machine learning in mind. Existing CC licenses lack clarity regarding the scope of permitted uses for training AI models, particularly concerning the creation of derivative works and commercial applications. The licenses do not differentiate between human-readable consumption and the computational processing required for AI training, leading to ambiguity about whether data mining and model training constitute permissible use. Consequently, rights holders may be hesitant to release data under standard CC licenses for AI training purposes, and users may face legal uncertainty when utilizing such data. This inadequacy necessitates the development of new or supplemental licensing mechanisms tailored to the unique characteristics of AI and machine learning.

CC Signals is a developing set of technical standards designed to embed copyright and usage permissions directly into digital works, allowing machine learning models to automatically interpret and respect those rights. This framework utilizes standardized metadata tags – specifically, Resource Description Framework (RDF) – to clearly communicate licensing terms, such as whether a work is permitted for training, and under what conditions. Unlike traditional licenses which require human interpretation, CC Signals aims to provide a machine-readable layer of information, reducing the ambiguity surrounding data usage for AI development. Currently under development by Creative Commons and a consortium of stakeholders, CC Signals seeks to address the limitations of existing licenses in the context of large-scale machine learning, potentially streamlining the process of legally and ethically sourcing training data.

Mitigating Risk and Shaping a Sustainable Future

Retrieval-Augmented Generation (RAG) represents a significant step toward addressing copyright concerns within large language models. Instead of relying solely on the vast amounts of data memorized during training – which may include copyrighted material without explicit permission – RAG systems dynamically access and incorporate information from external, verifiable sources during the generation process. This approach fundamentally shifts the mechanism of content creation; the AI doesn’t recall information, but rather retrieves it and then synthesizes it into a response. By grounding outputs in documented evidence, RAG not only enhances the accuracy and trustworthiness of AI-generated text but also provides a clear audit trail, potentially mitigating legal risks associated with copyright infringement and fostering greater transparency in AI content creation. This method allows for more responsible innovation, moving beyond simply replicating existing works to building upon them with proper attribution and verifiable sources.

Governments worldwide are actively crafting legislation to address the unique challenges presented by artificial intelligence, with the European Union and the United Kingdom at the forefront. The EU AI Act proposes a risk-based framework, categorizing AI systems and imposing stringent requirements on high-risk applications, while simultaneously mandating transparency regarding the data used in their training. Similarly, the UK’s proposed bill emphasizes data governance and accountability, seeking to establish clear lines of responsibility for AI-driven outcomes. Both initiatives share a common thread: a commitment to fostering innovation while safeguarding against potential harms, and a crucial focus on ensuring that developers can demonstrate how AI systems utilize data, thereby promoting trust and responsible deployment. These emerging regulations aren’t simply about restriction; they aim to establish a legal foundation that encourages ethical AI development and protects individuals from bias and misuse.

The convergence of emerging AI methodologies and proactive regulatory frameworks is fundamentally reshaping the landscape of artificial intelligence development. Nations are no longer solely focused on fostering innovation; instead, they are actively implementing policies that prioritize accountability and transparency in AI systems. This dual approach – combining techniques like Retrieval-Augmented Generation, which grounds outputs in verifiable data, with legislation addressing data disclosure – moves beyond simply allowing AI advancement to guiding it towards responsible applications. The resulting ecosystem isn’t just about building more powerful AI, but about ensuring its deployment aligns with ethical considerations and respects intellectual property rights, paving the way for a future where AI benefits society without compromising existing legal structures.

A globally coordinated strategy regarding copyright and artificial intelligence regulation is increasingly vital for sustaining both innovation and the rights of intellectual property creators. This work proposes a move towards greater openness, requiring explicit author permission for the use of copyrighted material in AI training, and establishing fair licensing practices. Without such a framework, there is a substantial risk of concentrating power within a few dominant entities – an oligopolistic structure that could stifle competition and limit access to the benefits of AI technology. By prioritizing transparency and equitable compensation for creators, a more sustainable and inclusive AI ecosystem can be fostered, ensuring that the incentives for creative work remain strong and that the advancements in artificial intelligence benefit a wider range of stakeholders.

The exploration of copyright’s limitations within the context of Generative AI training highlights a critical tension between established legal frameworks and rapidly evolving technological capabilities. This paper correctly identifies the need for a proactive stance from academic institutions, moving beyond reactive measures to shape a future where intellectual property is respected and innovation isn’t stifled. As Barbara Liskov once stated, “It’s one of the most powerful laws of nature that the self-serving is always self-defeating.” This resonates deeply with the argument presented; a solely profit-driven approach to AI training, disregarding the rights of creators, will ultimately prove unsustainable and detrimental to the broader academic ecosystem. Good architecture is invisible until it breaks, and only then is the true cost of decisions visible.

Where Do We Go From Here?

The questions raised by the intersection of generative AI and scholarly communication are not, at their core, about technology. They are, predictably, about control. Existing legal frameworks, constructed for a world of finite reproduction, strain to accommodate a system of near-infinite replication inherent in large language model training. Attempts to retrofit these frameworks will likely yield increasingly complex and fragile solutions – cleverness, as it were, rarely equates to robustness. The pursuit of granular permissions and algorithmic gatekeeping feels less like a sustainable strategy and more like a desperate attempt to contain an emergent property of the digital landscape.

A more fruitful path lies in acknowledging the systemic nature of the problem. Universities, as both producers and consumers of scholarly knowledge, are uniquely positioned to shape a new paradigm. This necessitates a shift from reactive copyright enforcement to proactive data governance – a reimagining of academic publishing not as a marketplace of ideas, but as a shared ecosystem. Such an ecosystem demands transparency in AI training datasets and a re-evaluation of the incentives that currently prioritize individual attribution over collective benefit.

Ultimately, the longevity of any solution will depend not on its legal intricacy, but on its simplicity. A system built on trust, open access, and a recognition that knowledge thrives through dissemination, not restriction, is more likely to endure than one predicated on control. The challenge, of course, is that simplicity often requires a level of intellectual honesty and collective action that is, historically, in short supply.

Original article: https://arxiv.org/pdf/2511.21755.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-01 17:07