Advancing Indigenous Foundation Models: India’s Roadmap for AI Development

The Office of the Principal Scientific Adviser to the Indian government has released a white paper titled “Advancing Indigenous Foundation Models”.

• It outlined a roadmap to develop India’s own artificial intelligence models and strengthen the country’s position in the global AI ecosystem.
• The government is actively encouraging the development of indigenous AI systems through collaboration between public institutions and private companies.

Key Highlights of White Paper

• Priority on Indigenous AI Development: It highlights the development of indigenous foundation models as a key priority to ensure inclusive growth, public welfare, and alignment with India’s legal framework, values, and security interests.
• India-Specific Foundation Models: According to the white paper, India plans to develop its own foundation models trained on datasets relevant to the country. 
  • This approach is expected to improve transparency, inclusivity, and alignment with national priorities while strengthening India’s role in the global AI landscape.
• Focus on Diverse AI Models: It also emphasises the importance of both large language models (LLMs) and small language models (SLMs). 
  • While LLMs can perform broad tasks across sectors, SLMs are specialised models designed for specific domains and are generally more cost-effective to operate.
• Applications: 
  •  In India, such models can be used in areas like agriculture, healthcare, education, and micro, small and medium enterprises.
  • The combination of LLMs, SLMs, and multimodal AI models is expected to promote linguistic inclusion, affordability, and energy efficiency, while enabling innovation in sectors such as climate, health, education, and urban governance.

About Foundation Models

• Foundation models are large AI systems trained on massive datasets such as text, images, audio, and video.
  • For Example: Models like GPT-4 and BERT are widely used foundation models.
• Working Principle: These models can perform a wide range of tasks, including translation, summarisation, question answering, and text classification, and are considered an important layer in modern AI development.

Need of Indigenous AI Models

• Currently, many AI models used in India are developed overseas and trained on datasets that may not fully represent the country’s diversity. 
  • To address this gap, the government is prioritising local AI development as part of its digital infrastructure strategy.

Challenges

• High Computational and Infrastructure Requirements:  Training foundation models require massive computing power, advanced GPUs/TPUs, and large data centres, which makes development expensive and resource-intensive. 
  • For  Example: Companies like OpenAI and Google invest billions of dollars in computing infrastructure to train large models such as GPT-4.
• Data Availability and Quality Issues: Foundation models require large, diverse, and high-quality datasets. In many countries, including India, structured datasets in regional languages and specialised domains are limited, which can affect model accuracy and inclusivity.
  • For  Example: Many Indian languages have far less digital content compared to English, creating challenges for AI systems serving multilingual populations.
• Bias and Ethical Concerns: AI models may inherit biases present in training datasets, which can lead to discriminatory or unfair outcomes. This can affect decision-making in sensitive areas like hiring, credit scoring, or healthcare.
• • For Example: AI-based hiring tools have previously shown bias when trained on historically skewed employment data.
• Data Privacy and Security Risks: Training large models involves processing massive amounts of personal and public data, raising concerns about data privacy, misuse, and surveillance. Strong regulatory safeguards are required to protect user data.
• • For  Example: Concerns about data protection are addressed in frameworks like the Digital Personal Data Protection Act, 2023.

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Way Forward

• Build Multilingual and Domain-Specific Datasets: Developing datasets for regional languages, governance data, healthcare, agriculture, and climate will improve AI inclusivity.
• Promote Smaller and Efficient AI Models: Encouraging small language models (SLMs) and energy-efficient architectures can reduce costs and environmental impact.
• Strengthen Ethical and Regulatory Frameworks: Governments should adopt transparent AI governance frameworks addressing bias, accountability, and responsible AI use.

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