Research on Intellectual Property System Management of Digital Libraries in the Era of Artificial Intelligence
Author: USA IP Research Team Published date: 01/17/2026
Abstract
This paper is based on the background of artificial intelligence (AI) technology and, from the perspective of intellectual property system management in digital libraries, systematically analyzes the new challenges faced by digital libraries in intellectual property management in the AI era. At the theoretical level, drawing on intellectual property protection theory, digital information resource management theory, and artificial intelligence governance theory, the management framework of the digital library intellectual property system is examined. At the practical level, the paper summarizes the main problems in copyright management, data sharing, and AI applications in current digital libraries. Based on this, it proposes the construction of an intellectual property system management framework for digital libraries adapted to the AI era, including improving copyright licensing mechanisms, building AI data governance systems, establishing reasonable use mechanisms for digital resources, and promoting coordinated governance between technology and institutions.
Keywords: artificial intelligence; digital library; intellectual property management; copyright protection; institutional innovation
Chapter 1 Introduction
With the entry into the artificial intelligence era, the development of digital libraries has further exhibited significant characteristics of intelligence and data-driven operation. AI technologies are widely applied in multiple aspects such as automatic document classification, natural language processing, semantic retrieval, knowledge graph construction, and personalized recommendation. These applications not only significantly improve the efficiency of information organization and knowledge services but also promote the transformation of digital libraries from “information storage centers” to “knowledge service platforms.” In this process, the importance of data resources continues to increase and has gradually become the core element supporting the operation and optimization of AI systems. The massive high-quality document data accumulated by digital libraries provides an important foundation for AI model training and knowledge mining, but it also brings more complex intellectual property risks.
Specifically, the introduction of AI technology has led to new developments in intellectual property issues in digital libraries. First, in the process of AI model training, large-scale use of digital literature data has become common practice. However, such data often involves different copyright holders and usage permissions. Without clear authorization or standardized mechanisms, infringement disputes are likely to arise. Second, the rapid development of AI-generated content (AIGC) challenges traditional copyright systems. Due to the non-human nature of AIGC creation, there are significant theoretical and practical disagreements regarding whether such works qualify for copyright protection and who should own the rights. This also creates new institutional difficulties for digital libraries in resource collection, organization, and dissemination. Third, digital libraries often engage in resource sharing and cooperative services involving multiple stakeholders such as publishers, database providers, research institutions, and users. Complex interest games among these parties further increase the difficulty of intellectual property management.
Against this background, research on “intellectual property system management of digital libraries in the AI era” has important theoretical and practical significance. Theoretically, it helps enrich research in the intersection of digital intellectual property and AI governance and promotes the innovative development of traditional intellectual property theory in new technological environments. Practically, it provides feasible institutional design pathways for resource management, data utilization, and intelligent services in digital libraries, thereby achieving a dynamic balance between knowledge sharing and rights protection.
Based on this understanding, this paper takes AI technology as the research background and focuses on the development practice of digital libraries to systematically analyze the main problems and challenges in intellectual property system management. On this basis, combining intellectual property protection theory, digital resource management theory, and AI governance concepts, the paper organizes the operational mechanism of the digital library intellectual property system and explores pathways for integrating institutional optimization with technological governance. The aim is to construct an intellectual property management framework for digital libraries that meets the development needs of the AI era and provides references for policy-making and practical exploration.
Chapter 2 Literature Review
With the rapid development of artificial intelligence technology, research on digital libraries has gradually shifted from traditional information resource management to a comprehensive governance model centered on data-driven and intelligent services. In this context, studies on “intellectual property system management of digital libraries in the AI era” have been conducted from multiple perspectives, including digital library development, evolution of intellectual property systems, legal issues of artificial intelligence, and technological governance pathways.
First, in terms of digital library development and intellectual property issues, early research mainly focused on the relationship between digital resource construction and copyright protection. Scholars generally believe that the essence of digital libraries lies in efficient dissemination of knowledge resources through information technology. However, the low-cost nature of digital reproduction and online distribution significantly increases the risk of copyright infringement. Therefore, how to balance knowledge dissemination and protection of rights holders’ interests has become an important issue. Relevant studies point out that digital rights management (DRM) technologies and licensing mechanisms have alleviated copyright risks to some extent, but they also restrict users’ legitimate fair use rights.
Second, in the evolution of intellectual property systems, scholars have analyzed copyright protection in digital environments from a legal perspective. Some studies emphasize improving copyright law, neighboring rights systems, and database protection systems to adapt to digital communication features. Others focus on adjustments to fair use systems, arguing that the scope of fair use should be moderately expanded in education and research fields to support knowledge innovation and academic development. In addition, some scholars propose a multi-stakeholder governance system based on interest balance theory to coordinate the interests of authors, publishers, and public cultural institutions.
Third, with the widespread application of AI technology, research has gradually focused on the impact of AI on intellectual property systems. Regarding AI training data usage, some scholars argue that data, as a fundamental resource for AI development, should be used under legal authorization or fair use frameworks; otherwise, it constitutes infringement. Others propose establishing a “data use exception” to promote technological innovation. In terms of AI-generated content (AIGC), there is significant controversy: one view holds that AI-generated content lacks human creativity and should not be protected by copyright law; another view argues that rights should be granted to developers or users to incentivize innovation. These studies provide important theoretical references for digital libraries in handling AIGC.
In terms of collaborative governance, scholars generally believe that a single actor cannot effectively address complex intellectual property issues, and a multi-party governance mechanism involving government, libraries, publishers, and users should be established. Some studies emphasize the combination of policy guidance and industry self-regulation, while others highlight international cooperation, noting that coordination of intellectual property systems is increasingly important in the context of cross-border data flows.
Overall, existing research has achieved the following: (1) identifying the core contradiction between knowledge sharing and copyright protection; (2) proposing various legal and institutional optimization paths; and (3) exploring preliminary governance models under AI conditions. However, there are still limitations: (1) lack of systematic institutional design under deep AI–digital library integration; (2) gap between theory and practice with limited operational frameworks; and (3) no consensus on AIGC and data copyright issues.
Therefore, it is necessary to further integrate intellectual property theory, data governance theory, and AI governance theory to construct an intellectual property system management framework for digital libraries suitable for the AI era, promoting coordinated development of institutional innovation and technological application.
Chapter 3 Research Findings
Through systematic analysis of intellectual property system management in digital libraries in the AI era, combining theoretical discussion and practical investigation, this paper presents the following findings:
First, AI technology has significantly changed the structural form of intellectual property issues in digital libraries. The traditional linear relationship of “work–author–user” has gradually evolved into a complex system involving “data–algorithm–platform–user.” In this system, data is not only a carrier of knowledge but also a production factor and core asset. The scope of intellectual property objects is continuously expanding. Therefore, traditional copyright systems are increasingly inadequate for defining rights boundaries in data-driven and algorithm-generated content environments, revealing institutional lag.
Second, the use of AI training data has become a key source of intellectual property risks in digital libraries. Large-scale corpus data used in model training has characteristics of “high dependency” and “low visibility,” meaning AI systems heavily rely on massive datasets, while data sources and usage paths are often opaque. This makes unauthorized use of digital resources difficult to detect and regulate in time, increasing infringement risks. Therefore, regulating data usage has become a core issue in intellectual property management.
Third, AI-generated content (AIGC) has a fundamental and long-term impact on copyright systems. Under existing legal frameworks, AIGC faces institutional gaps in authorship attribution, rights ownership, and protection scope. AI-generated works lack traditional human authors, challenging the logic of copyright law based on human creativity. As AIGC becomes more widely used in digital libraries, unclear rights definitions may affect legal use and dissemination order of knowledge resources.
Fourth, there is a structural tension between knowledge sharing and intellectual property protection in digital libraries. The core function of digital libraries is to promote knowledge dissemination and public service, while intellectual property systems emphasize protection of rights holders. AI enhances dissemination efficiency but also increases copyright control difficulty, intensifying the conflict between expanded sharing needs and strengthened protection requirements. This is particularly evident in educational and research contexts.
Fifth, cross-platform and cross-institutional data sharing increases management complexity. In digital library alliances and cooperation platforms, lack of unified standards for copyright ownership, usage rights, and liability allocation easily leads to conflicts and legal risks. Differences in national intellectual property systems further increase uncertainty in cross-border data sharing.
Sixth, technological tools play an increasingly important role in intellectual property management, but their application remains insufficient. Technologies such as DRM, blockchain evidence storage, and intelligent copyright recognition can improve efficiency in theory, but face high costs, lack of standards, and immature development in practice. Therefore, technology alone cannot solve the problem and must work in synergy with institutional frameworks.
In summary, intellectual property issues in digital libraries in the AI era are becoming more complex, diversified, and dynamic. Traditional systems face significant challenges. Only through institutional improvement, technological support, and collaborative governance can these challenges be effectively addressed.
Chapter 4 Conclusion and Recommendations
This study shows that intellectual property management in digital libraries in the AI era is highly complex and dynamic. On the one hand, the widespread use of data in AI training highlights data as an asset, but its ownership and usage boundaries remain unclear. On the other hand, AI involvement in knowledge production challenges the traditional copyright system centered on human authorship. Meanwhile, the tension between knowledge sharing and rights protection is further amplified. In addition, multi-stakeholder data sharing increases spillover risks and liability ambiguity.
Based on this analysis, it can be concluded that neither reliance on traditional systems nor purely technological approaches can effectively address these issues. A coordinated path of “institutional innovation + technological governance” is necessary to build an open, standardized, and secure intellectual property management system that balances knowledge sharing and rights protection.
The following recommendations are proposed:
First, improve digital resource copyright authorization and management mechanisms. A unified digital copyright management platform should be established to integrate registration, authorization, tracking, and revenue distribution. Flexible licensing models such as tiered licensing, on-demand licensing, and open licensing should be explored.
Second, establish a systematic AI data governance framework. A legality review mechanism for training data should be implemented, standardizing data collection, annotation, storage, and usage processes. Data usage logs and traceability systems should be built to ensure compliance and algorithm transparency.
Third, clarify ownership rules for AI-generated content. Legal frameworks should be accelerated to define AIGC’s legal status and clarify rights and responsibilities among developers, users, and platforms. Limited protection or neighboring rights models may be explored.
Fourth, optimize fair use systems to adapt to AI development. While protecting rights holders’ interests, the scope of fair use should be moderately expanded, especially in education, research, and public cultural services, to support AI training and knowledge services.
Fifth, strengthen technological applications in intellectual property protection. Technologies such as blockchain, digital watermarking, content recognition, and AI monitoring should be adopted to enable dynamic supervision and intelligent infringement detection.
In conclusion, the AI era brings both opportunities and challenges to digital libraries. Institutional innovation should be combined with technological empowerment to build a robust intellectual property system that supports both knowledge sharing and rights protection, thereby promoting high-quality and sustainable development of digital libraries. This will also contribute to modernization of public cultural services and development of the digital economy and knowledge society.
References
[1] Wu Handong. Intellectual Property Law. Beijing: Law Press, 2021.
[2] Zheng Chengsi. On Intellectual Property Rights. Beijing: Law Press, 2019.
[3] Li Xiaoming. Introduction to Digital Libraries. Beijing: Higher Education Press, 2020.
[4] Zhou Guangquan. “Research on Artificial Intelligence and Legal Regulation.” Legal Studies, 2022(3): 15–28.