The integration of Artificial Intelligence (AI) into governance systems marks a transformative era in public administration.

About Artificial Intelligence (AI)

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• Definition: Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are designed to think, learn, and solve problems autonomously. 
• Coined by: The term “Artificial Intelligence” was coined by American computer scientist John McCarthy in 1956. 
• Types of Artificial Intelligence: Artificial Intelligence (AI) can be categorised:
  • Based on Capability
    • Narrow AI (Weak AI): Designed for specific tasks (e.g.,Siri, Google Translate).
    • General AI (Strong AI):Theoretical AI that can perform any intellectual task like a human.
  • Based on Functionality
    • Reactive Machines: No memory, focuses on specific tasks (e.g., Deep Blue chess program).
    • Limited Memory: Can handle complex classification tasks, Uses past data for decisions (e.g., self-driving cars).
    • Theory of Mind: Understands emotions and interactions (under development).
    • Self-Aware AI: Hypothetical AI with consciousness and self-awareness, considered as a long shot goal.

India’s Transformation in Governance Due to Artificial Intelligence (AI)

• Over the last decade, India has emerged as the fifth-largest economy in the world, with the government budget tripling in size.
• Technology-driven governance has replaced inefficiencies with citizen-centric, digital public infrastructure (DPI).

Key Applications of AI in Governance (“GovAI”)

Artificial Intelligence can redefine governance by enabling:

• Data-Driven Decision-Making: Leveraging big data analytics for informed policymaking, resource allocation, and identifying societal needs.
  • Example: Aadhaar-Linked Welfare Programs in India uses big data analytics to ensure targeted delivery of subsidies and benefits, reducing leakages and duplication.
• Automation of Processes: Reducing manual effort and human error in public service delivery through automation.
  • Example: Passport Seva Kendras in India automates appointment scheduling, improving efficiency.
    • GSTN (Goods and Services Tax Network) uses AI to analyse transaction data, detect fraud, and optimise tax collection.
• Personalized Citizen Services: AI-powered chatbots and platforms providing tailored services to diverse populations.
  • Example: MyGov Portal has AI-powered features personalising citizen engagement and suggestions for public schemes.
• Predictive Analytics: Anticipating challenges like natural disasters, disease outbreaks, or economic trends.
  • Example: Indian Meteorological Department (IMD) uses AI to predict cyclones and improve disaster preparedness.
• Improved Monitoring and Evaluation: Real-time assessment of policy implementation and feedback mechanisms.
  • Example: Pradhan Mantri Awas Yojana (PMAY) features real-time dashboards to monitor housing scheme progress and allocate resources efficiently.
• Policy and Scheme Performance: Enhancing the design and effectiveness of social security programs. Example: AI-powered platforms like Aarogya Setu and UMANG in India.
• Economic Sectors: Improving livelihoods in agriculture, fisheries, and animal husbandry.
• Language Translation: Bridging linguistic barriers for better service delivery.
  • Example: eSanjeevani’s app provides a multilingual interface to enable assisted teleconsultations (doctor to doctor) in Ayushman Bharat Health & Wellness Centres (AB-HWCs) across the country.

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Three Drivers of GovAI In India

• Rapid Digitalisation
  • India is set to become the most connected and digitised country, with internet users projected to rise from 90 crore to 120 crore by 2026.
  • Government-led digital initiatives have catalysed over 1,00,000 startups, especially in the fintech space.
  • GovAI can further enhance the IndiaAI ecosystem, encouraging innovation in AI-based models, products, and platforms.
• Data as the Foundation of AI
  • India has become one of the largest data repositories due to digitalisation and DPI initiatives.
  • Governments, as custodians of vast personal and non-personal data, can leverage the data to train high-quality AI models.
    • The India Datasets Programme will provide the foundation for government-led AI models while adhering to personal data protection laws.
    • Small language models (SLMs) interacting with large language models (LLMs) can create a unique ecosystem for governance.
• Efficiency as a Political Goal
  • Post-COVID, governments globally are striving for efficiency with limited resources.
  • India’s digital government transformation has showcased how DPI and AI can extend the impact of public spending, reimagining governance to achieve ‘maximum governance with minimal resources’.

Challenges in Implementing Artificial Intelligence (AI) in Governance

• Data Privacy and Security: AI systems require large datasets, including personal data, to function effectively. This raises concerns about privacy, data protection, and surveillance.
  • Example: Aadhaar data breaches highlight risks of unauthorised access to citizen data.
• Digital Divide: There is a gap in access to digital services, especially in marginalised communities, which limits the reach of AI-driven governance.
  • Example: Rural areas face limited access to AI-driven government services due to infrastructure gaps.
• Skill Gap: A lack of technical expertise among government personnel hinders the effective deployment and management of AI systems.
  • Example: Government employees may not have the necessary training to manage AI technologies or interpret their outputs.
• Cost of Implementation: Developing, maintaining, and scaling AI systems is expensive, which can be a barrier for governments with limited budgets.
  • Example: The Smart Cities Mission involves high costs for building and sustaining AI-driven infrastructure.

Ethical Issues in Implementing AI in Governance

• Bias and Fairness: AI models are trained on data that may reflect societal biases, and if not properly managed, these systems can perpetuate or worsen existing inequalities.
  • Example: AI recruitment tools and facial recognition systems may perpetuate discrimination due to inherent biases in training data.
• Concerns of Violations of Civil Liberties: AI systems, particularly in surveillance, may infringe upon personal freedoms if used without proper oversight, leading to violations of privacy and civil rights.
  • Example: Mass surveillance by governments or corporations could erode civil liberties and infringe on citizens’ rights.
• Accountability and Transparency: Many AI systems operate as “black boxes,” making it difficult to understand how decisions are made. This lack of transparency raises concerns about accountability, particularly in high-stakes applications.
  • Example: In autonomous vehicles, accidents caused by AI decision-making raise questions about who is responsible: the manufacturer, the developer, or the AI itself.
• Job Displacement: Automation powered by AI has the potential to displace workers across sectors, leading to economic inequality and social instability.
  • Example: AI-driven automation in manufacturing or service industries could lead to significant job loss, worsening unemployment rates.
• Informed Consent:  Citizens may not fully understand how their data is being used by AI systems, nor the implications of AI decisions in governance. 

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Government Initiatives for Integrating AI in Governance

• Global Partnership on Artificial Intelligence (GPAI): In 2020, India joined with 15 other countries to form the GPAI to establish frameworks for the responsible utilisation of emerging technologies.
  • India is the current Chair of GPAI (Global Partnership of AI).

• Declaration on Digital Public Infrastructure, AI and Data for Governance: It is a Joint Communiqué by the G20 Troika (India, Brazil and South Africa), endorsed by several G20 countries, guest countries and international organisations.
• IndiaAI Mission: The Indian government has allocated Rs 10,372 crore for the next five years  under which the government will allocate funds towards subsidising private companies looking to set up AI computing capacity in the country.
• ‘AI for All’: It is a self-learning online program designed to raise public awareness about Artificial Intelligence. 
• IndiaAI Innovation Centre: The IndiaAI Innovation Centre will undertake the development and deployment of indigenous Large Multimodal Models (LMMs) and domain-specific foundational models in critical sectors.
• Smart Cities Mission: Under the Smart Cities Mission, AI is being used to enhance urban governance through smart traffic management, waste management systems, and surveillance to improve quality of life.

Way Forward

• Strengthening Data Infrastructure and Security: Governments must prioritise building robust data infrastructures that securely store and manage personal and non-personal data.
  • Ensuring data privacy and security through strong encryption, access controls, and compliance with global data protection laws will build public trust in AI-driven governance.
• Reskilling and Upskilling the Workforce: To thrive in the AI-driven era, it is essential to focus on reskilling and upskilling the workforce. 
  • Programs like MeitY’s FutureSkills PRIME, can equip individuals with the skills necessary for the AI economy.
  • The Central Board of Secondary Education (CBSE) has introduced Artificial Intelligence as an elective for students from classes 9 to 12.
• Strengthening Cyber Regulations: As AI technology evolves, governments must tighten cyber regulations to address the new risks and challenges posed by AI, such as data security, privacy concerns, and the ethical use of AI.
• Enhancing Transparency and Accountability: Governments must ensure AI systems in governance are transparent, with clear frameworks explaining decision-making, especially in critical areas, to ensure accountability.
• Promoting Inclusive Access: To ensure inclusive governance, it is crucial that AI-driven public services are accessible to all citizens, including marginalised and rural communities.
• Encouraging Public-Private Partnerships: Governments should foster collaborations with the private sector, universities, and research institutions to leverage their expertise in developing AI applications tailored to public service needs.

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Conclusion

GovAI can transform governance by improving efficiency, transparency, and public service delivery. However, its success depends on ethical deployment, data privacy, and balancing AI’s transformative power with accountability, social justice, and human values. 

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