AI in India: strategy must precede mission, Pg7

Context:

•  India has positioned itself as a global AI leader, especially for the Global South. However, a democratically grounded national strategy is urgently needed to prevent AI governance from becoming technocratic and opaque.

Key Highlights:

• India is executing the IndiaAI Mission without a comprehensive national AI strategy.
• AI is impacting defence, employment, infrastructure, and data ecosystems, yet lacks democratic oversight.
• Job displacement risk: ~65,000 jobs lost in 2024 from top IT firms; IMF: 26% workforce exposed, 12% at risk.
• Energy & water stress: AI/data centres threaten resource sustainability in cities like Bengaluru and Hyderabad.
• Current AI governance risks being technocratic, opaque, and legitimacy-deficient.
• Without internal coherence, India’s global AI leadership claims lack credibility.

Critical Issues:

• Lack of Strategic Autonomy:
  India risks becoming dependent on foreign AI technologies in key areas like defence, intelligence, and financial infrastructure.
• Opaque Data Governance:
  Absence of transparent frameworks may lead to corporate monopolies and erode public trust.
• Neglected Labour Impact:
  No national AI framework currently addresses employment transitions, reskilling, or social protections.

Way Forward:

• Publish a Cabinet-approved national AI strategy and table it in Parliament.
• Create a Standing Parliamentary Committee on AI and Emerging Technologies for democratic oversight.
• Conduct a national employment impact study to assess AI-driven displacement across sectors and regions.
• Ensure inter-sectoral consultations involving industry, labour, education, and civil society.
• Anchor AI governance in public values, social justice, and sustainability.

Key Concepts Involved:

• AI Governance: Frameworks to ensure responsible development, deployment, and accountability of AI systems.
• Technological Sovereignty: National control over core AI infrastructure, data, and models.
• Generative AI: is a subfield of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.
• AI and Energy Use: Training AI models requires massive data processing, contributing to climate and resource challenges.