Why in the news?

  • The Comptroller and Auditor General of India is planning to develop an AI model designed to help auditors, thereby improving efficiency in auditing processes.

AI in Governance

  • What is it?:
    • Artificial Intelligence (AI) in governance refers to the use of machine learning, natural language processing, computer vision, and data-driven models to enhance decision-making, service delivery, policy design, and citizen engagement.
  • Applications of AI in Governance:
    • Policy and Decision Support: AI tools facilitate faster and accurate decision making activities.
      • Big-data driven simulations for urban planning and climate adaptation.
      • AI-based weather forecasting models to improve disaster preparedness.
      • Predictive analytics for agriculture yields (e.g., AMED API by ISRO for crop forecasting).
    • Service Delivery and E-Governance:
      • Automated beneficiary verification in welfare schemes like PM-KISAN, MGNREGS.
      • AI chatbots for grievance redressal (e.g., UMANG app, RBI’s chatbot “AskRBI”).
      • AI-enabled language translation for multi-lingual governance (Bhashini under Digital India).
    • Healthcare:
      • AI-based disease surveillance systems (Integrated Health Information Platform).
      • Predictive models for maternal and child health (Reproductive & Child Health portal).
      • AI-powered telemedicine platforms in rural areas.
    • Law Enforcement & Judiciary:
      • AI-driven facial recognition in policing and border security.
      • Supreme Court’s AI tool “SUPACE” assists judges with legal research.
      • e-Courts Project uses AI for case management and translation of judgments.
    • Infrastructure & Smart Governance:
      • Smart cities mission: AI-based traffic control, waste management, and energy optimization.
      • Drones and AI for infrastructure monitoring and environmental compliance.
      • AI in logistics and supply chain management (supporting National Logistics Policy 2022).
  • Benefits:
    • Efficiency: Reduces human error, improves speed of service delivery.
    • Transparency: Automated workflows minimize discretion and corruption.
    • Inclusivity: Natural language processing tools enable access for non-English speakers.
    • Data-driven governance: Facilitates evidence-based policymaking.
  • Challenges:
    • Data privacy: Lack of a strong data protection law raises concerns.
    • Bias and discrimination: AI models risk perpetuating social inequalities.
    • Digital divide: Unequal access to technology can exclude vulnerable groups.
    • Accountability: Difficult to assign responsibility for AI-driven decisions.
    • Capacity gap: Shortage of skilled manpower in AI within government.
  • Way Forward:
    • Enact a comprehensive data protection and AI ethics law.
    • Develop Responsible AI frameworks ensuring fairness, accountability, transparency.
    • Invest in AI R&D, skilling, and indigenous compute infrastructure.
    • Encourage public-private partnerships for scalable solutions.
    • Promote AI-enabled citizen participation in governance.
  • Indian Initiatives:
    • IndiaAI Mission (2024)– ₹10,000+ crore outlay for AI startups, compute infrastructure, and governance applications.
    • National Strategy for AI (NITI Aayog, 2018)- “AI for All” vision.
    • Bhashini Mission– AI-based language translation for inclusive digital governance.
    • Responsible AI for Youth (2020)– building capacity among students.
    • AI in Digital Public Goods– e-RaktKosh, COWIN, DigiLocker integrations.
  • Global Examples:
    • Estonia: AI-based e-governance for tax filing, citizen services.
    • Singapore: AI in Smart Nation program (transport & urban planning).
    • UK: AI in predictive policing and NHS healthcare delivery.