S.R. Ranganathan, the pioneering Indian librarian and mathematician, is best known for his Five Laws of Library Science and the development of the Colon Classification system. His work emphasised organising knowledge for accessibility, relevance, and user-centricity. If he were alive today, his approach to generative AI would likely be shaped by his knowledge organisation principles, focus on serving users, and innovative mindset. While it’s impossible to know exactly what he would have done, we can make informed speculations based on his philosophy and contributions.

1. Applying the Five Laws to Generative AI
   Ranganathan’s Five Laws of Library Science (1931)—”Books are for use,” “Every reader his/her book,” “Every book its reader,” “Save the time of the reader,” and “The library is a growing organism“—could be adapted to generative AI systems, which are increasingly used to organize and generate knowledge. Here’s how he might have approached generative AI:
   – Books are for use: Ranganathan would likely advocate for generative AI to be designed with practical utility in mind, ensuring it serves real-world needs, such as answering queries, generating content, or solving problems efficiently. He might push for AI interfaces that are intuitive and accessible to all users, much like a library’s catalog.
   – Every reader his/her book: He would likely emphasise personalisation in AI systems, ensuring that generative AI delivers tailored responses to diverse users. For example, he might explore how AI could adapt outputs to different languages, cultural contexts, or knowledge levels, aligning with his goal of meeting individual user needs.
   – Every book its reader: Ranganathan might focus on making AI-generated content discoverable and relevant, developing classification systems or metadata frameworks to organise AI outputs so users can easily find what they need. He could propose taxonomies for AI-generated text, images, or code to enhance retrieval.
   – Save the time of the reader: He would likely prioritise efficiency, advocating for AI systems that provide accurate, concise, and relevant outputs quickly. He might critique models that produce verbose or irrelevant responses and push for prompt engineering techniques to streamline interactions.
   – The library is a growing organism: Ranganathan would recognise generative AI as a dynamic, evolving system. He might encourage continuous updates to AI models, integrating new data and user feedback to keep them relevant, much like a library evolves with new books and technologies.
2. Developing Classification Systems for AI Outputs
   Ranganathan’s Colon Classification system was a faceted, flexible approach to organising knowledge, allowing for complex relationships between subjects. He might apply this to generative AI by:
   – Creating a taxonomy for AI-generated content: He could develop a faceted classification system to categorize outputs like text, images, or code based on attributes such as topic, format, intent, or audience. For example, a generated article could be tagged with facets like “subject: science,” “tone: formal,” or “purpose: education.”
   – Improving information retrieval: Ranganathan might work on algorithms to enhance the discoverability of AI-generated content, ensuring users can navigate vast outputs efficiently. He could integrate his classification principles into AI search systems, making them more precise and context-aware.
   – Addressing ethical concerns: He would likely consider the ethical implications of AI-generated content, such as misinformation or bias, and propose frameworks to tag or filter outputs for reliability and fairness, aligning with his user-centric philosophy.
3. Advancing AI for Libraries and Knowledge Management
   As a librarian, Ranganathan would likely focus on how generative AI could enhance library services and knowledge management:
   – AI-powered library assistants: He might advocate for AI chatbots to assist patrons in finding resources, answering queries, or recommending materials, saving librarians’ time and improving user experience. For example, an AI could use natural language processing to interpret complex research queries and suggest relevant books or articles.
   – Automating cataloguing: Ranganathan could explore generative AI for automating metadata creation or cataloguing, using models to summarise texts, extract keywords, or classify resources according to his Colon Classification system. This would align with his goal of saving time and improving access.
   – Preserving cultural knowledge: Given his work in India, he might use AI to digitise and generate summaries of regional texts, manuscripts, or oral traditions, making them accessible globally while preserving cultural context.
4. Ethical and Social Considerations
   Ranganathan’s user-focused philosophy suggests he would be concerned with the ethical and societal impacts of generative AI, as noted in sources discussing AI’s risks like misinformation and job displacement. He might:
   – Promote equitable access: He would likely advocate for open-source AI models or affordable tools to ensure generative AI benefits diverse populations, not just affluent institutions or countries.
   – Address misinformation: Ranganathan might develop guidelines for libraries to educate users about AI-generated content, helping them distinguish reliable outputs from “hallucinations” or deepfakes.
   – Mitigate job displacement: While recognising AI’s potential to automate tasks, he might propose training programs for librarians to adapt to AI-driven workflows, ensuring human expertise remains central.
5. Innovating with Generative AI
   Ranganathan was an innovator, so he might experiment with generative AI to push boundaries in knowledge organisation:
   – AI for creative knowledge synthesis: He could use AI to generate new insights by synthesising existing literature, creating summaries or interdisciplinary connections that human researchers might overlook.
   – AI in education: Drawing from his focus on accessibility, he might develop AI tools to generate educational content tailored to different learning styles, supporting students and educators.
   – Collaborative AI systems: He might propose collaborative platforms where AI and librarians work together, with AI handling data-intensive tasks and humans providing critical judgment, aligning with his belief in human-centric systems.
6. Critiquing and Shaping AI Development
   Ranganathan’s analytical mindset suggests he would critically examine generative AI’s limitations, such as data dependence, bias, and lack of true creativity. He might:
   – Push for transparency: Advocate for clear documentation of AI training data and processes, ensuring users understand how outputs are generated.
   – Enhance AI explainability: Develop frameworks to make AI decisions more interpretable, helping users trust and verify generated content.
   – Focus on sustainability: Given the environmental impact of AI training, he might explore energy-efficient models or advocate for sustainable practices in AI development.
   

Conclusion
If S.R. Ranganathan were alive today, he would likely embrace generative AI as a tool to enhance knowledge organisation and accessibility while critically addressing its ethical and practical challenges. He would adapt his Five Laws to AI, develop classification systems for AI outputs, and leverage AI to improve library services and education. His focus would remain on serving users, ensuring equity, and advancing knowledge management in an AI-driven world. His innovative spirit and user-centric philosophy would make him a key figure in shaping generative AI’s role in libraries and beyond.