Copyright Challenges In The Age Of AI- Generated Content - Implications For Library Collections
Author: LawSphereX
Category: Intellectual Property
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COPYRIGHT
CHALLENGES
IN THE AGE OF AI- GENERATED CONTENT: IMPLICATIONS FOR LIBRARY COLLECTIONS Aqsa Kalam, B.A. LL.B., Jamia Millia Islamia, New Delhi Zikra Akbar, B.A.LL.B., Jamia Millia Islamia, New Delhi
ABSTRACT
Artificial intelligence is rapidly changing how content is created, stored, and accessed, and academic law libraries in India are feeling the impact. As AI tools such as ChatGPT and Gemini produce legal texts, case summaries, and research materials with minimal human input, serious questions arise about who owns such content and how libraries should handle it. The Copyright Act, 1957 recognizes only human authors, leaving AI generated works in a legal grey area. International frameworks including the Berne Convention and the TRIPS Agreement follow the same human centric philosophy, offering no clear guidance on ownership or protection of AI generated works. This paper explores what this legal uncertainty means for law libraries in practice. When there is no recognized rights holder, how do libraries acquire, license, catalogue, and preserve AI generated content responsibly?
The paper also addresses the growing problem of AI hallucination, where AI systems generate fictitious case citations and fabricated legal precedents that have already appeared before Indian courts including the Bombay and Delhi High Courts. Beyond authenticity, the paper raises concerns about unequal access, as sophisticated AI legal research tools remain concentrated among wellfunded National Law Universities, leaving smaller institutions and their users at a significant disadvantage. Keywords: Artificial Intelligence, Copyright Law, Law Libraries, AI Generated Content, Indian Copyright Act 1957, Legal Misinformation, Library Collections.
1. Introduction
Artificial Intelligence (AI) has rapidly transformed the way information is created, accessed, and distributed across the world. With the emergence of generative AI tools capable of producing text, images, code, and other creative works, the boundaries between human creativity and machine-generated content have become increasingly blurred. While these technologies offer significant opportunities for innovation, efficiency, and knowledge creation, they also raise complex legal and ethical questions, particularly in the field of copyright law. Copyright law has traditionally been built upon the principle of human authorship, granting legal protection to original works created through human intellectual effort. However, the growing use of AI systems that can independently generate content challenges this foundational concept.
In India, the Copyright Act, 1957 does not expressly address many of these issues, leaving significant gaps in the regulation of AI-generated content. 1 These challenges are especially relevant for academic law libraries, which serve as important repositories of legal knowledge and information. As AI-generated legal materials become increasingly common, libraries face difficulties in acquiring, licensing, cataloguing, preserving, and verifying such content. The absence of clear legal standards creates uncertainty regarding the status and management of AI-generated works, while concerns about authenticity, misinformation, and copyright compliance further complicate collection development practices. This paper examines the intersection of copyright law and artificial intelligence from the perspective of academic law libraries in India.
It explores the existing legal framework, the copyright implications of AI-generated content and AI training data, and the practical challenges faced by libraries in managing these emerging resources. The study further highlights the need for legislative reform and institutional preparedness to ensure that law libraries can continue to provide reliable, accessible, and legally compliant information services in an increasingly AI-driven environment. 1 Amith Sriram, Copyright Facing the Challenges of Generative Artificial Intelligence: Judicial Practice and Legislative Strategies in India, the United States and the European Union, J. OF DIGITAL TECHS. & L. (Dec. 27, 2025), https://www. lawjournal. digital/jour/article/view/592? locale=en_US.
2. Understanding AI Generated Content
2.1 Conceptual Framework and Definitional Scope
AI generated content refers to any material such as text, images, designs, code, or other creative work that is produced with the help of artificial intelligence. These systems rely on human instructions, often called prompts, and use technologies like machine learning and natural language processing to create content that resembles human expression in tone, style, and structure. Unlike earlier forms of artificial intelligence that were mainly used to analyze data, identify patterns, or organize information, generative AI is designed to create new and original material. It is trained on large collections of data and uses that training to generate fresh outputs based on the user request. This ability to produce new content is what makes generative AI different from traditional AI tools.
In recent years, AI has become much more advanced and widely available, which has increased its role in content creation. While tools like ChatGPT have made this technology more popular, many other AI systems are also used to produce different forms of content, including written articles, product descriptions, social media captions, visuals, and marketing materials. AI generated content has many practical uses. It can help create drafts quickly, reduce the time spent on repetitive writing tasks, and support the production of a wide range of materials such as blog posts, images, code, and promotional content. It is also useful for tasks like brainstorming ideas, developing content briefs, preparing meta descriptions, and assisting with keyword research.
However, despite its efficiency, AI generated content is most effective when reviewed by humans to ensure it is accurate, relevant, original, and appropriate for its intended purpose2.
2.2 Technological Architecture of Generative AI Systems
Artificial Intelligence works by collecting and processing large amounts of data from sources such as text, images, videos, and audio. It first analyzes the input data and identifies the user requirements, including the type, tone, and style of content needed. Through Natural Language 2 Anshul Tewari, What Is AI-Generated Content: Is It a Trend, or the Future of Digital Content?, VTIGER CRM BLOG (Feb. 10, 2026), https://www.vtiger.com/blog/is-ai-generated-content-a-trend-or-is-it-the-future/.
Processing, AI understands the context and meaning of the information by breaking text into smaller units called tokens and recognizing important entities such as names, dates, and locations. Advanced machine learning models, trained on vast datasets, then use this understanding to generate relevant and coherent content. The generated output is organized into logical sections and refined through grammar correction, tone adjustment, and originality checks to ensure quality. AI can further optimize the content by incorporating relevant keywords, improving formatting, and integrating multimedia elements when necessary. Finally, the content is reviewed, published on suitable platforms, and its performance is monitored using analytics. The insights gained from user engagement and feedback help improve future AI generated content, making the system more effective and accurate over time.3
3 Copyright Law and AI
Current Framework
3.1 Indian Copyright Act, 1957
The cornerstone of intellectual property protection in India is the Copyright Act, 1957, which safeguards original literary, dramatic, musical, and artistic works. The Act under Section 2(d) defines "author" as the individual who brings a work into existence, establishing human creative agency as the foundational requirement for copyright ownership. Section 2(d)(vi) further designates authorship of computer-generated works to "the person who causes the work to be created. " However, when the Act was framed in 1957, legislative intent was confined to computational outputs requiring direct human instructions, never contemplating autonomous AI content generation. 4 Since AI systems do not possess legal personality and cannot hold rights or discharge obligations, they cannot assert ownership over content they produce.
This places India in alignment with major jurisdictions such as the United States, where human authorship remains a non-negotiable threshold for copyright protection. For academic law libraries, this creates a substantial legal vacuum when acquiring, cataloguing, or preserving AI generated materials. 5
3 Id.
4 The Copyright Act, 1957, No. 14, Acts of Parliament, 1957 (India).
5 Astha Ojha, AI & Copyright in India
Law, Policy, and the Future of Creative Rights, NAT'L E- GOVERNANCE DIV. (2025), https://negd.gov.in/wp-content/uploads/2025/10/Astha-Ojha-AI-Copyright-in- India-Bridging-the-Digital-Divide.pdf.
3.2 International Frameworks
Berne Convention and TRIPS The international copyright framework rests upon the Berne Convention of 1886 and the TRIPS Agreement of 1994. Under Article 9(1) of TRIPS6, all signatory nations must adhere to Berne obligations, extending the implicit human authorship requirement globally. Both treaties are built upon a human centric philosophy: recognizing the natural rights of creators and incentivizing creative production for societal benefit. Neither rationale applies to purely AI generated outputs, making copyright protection for such works permissible only where a demonstrable human creative contribution can be established.
3.3 Contested Ownership of AI Generated Works
Ownership of AI generated content is among the most contested issues in contemporary intellectual property law. Three potential claimants exist: the AI developer, the user providing the prompt, and in some cases no identifiable owner, placing the work in the public domain. Under Indian law, prompt-based authorship may be acknowledged where the user has exercised significant intellectual effort. However, this lacks statutory clarity. In the United States, the Copyright Office declined to register AI artwork from "Zarya of the Dawn" for insufficient human creative control. Similarly, India's Copyright Office revoked registration of artwork by AI system Raghav upon finding no human author existed under the Copyright Act, 1957, exposing a fundamental legislative gap.7
3.4 Legislative Gaps and the Road to Reform
In May 2025, the Ministry of Commerce and Industry constituted an expert committee to assess whether the Copyright Act, 1957 adequately addresses generative AI challenges, driven by ANI Media Private Limited vs Open AI 8 Incorporated before the Delhi High Court, 2024, which raised critical questions including whether AI training on copyrighted material constitutes infringement and whether Section 52 fair dealing applies to commercial AI developers. Since AI systems do not meet the statutory threshold of authorship, no enforceable ownership claim over AI generated outputs can arise. This creates three direct consequences for academic 6 Agreement on Trade-Related Aspects of Intellectual Property Rights art. 9(1), Apr. 15, 1994, 1869 U.N.T.S. 299.
7 Sriram, supra note 1.
8 ANI Media Pvt. Ltd. v. OpenAI Inc., CS (Comm.) No. 1028/2024 (Delhi High Court 2024).
law libraries: inability to execute valid licensing agreements, legal uncertainty in statutory deposit preservation, and infringement liability risks when cataloguing works of indeterminate authorship, collectively underscoring the urgent need for legislative reform.9
4. Training Data, Copyright Infringement, and Emerging Jurisprudence
4.1 The Copyright Implications of AI Training Data
Copyright law is a legal framework that protects the original creations of authors, artists, musicians, filmmakers, and other creators, granting them exclusive rights to control how their work is used, copied, distributed, or shared. In India, the Copyright Act, 1957 protects literary, artistic, musical, dramatic, and cinematographic works for the lifetime of the author plus sixty years after their death. Any reproduction or use of copyrighted material ordinarily requires permission from the copyright owner, although certain exceptions such as fair dealing may apply. With the rapid growth of generative AI, the question of whether using copyrighted content to train AI systems constitutes infringement has emerged as one of the most pressing unresolved legal issues globally.
4.2 Intersection of Copyright Law with Artificial Intelligence
Copyright and artificial intelligence are closely connected because AI systems learn from large amounts of data that may include books, articles, images, music, and other copyrighted works, raising fundamental questions about whether AI developers require permission to use such materials for training their models. The New York Times versus Microsoft and OpenAI 10case is one of several landmark lawsuits seeking to determine how copyright laws apply to AI in the United States. Similarly, Getty Images versus Stability AI before the UK High Court addressed whether AI image generation systems trained on copyrighted photographs constitute infringement, with the court acknowledging that Getty Images had reasonable prospects of succeeding in its copyright claims.
In India, the ANI versus OpenAI case before the Delhi High Court represents the first major judicial engagement with AI and copyright, examining whether using copyrighted material to train AI models constitutes infringement or qualifies as fair dealing under Section 52 of the Copyright Act, 1957. These cases collectively signal that the
9 Id.
10 The New York Times Co. v. Microsoft Corp. & OpenAI, Inc., No. 1:23-cv-11195 (S.D.N.Y. filed Dec. 27, 2023). legal boundaries of AI training data remain deeply contested and are likely to shape copyright jurisprudence globally for years to come.
4.3 Academic Libraries as Sources of AI Training Data
Academic libraries occupy a uniquely consequential position in the AI training data ecosystem. As repositories of peer reviewed journals, legal texts, case law compilations, and scholarly monographs, they house precisely the categories of high-quality authoritative content that AI developers seek for training large language models. While libraries traditionally facilitate access to knowledge through fair dealing and statutory licensing, the use of their collections as AI training data represents a qualitatively different form of exploitation involving large scale computational reproduction that may not be covered by existing fair dealing provisions. Although the Copyright Act, 1957 grants creators control over their works, it does not clearly address whether using copyrighted content for AI training constitutes infringement.
Many countries face similar legal challenges as existing copyright laws struggle to keep pace with rapid technological advancements. In India, the central challenge remains creating a balanced legal framework that supports AI innovation while protecting the rights of copyright owners, with academic libraries positioned at the heart of this debate. 11
5. Implications for Library Collections
5.1 Acquiring AI Generated Content
The emergence of AI generated content has fundamentally altered the landscape of collection development in academic law libraries. Traditionally, libraries have operated within a wellestablished acquisition framework relying upon clearly defined authorship, verified copyright ownership, and negotiated licensing agreements. The proliferation of AI generated legal texts, case summaries, and research materials has disrupted this framework, as the legal status of such content remains ambiguous under existing copyright law. 12 A critical challenge lies in the inability to verify the provenance and legal ownership of AI generated materials before acquisition. Since AI systems do not qualify as legal authors under the Copyright Act, 1957, libraries risk unknowingly acquiring content subject to future legal disputes.
While awareness of AI tools is growing among Indian library professionals, institutional policies governing acquisition of AI generated content remain largely undeveloped across Indian academic libraries. 13 Furthermore, the sheer volume of AI generated content entering the publishing ecosystem poses a quality control challenge, as such materials may not meet standard acquisition criteria, potentially overwhelming collections and diminishing the discovery experience for users, with serious consequences for law libraries where accuracy of legal information is paramount.
5.2 Licensing Challenges
Licensing constitutes one of the most significant practical challenges arising from AI generated content in library collections. Conventional licensing agreements are predicated upon an identifiable rights holder who can grant permissions for use, reproduction, and distribution. In the context of AI generated works, this foundational requirement breaks down entirely, as no recognized legal owner exists under the current Indian copyright framework. The Association of Research Libraries has highlighted that AI is reigniting longstanding debates around digital rights and licensing, urging institutions to preserve fair use rights when negotiating licensed digital resources.
Major AI developers have responded to legal uncertainty by aggressively pursuing content licensing deals with publishers, signaling that the industry itself does not consider existing fair use provisions a guaranteed legal defense. For Indian law libraries, the absence of a Text and Data Mining exception analogous to those under the European Union AI Act, 2025 creates additional complexity. Unlike the EU framework, which permits use of copyrighted material for AI training under defined conditions, India presently has no comparable provision, leaving library professionals without clear legal guidance. 14
5.3 Cataloguing and Classification Issues
The cataloguing and classification of AI generated content presents unique challenges that existing library metadata standards have not been designed to address. Effective cataloguing requires accurate attribution of authorship, identification of the rights holder, and adherence to established classification schemes. When authorship is either absent or legally contested, as is the case with purely AI generated materials, the integrity of the library catalogue is fundamentally compromised. Research on AI integration in Indian academic libraries has identified automated cataloguing and metadata generation as significant AI applications, while acknowledging that inconsistent 11 Yossi, How Academic Libraries Are Leveraging AI and Modern Reading List Systems to Transform Student Learning, MEDIUM, https://yossi1. medium.
com/how-academic-libraries-are-leveraging-ai-and-modernreading-list-systems-to-transform-student-dfae6df3688c. 12 Mala D, Application of Artificial Intelligence in Law Libraries and Legal Profession, SSRN (July 15, 2024), https://ssrn. com/abstract=5138825.
13 The Copyright Act, 1957, No. 14, Acts of Parliament, 1957 (India).
14 Association of Research Libraries, AI is Reigniting Decades-Old Questions over Digital Rights, but Fair Use Prevails, ASS'N OF RES. LIBRS. (Feb. 28, 2025), https://www.arl.org/blog/ai-is-reigniting-decades-oldquestions-over-digital-rights-but-fair-use-prevails/. subject term assignment and dependence on opaque AI decision-making processes remain unresolved challenges. This is particularly problematic for law libraries where precise classification directly impacts the effectiveness of legal research. The absence of standardized metadata protocols for AI generated legal content means different institutions may classify similar materials differently, leading to fragmentation and reduced interoperability across library networks.15
5.4 Preservation Concerns
Digital preservation of AI generated content raises layered legal and technical concerns for academic law libraries. Under the Delivery of Books and Newspapers (Public Libraries) Act, 1954, certain publications are subject to statutory deposit obligations. However, the application of this framework to AI generated digital content remains legally uncertain, as the statute predates the digital era and makes no provision for works of indeterminate authorship. 16 From a technical standpoint, AI generated content is often dynamic and version dependent, meaning the same prompt may generate different outputs at different points in time, making long-term preservation particularly difficult as libraries cannot guarantee the authenticity or reproducibility of archived materials.
The combined challenges of acquisition, licensing, cataloguing, and preservation collectively signal that academic law libraries in India are operating in an environment that has not kept pace with technological developments, underscoring the urgent need for coordinated legislative and institutional reform.
6. Fair use and fair dealing
6.1 Definition of fair use
The concept of “fair use” originated in the U.S., tracing back to early cases such as Gyles v Wilcox17 and later Folsom v. Marsh18, which established that courts should consider the purpose and nature of the selection, the amount and value of the material taken, and whether the use harms the original’s market. Generally, unauthorized use of copyrighted material is 15 A. Subaveerapandiyan, Exploring the Integration of Artificial Intelligence in Academic Libraries: A Study on Librarians' Perspectives in India, 8 OPEN INFO. SCI. 1 (2024), https://doi.org/10.1515/opis-2024-0006. 16 The Delivery of Books and Newspapers (Public Libraries) Act, 1954, No. 27, Acts of Parliament, 1954 (India).
17 Gyles v. Wilcox, (1740) 26 Eng. Rep. 489 (Ch.).
18 Folsom v. Marsh, 9 F. Cas. 342 (C.C.D. Mass. 1841).
infringement, but the law permits limited, unapproved copying for purposes like research, private study, criticism, news reporting, teaching, and review this is called fair use. Because the term lacks a precise statutory definition, courts apply a flexible four-factor test under Section 107 of the U. S. Copyright Act 19: (a) the purpose and character of the use (including whether it is commercial or nonprofit educational), (b) the nature of the copyrighted work, (c) the amount and substantiality of what was used, and (d) the effect of the use on the copyrighted work’s potential market. In Sk Dutt v. Law Book Co. & Ors20. , the court explained fair dealing as allowing use or copying that would otherwise be infringement but is permitted because of a recognized exception. In short, fair dealing functions as a lawful exception to intellectual property rights. In Civic Chandran v.
Ammini Amma21, the court said that when deciding fair dealing cases it looks at factors like the purpose of the use, how much of the original work was taken, and whether the new work is likely to compete with the original. There’s no single formula that applies to every copyright dispute; each case is decided on its own facts to determine whether the use qualifies as fair dealing.
6.2 Fair use under Indian copyright law
The concept of fair dealing is recognized differently in various countries according to their copyright laws. In India, the exceptions to copyright infringement are provided under Section
52 of the Copyright Act, 1957. This provision allows the use of copyrighted material without
permission for specific purposes such as private study, research, criticism, review, and reporting of current events. Although the Copyright Act does not specifically define "fair dealing," the concept is based on principles of fairness and equity. It permits limited use of copyrighted works when such use is reasonable and justified by the circumstances of the case. Fair dealing helps distinguish a genuine and lawful use of a work from unauthorized copying. In Wiley Eastern Ltd. v. IIM,22 the court observed that Section 52 protects freedom of expression by allowing activities such as research, private study, criticism, and review, which are connected to Article 19(1)(a) of the
19 U.S. Copyright Act of 1976 , Section107, 17 U.S.C.
20 A.I.R. 1954 All. 570 (Ind.).
21Civic Chandran v. C. Ammini Amma, 1996 PTC (16) 329 (Ker.) (Ind.).
22 A.I.R. 1996 Del. 281 (Ind.).
Constitution of India. The first factor is the amount and substantiality of the portion used. Courts examine whether a significant part of the original work has been copied. In the Blackwood case, the court held that the intention of the alleged infringer is relevant but not the deciding factor. What matters is whether a substantial part of the work has been reproduced. Similarly, in SK Dutt v. Law Book Co. , the court found that the copied material was not substantial enough to amount to infringement. The second factor is the purpose and character of the use. Fair dealing applies only when the use falls within the purposes mentioned in Section 5223. Courts also consider whether the new work adds something original or transformative. In V. Ramaiah v. K.
Lakshmaiah,24 the court emphasized that a person claiming fair dealing must make an independent contribution rather than simply copying the original work. The third factor is the effect on the market of the original work. Courts consider whether the use of the copyrighted material could compete with or reduce the demand for the original work. In the Blackwood case, the court noted that even the possibility of competition could indicate infringement.
7. Challenges
for Law Libraries
7.1 Authenticity and Verification Concerns
One of the most pressing challenges confronting academic law libraries in the context of AI integration is the fundamental question of authenticity. Law libraries have historically served as custodians of verified and authoritative legal information, relied upon by lawyers, judges, researchers, and students as trustworthy sources of legal knowledge. The introduction of AI generated legal content disrupts this foundational premise, as the accuracy and reliability of such content cannot be guaranteed through conventional verification mechanisms. A particularly serious dimension is the phenomenon of AI Hallucination, wherein generative AI systems produce outputs that appear credible but are factually incorrect or entirely fabricated. A comprehensive study published in the Journal of Empirical Legal Studies in 2025
23 Copyright Act, No. 14 of 1957, § 52 (Ind.).
24 A.I.R. 1989 A.P. 137 (Ind.).
found hallucination rates of seventeen percent for Lexis AI, thirty three percent for Westlaw AI Assisted Research, and as high as forty three percent for general purpose models in legal research tasks. The Indian judicial context has witnessed troubling manifestations of this problem, with Indian courts increasingly encountering litigants submitting AI generated phantom precedents as binding authority. In January 2026, the Bombay High Court imposed costs of fifty thousand rupees on a party for submitting fabricated AI generated case laws, underscoring the urgent need for law libraries to incorporate robust verification protocols preventing circulation of legally invalid AI generated content.25
7.2 Legal Misinformation and the Credibility Illusion
Closely related to the authenticity challenge is the broader risk of legal misinformation that AI generated content introduces into academic library environments. Unlike conventional misinformation, AI generated legal misinformation is particularly insidious because it is presented in a formal and authoritative register closely mimicking authentic legal writing, making it difficult for non-specialist users to identify errors without expert verification. 26 Research conducted by Stanford University in 2024 in a study on legal hallucinations in large language models found that general purpose AI models hallucinated between fifty eight and eighty eight percent of the time when tested against over eight hundred thousand verifiable legal questions.
For Indian academic law libraries, where students and early career researchers frequently rely on library resources for foundational legal research, the circulation of AI generated misinformation could have serious consequences for the quality of legal scholarship and education. The credibility illusion associated with AI generated content is further compounded by the fact that such systems generate content appearing rigorous and wellreasoned but containing embedded factual errors or misrepresentations of legal principles, significantly increasing the difficulty of identifying misinformation within library collections. 27
7.3 Equitable Access and the Digital Divide
The integration of AI tools into academic law library services raises significant concerns 25 Varun Magesh et al., Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools, 22 J. EMPIRICAL LEGAL STUD. 1 (2025), https://doi.org/10.1111/jels.12413. 26 Matthew Dahl et al., Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models, 16 J. LEGAL ANALYSIS 64, 67 (2024), https://doi.org/10.1093/jla/laae003.
27 Id. at 72.
regarding equitable access, particularly in the Indian context where considerable disparities exist in digital infrastructure, institutional funding, and technological capacity across different categories of educational institutions. Advanced AI powered legal research platforms such as Lexis AI and Westlaw AI Assisted Research are predominantly available to well-resourced institutions, creating a two-tier system wherein students at well-funded National Law Universities enjoy sophisticated AI tools while those at smaller institutions resort to unverified general purpose AI tools, thereby increasing exposure to hallucinated legal content. 28 The absence of formal AI literacy programs within Indian academic libraries further compounds this challenge, leaving many library professionals ill equipped to guide users in the responsible and critically informed use of AI generated legal content.
Addressing this literacy gap alongside the access disparity is essential to ensuring that AI integration in law libraries serves the cause of equitable and informed access to legal knowledge rather than amplifying existing inequalities in the Indian legal education ecosystem. 29
8. Recommendations
8.1 Reform of the Copyright Act, 1957
The rapid growth of generative artificial intelligence has exposed significant gaps in the existing copyright framework. The Copyright Act, 1957 should be amended to provide clear provisions regarding AI-generated content, authorship, ownership, and liability. Based on the study suggests that the law should establish criteria for determining when human involvement in AI-assisted works is sufficient to qualify for copyright protection. Clear statutory guidance would reduce legal uncertainty for creators, AI developers, publishers, and academic institutions.
8.2 Balancing Innovation with Copyright Protection
Future reforms should seek to balance technological advancement with the protection of intellectual property rights. A well-structured legal framework should encourage innovation in artificial intelligence while ensuring that authors, publishers, and other rights holders receive adequate recognition and protection for their creative contributions.
28 Subaveerapandiyan, supra note 13.
29 Id.
8.3 Development of National Guidelines for Libraries
Based on the findings of the paper The Ministry of Education, National Digital Library initiatives, and professional library associations should formulate standardized guidelines for the acquisition, licensing, cataloguing, and preservation of AI-generated content. Uniform practices would ensure consistency across academic institutions and reduce uncertainty regarding the management of AI-generated legal materials.
8.4 Mandatory Transparency and Disclosure Requirements
Libraries and publishers should adopt policies requiring AI-generated content to be clearly labelled. Metadata records should disclose the extent of AI involvement in the creation of a work, the AI tool used, and the level of human contribution. Such transparency would help users assess the reliability and legal status of the material.
8.5 Strengthening Verification Mechanisms
Given the growing risk of AI hallucinations and fabricated legal citations, academic law libraries should establish robust verification procedures before incorporating AI-generated legal content into their collections. Librarians should cross-check legal authorities, citations, and factual claims against authentic primary sources to maintain the integrity of library resources.
9. Conclusion
The intersection of artificial intelligence and copyright law presents one of the most complex challenges confronting academic law libraries in India today. The Copyright Act, 1957, with its foundational insistence on human authorship, creates a substantial legal vacuum when applied to AI generated works. International frameworks including the Berne Convention and TRIPS Agreement reinforce this human centric philosophy, leaving AI generated legal content in a state of unresolved legal ambiguity globally. This legal vacuum has direct consequences for academic law libraries, which face compounding difficulties in acquiring, licensing, cataloguing, and preserving AI generated legal materials.
The serious risks of AI hallucination, evidenced by hallucination rates as high as forty three percent in general purpose legal AI tools, and the growing incidence of fabricated case citations in Indian courts, further underscores the urgent need for robust content verification frameworks within law libraries. The constitution of the Ministry of Commerce and Industry expert panel in May 2025 and the ongoing Delhi High Court proceedings in ANI Media versus Open AI signal an institutional recognition that fundamental legislative reform is necessary. The copyright challenges posed by AI generated content are not peripheral concerns but constitute central questions for the future of legal information management and access to justice in India.
Addressing them demands coordinated legislative reform, institutional policy development, and professional capacity building to ensure law libraries continue fulfilling their foundational mission of providing reliable and equitable access to legal knowledge in the age of artificial intelligence.