Preparing for Tomorrow: A Roadmap for AI, ML, and Data Science in the National Library of Malaysia

Introduction
The integration of data science, machine learning (ML), and artificial intelligence (AI) into library services presents an opportunity to redefine user experiences, optimise resource management, and bring enhanced relevance to libraries in a digital-first world. For the National Library of Malaysia (NLM), these technologies are critical in delivering data-driven, personalised, and efficient services. As NLM undertakes this journey, planning for AI, ML, and data science becomes paramount to ensure these advancements align with NLM’s mission while adhering to ethical standards around privacy, fairness, and transparency. This article provides a roadmap for responsibly incorporating these technologies, envisioning NLM’s future as a leader in digital library services.

Data Science: Unlocking Insights from Library Data
Data science uses advanced techniques to extract actionable insights from vast data sources, making it invaluable for NLM. With rich data from user demographics, borrowing histories, and digital resource interactions, NLM has a unique opportunity to gain a deeper understanding of user preferences and trends, directly informing collection management and service optimisation.

For instance, by analysing circulation data, NLM can identify materials in high demand and forecast future needs, enabling more efficient resource allocation and improved user experience. Similarly, data science can support the analysis of digital resource usage to identify peak times, allowing NLM to optimise digital services and allocate resources where they are most needed. However, as NLM processes extensive user data, transparency and user privacy are essential. NLM is committed to using data responsibly, ensuring users are informed about data collection practices and data use.

Machine Learning: Enhancing Personalization and Efficiency
Machine learning (ML), a subset of AI, enables systems to learn and improve over time based on data. In NLM’s roadmap, ML can support critical applications such as personalised recommendations, automated cataloguing, and enhanced search functionality. By analysing borrowing patterns and user interests, ML can power recommendation systems that offer users suggestions tailored to their unique preferences, transforming their library experience into a more engaging journey of discovery.

Automated cataloguing is another key area where ML can reduce manual work, allowing NLM staff to focus on more complex tasks. For instance, ML algorithms can support metadata generation and book classification, helping NLM staff maintain an organised and accessible collection with less effort. However, the potential for bias in ML models requires careful attention. Therefore, ensuring that algorithms are trained on diverse and representative data is critical to avoid unintended biases and deliver fair, equitable services for all users.

Artificial Intelligence: Building the Future of User Interaction
Artificial intelligence has the power to automate processes and transform the way NLM interacts with its users. AI-driven tools such as virtual assistants and chatbots can handle common inquiries, helping users find materials, access databases, and address basic research questions. These assistants provide immediate, round-the-clock support, bridging the gap when human staff are unavailable and making library resources accessible at any time.

Natural language processing (NLP), a key AI technology, enables these virtual assistants to interpret and respond to users’ questions accurately. NLP-based search functions also improve catalogue interaction, delivering more relevant search results by understanding user intent rather than relying solely on keywords. As NLM deploys these tools, the library is dedicated to ensuring transparency, allowing users to understand how these tools work and safeguarding their privacy through clear data usage policies.

A Roadmap for Implementation: Steps to Integrate AI, ML, and Data Science at NLM
NLM’s strategic integration of these technologies involves a structured roadmap to ensure a smooth and responsible transition. Below is a detailed plan to guide NLM’s journey in adopting AI, ML, and data science.

1. Assess Library Needs and Set Objectives
   The first step in planning is to assess NLM’s needs and align technology objectives with the library’s mission. Identifying areas with the highest potential for AI, ML, and data science (such as enhancing cataloguing efficiency, developing recommendation systems, and improving resource management) ensures that technology adoption directly benefits library users and fulfils NLM’s broader goals.

2. Choose Appropriate Technologies and Tools
   With clear objectives in mind, NLM will select the best tools for each application. Options may include open-source machine learning libraries like TensorFlow and Scikit-learn or cloud-based AI services such as Google Cloud AI or AWS AI. Choosing the right tools is crucial, as it influences NLM’s ability to implement solutions efficiently and with minimal overhead.

3. Develop an Ethical Framework
   As NLM moves forward with implementing AI and ML, establishing an ethical framework is essential to address potential concerns around privacy, algorithmic bias, and user consent. This framework will guide responsible data collection, ensuring users’ rights are respected, and reinforcing NLM’s commitment to ethical standards. Key components of this framework include anonymising data where possible, obtaining informed user consent, and providing transparency around data use.

4. Ensure Diversity in Training Data
   NLM is committed to creating inclusive services for its diverse user base, which means training ML models on representative data. Regular audits will identify and mitigate any potential biases in algorithms, ensuring all users benefit from AI-powered services. This approach helps NLM provide equitable access to library resources, honouring Malaysia’s cultural and linguistic diversity.

5. Invest in Staff Training
   Equipping NLM staff with the skills to manage and maintain AI, ML, and data science tools is essential for success. Training programs focused on data analytics, AI ethics, and practical applications will enable staff to oversee these technologies effectively. Partnerships with educational institutions and tech companies can further support this goal, giving NLM access to technical expertise and additional resources.

6. Monitor and Evaluate Progress
   Once implemented, regular monitoring and evaluation are necessary to gauge the effectiveness of these technologies. NLM will track key metrics such as user satisfaction, recommendation accuracy, and service efficiency to assess progress. Collecting feedback from users through surveys and focus groups will also ensure that services meet users’ expectations and highlight areas for improvement.

Future Perspectives: Envisioning the Long-Term Impact
Looking ahead, NLM aspires to lead in digital library innovation by continually refining its AI, ML, and data science capabilities. Future initiatives may include developing adaptive learning resources for various age groups, implementing real-time sentiment analysis to assess user satisfaction, and expanding accessibility through advanced translation and voice recognition services. Collaborations with other national and international libraries may also foster shared knowledge and resources, benefiting the global library community.

Through this strategic approach, NLM aims to create a forward-thinking library that meets the changing needs of users, adapting to digital advancements while upholding ethical standards. As NLM progresses, it will not only serve as a knowledge repository but as a model of innovation and responsible technology use in the library sector.

Conclusion
By following this roadmap, the National Library of Malaysia is well-prepared to leverage data science, ML, and AI in a manner that enriches user experience, enhances operational efficiency, and maintains the highest ethical standards. This plan positions NLM as a leader in the digital library landscape, ready to meet future challenges and deliver unparalleled value to its users.

Contributed by Wan Azuan Shah bin Wan Mohamed, Deputy Director of the Digital Project Management Office,National Library of Malaysia

References
Das, R. K., & Islam, M. S. U. (2021, August 1). Application of artificial intelligence and machine learning in libraries: A systematic review. Library Philosophy and Practice (e-journal). University of Nebraska – Lincoln. https://shorturl.at/ChvGC

Hodonu-Wusu, J.O. (2024). The rise of artificial intelligence in libraries: the ethical and equitable methodologies, and prospects for empowering library users. AI Ethics. https://doi.org/10.1007/s43681-024-00432-7

Preza Díaz, J.-L. (2017). Data science and analytics in libraries. Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare, 70(2), 244. https://doi.org/10.31263/voebm.v70i2.1796