Algorithmic Sovereignty Should Be India’s Strategic Priority

Context

Most leading AI models are developed and trained in the West, primarily by companies such as OpenAI, Google, Microsoft, and Anthropic, using datasets heavily dominated by Western scholarship and institutional viewpoints which has raised concerns about algorithmic sovereignty.

Structural Bias in Global AI Systems

• Western-Centric Training Data: Most AI models are trained on datasets dominated by Western academic publications; it often prefers Western interpretations of global issues, including geopolitics, law, and governance.
  • g. Debates surrounding the UNCLOS illustrate this bias. Western powers often interpret provisions on maritime activities in Exclusive Economic Zone (EEZ) expansively, permitting military operations and intelligence gathering.
  • In contrast, countries such as India, China, Brazil, Indonesia, and South Africa interpret these provisions more restrictively, emphasising the need for coastal state consent for military activities.
• If AI models rely primarily on Western legal scholarship it marginalises perspectives from the Global South.

Why Algorithmic Sovereignty Matters for India

• Safeguarding Strategic Narratives: AI systems increasingly influence policy debates and public understanding of global issues. Thus India risks having its strategic narratives shaped externally.
• Preventing Digital Dependence: Reliance on foreign AI ecosystems could lead to structural technological dependence, where key components—models, chips, and cloud infrastructure—are controlled by external actors.
• Protecting Cultural and Linguistic Diversity:. AI systems trained primarily on English or Western datasets may inadequately represent Indian languages, legal traditions, and cultural contexts.
• Securing Data Sovereignty: Without domestic control over data infrastructure, Indian data could be processed and monetised abroad, raising concerns over privacy, economic value, and national security.
  • It may lead to a form of digital colonialism, where foreign algorithms determine how information is processed, analysed, and disseminated.
• Limited Innovation Autonomy: If foundational AI models remain foreign-controlled, domestic innovators may only build applications on top of externally controlled platforms, restricting technological sovereignty.
• Strategic Vulnerability: Access to critical AI technologies could be restricted due to geopolitical tensions, export controls, or technological sanctions.

Strategic Dilemmas for India

Adopting a foreign AI stack could provide rapid technological capabilities but may limit long-term autonomy. For example:

• The S. AI ecosystem offers advanced chips, cloud services, and models but remains controlled by foreign corporations.
• The Chinese AI ecosystem raises concerns related to data security, governance models, and geopolitical alignment.

This creates pressure on India to develop its own AI capabilities.

Building a Sovereign AI Ecosystem

• Developing Indigenous AI Models: India must invest in building large-scale AI models trained on Indian datasets, incorporating diverse linguistic and cultural contexts.
• Expanding Domestic Computing Infrastructure: AI development requires massive computing power. Establishing national high-performance computing facilities and semiconductor ecosystems is essential.
• Creating Indian Data Ecosystems: Government initiatives should encourage the creation of open, high-quality datasets reflecting India’s legal, linguistic, and socio-economic realities.
• Strengthening AI Research and Talent: Investing in research institutions, universities, and startups will help build domestic expertise in AI technologies.
• Strengthening Digital Public Infrastructure: Integrating AI capabilities with India’s digital platforms—such as Aadhaar and digital payment systems—can accelerate AI adoption across sectors.
• Public Investment in AI Infrastructure: Large-scale funding is required for computing infrastructure, semiconductor manufacturing, and AI research.

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