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Artificial Intelligence: Sovereign AI & Responsible Governance ​

UPSC Mains PYQs
  • AI Potential & Ethics (2020): "Artificial Intelligence has the potential to transform public service delivery, but also introduces profound ethical dilemmas." Discuss the applications of AI in Indian agriculture and healthcare. Analyze the ethical concerns regarding algorithmic bias and data privacy, and suggest a regulatory framework for 'Responsible AI'. (15 Marks, 250 Words)
📊 High-Yield Data & Statistical Fact Sheet
  • AI Operational Metrics:
    • IndiaAI Mission Outlay: Approved budget of ₹10,371.92 Crore (with ₹4,564 Crore dedicated for GPU compute capacity).
    • GPU Target: Targets deploying 10,000+ high-end GPUs via Public-Private Partnerships (PPP) to build a sovereign compute marketplace.
    • Market Potential: Domestic AI market projected to hit $17 Billion by 2027 (CAGR of 25-35%).
    • Sovereign Supercomputing: India's AI Supercomputer AIRAWAT (C-DAC, Pune) ranked 75th globally (peak performance of 13,170 Teraflops).
    • Economic Impact: NITI Aayog estimates AI could add $957 Billion to India's economy by 2035 (boosting growth rate by 1.3%).

IndiaAI Mission: GPU Capacity Expansion

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India AI Market Projections (US$ Billion)

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1. INDIAAI MISSION & SOVEREIGN COMPUTE ​

  • Sovereign AI Infrastructure: Procuring a national public-sector GPU cloud (10,000+ GPUs) to provide subsidized compute power to startups, MSMEs, and academic institutions, breaking the monopoly of foreign hyperscalers.
  • Multilingual LLM Development: Funding localized models (e.g., Hanooman and Bhashini AI) trained on Indian regional data to enable real-time vernacular translation and governance access.
  • IndiaAI Innovation Centre: Establishing national research labs to build indigenous AI models, developing safe datasets, and setting up the AI Safety Institute (AISI) to audit high-risk commercial AI releases.

2. SECTORAL APPLICATIONS: HEALTH, AGRI & GOVERNANCE ​

  • Agriculture (Precision Farming): Deploying AI-powered drones to analyze soil health, optimize fertilizer/pesticide dosage, and utilizing predictive modeling to forecast pest infestations and crop yields.
  • Healthcare (Diagnostics): AI-enabled screening for tuberculosis and diabetic retinopathy in remote rural clinics, drastically reducing diagnostic turnaround times. Accelerates genomic research and AI-led drug discovery.
  • Predictive Governance: Deploying AI for early flood alerts, smart city traffic management, and evaluating creditworthiness for unbanked populations using alternative digital footprints.

3. REGULATORY ISSUES, ETHICS & BIAS ​

  • Algorithmic Bias: Machine learning models trained on skewed historical datasets can reinforce gender, caste, or religious biases in automated recruitment or credit systems.
  • Deepfakes & Disinformation: High-fidelity AI-generated media threatens democratic election integrity and social stability. Addressed via mandatory AI-watermarking and platform traceability regulations.
  • Explainable AI (XAI): Transitioning from "black-box" models to interpretable algorithms in public administration, ensuring automated decisions are auditable, fair, and legally explainable.

QUICK REVISION BOX ​

  • IndiaAI Mission Nodal Agency: MeitY.
  • IndiaAI Financial Outlay: ₹10,371.92 Crore.
  • National Compute Target: 10,000+ GPUs.
  • Sovereign AI Supercomputer: AIRAWAT (C-DAC, Pune).
  • Vernacular Translation Initiative: Bhashini AI.
  • Auditing Nodal Body: AI Safety Institute (AISI).
  • Audit Requirement for Public AI: Explainable AI (XAI).

Notes updated up to March 2026. Sources: NITI Aayog Responsible AI Papers, MeitY Gazettes.

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