Artificial Intelligence | Science & Technology for UPSC CSE PDF Download

Introduction

Artificial Intelligence (AI) is revolutionizing governance worldwide by enabling governments to process vast amounts of data, automate routine tasks, and deliver public services more efficiently. In India, AI aligns with initiatives like Digital India and Atmanirbhar Bharat, aiming to enhance governance, address socio-economic challenges, and strengthen national security. However, the adoption of AI raises significant ethical concerns, such as bias, privacy violations, and accountability, alongside risks like cybersecurity threats and societal inequalities. India’s National AI Strategy, updated through 2025, emphasizes ethical AI to ensure inclusive growth. As of August 2025, AI’s transformative potential and associated challenges make it a critical topic for UPSC aspirants, intersecting Science and Technology (GS-3), Governance (GS-2), and Ethics (GS-4).

Fundamentals of AI in Governance

Artificial Intelligence refers to systems that mimic human intelligence through technologies like machine learning, natural language processing, and computer vision. In governance, AI analyzes large datasets to inform policy decisions, predict trends, and streamline public services.

• Core Principles: AI systems learn from data to perform tasks such as forecasting, pattern recognition, and decision-making. They enable governments to handle complex challenges, like urban planning or disaster response, with greater speed and accuracy than traditional methods.
• Key Technologies:
  • Machine Learning (ML): Algorithms learn from historical data to predict outcomes, such as identifying high-risk areas for disease outbreaks.
  • Natural Language Processing (NLP): Enables chatbots and language analysis for citizen engagement, such as answering queries in multiple languages.
  • Computer Vision: Processes visual data for applications like traffic monitoring or facial recognition.
  • Robotics: Automates repetitive tasks, such as document verification in public offices.
• Advantages:
  • AI scales to manage India’s large population and diverse needs, improving service delivery.
  • It enables real-time decision-making, critical for crisis management like floods or pandemics.
  • AI reduces operational costs by automating routine processes, freeing resources for development.
• Limitations:
  • AI relies on high-quality data, which is often incomplete in India’s rural areas.
  • High initial costs for infrastructure and training pose financial challenges.
  • Risks of bias and privacy violations threaten public trust in AI systems.

Applications of AI in Governance

AI is transforming various sectors of governance in India, enhancing efficiency, transparency, and inclusivity.

• Public Service Delivery:
  • Healthcare: AI-driven tools diagnose diseases like tuberculosis using chest X-ray analysis, deployed in 200 districts by 2025. Predictive models forecast outbreaks, aiding public health responses.
  • Education: Platforms like SWAYAM use AI to recommend personalized courses, benefiting 10 million learners by 2025.
  • Welfare Schemes: AI ensures targeted delivery of subsidies under schemes like PM-KISAN by verifying beneficiary data, reducing leakages by 20% in 2024.
• Smart Cities and Urban Planning:
  • AI-powered cameras optimize traffic flow in cities like Bengaluru, reducing congestion by 15% in 2025.
  • Predictive analytics improve waste management by forecasting collection needs, implemented in 50 smart cities.
• Law Enforcement and Security:
  • Predictive policing maps crime hotspots, as seen in Delhi Police’s Crime Mapping Analytics, reducing response times by 30%.
  • Facial recognition at airports, integrated with Aadhaar, enhances security but raises privacy concerns.
• Judicial and Administrative Efficiency:
  • AI chatbots on the UMANG platform handle citizen grievances in 20+ languages, resolving 1 million queries monthly in 2025.
  • The e-Courts project uses AI to prioritize cases, reducing judicial backlog by 10% in 2024.
• Agriculture and Rural Development:
  • AI analyzes soil and weather data for precision farming, boosting yields by 15% in Andhra Pradesh’s pilot projects (2024).
  • Satellite imagery and ML assess crop damage for insurance claims under PM Fasal Bima Yojana.
• Economic Planning:
  • AI-driven forecasting models guide fiscal policies, improving GDP growth projections by 5% accuracy.
  • Fraud detection in GST compliance saved ₹50,000 crore in 2024 by identifying tax evasion.

Ethical Issues in AI for Governance

The integration of AI in governance raises profound ethical challenges that must be addressed to ensure fairness and public trust.

• Bias and Discrimination: AI systems trained on biased datasets can perpetuate inequalities. For example, facial recognition systems have misidentified darker skin tones, risking unfair profiling in policing. In welfare schemes, biased algorithms may exclude deserving beneficiaries, exacerbating social disparities.
• Privacy Concerns: Extensive data collection, such as Aadhaar-linked services or surveillance systems, raises risks of misuse or breaches. Citizens may not provide informed consent, undermining autonomy.
• Transparency and Accountability: Many AI models operate as “black boxes,” making their decision-making processes opaque. For instance, if AI denies welfare benefits, citizens may struggle to appeal without clear explanations. Accountability is unclear when AI errors cause harm, such as incorrect criminal profiling.
• Job Displacement: Automation of administrative tasks, like document verification, threatens jobs in public sectors. India needs to reskill 10 million workers by 2030 to mitigate AI-driven unemployment.
• Ethical Principles:
  • Fairness: AI must ensure equitable outcomes across caste, gender, and economic groups.
  • Transparency: Governments must disclose how AI systems make decisions.
  • Accountability: Mechanisms like audits must address AI errors or harm.
  • Privacy: Compliance with data protection laws is essential to safeguard citizen rights.

Risks of AI in Governance

While AI offers transformative benefits, it introduces significant risks that challenge governance systems.

• Cybersecurity Threats: AI systems are vulnerable to hacking, data poisoning, or adversarial attacks that manipulate outputs. A breach in government databases, such as Aadhaar, could compromise national security or expose citizen data.
• Over-Reliance on AI: Excessive dependence on AI for critical decisions, like judicial sentencing or disaster response, may erode human judgment. Systemic failures, such as power outages disrupting AI infrastructure, could paralyze governance.
• Digital Divide: Rural India, with only 40% internet penetration in 2025, lacks access to AI-driven services, deepening urban-rural disparities. Limited digital literacy hinders citizen engagement with AI tools.
• Misuse and Surveillance: AI’s potential for mass surveillance, as seen in China’s social credit system, raises fears of authoritarian misuse in India. Profiling based on sensitive data (e.g., caste, religion) could violate rights.
• Global Risks: AI-driven cyberattacks or misinformation campaigns, amplified by generative AI, threaten electoral integrity and global stability. India reported 1.5 million AI-based cyber incidents in 2024.

Global Developments in AI for Governance

Globally, AI is reshaping governance, with varying approaches to ethics and regulation.

• United States: The Internal Revenue Service uses AI to detect tax fraud, saving $10 billion annually. The National Institute of Standards and Technology’s AI Risk Management Framework (2023) guides ethical AI deployment, emphasizing transparency.
• China: AI powers the social credit system, monitoring citizen behavior, raising ethical concerns. China’s National AI Plan (2030) prioritizes governance applications, with 80% of public services AI-enabled by 2025.
• European Union: The AI Act (2024) classifies high-risk AI systems (e.g., in policing) for strict regulation, mandating transparency and human oversight. EU’s citizen-centric AI improves healthcare and urban planning.
• Other Players:
  • Singapore’s Smart Nation initiative uses AI for traffic and healthcare, reducing hospital wait times by 20%.
  • UAE’s AI Ministry drives innovations like AI-powered policing, solving 30% more cases in 2024.

India’s AI Policy Framework

India’s AI policies aim to harness AI for inclusive growth while addressing ethical and security concerns.

• National AI Strategy (2018–2025):
  • Launched by NITI Aayog under the #AIforAll campaign, it focuses on five sectors: healthcare, agriculture, education, smart cities, and security.
  • Vision: Position India as an “AI Garage” for global solutions, targeting 10% of global AI market share by 2030.
  • Key initiatives: Establish Centres of Excellence for AI research and skilling 1 million professionals by 2025.
• National Quantum Mission (2023):
  • Allocates ₹6,000 crore to integrate AI with quantum computing for governance applications, such as predictive disaster management.
  • Supports development of indigenous AI algorithms for public services.
• Responsible AI for Social Empowerment (RAISE) 2025:
  • Framework for ethical AI, emphasizing fairness, transparency, and accountability.
  • Mandates bias audits for high-risk AI systems (e.g., policing, welfare).
  • Promotes public trust through citizen awareness campaigns.
• Digital Personal Data Protection Act (DPDP), 2023:
  • Regulates AI data usage, requiring consent-based processing and imposing penalties for breaches (up to ₹250 crore).
  • Ensures privacy in AI-driven governance systems like Aadhaar or health records.
• Other Initiatives:
  • AI Task Force (2018): Recommended AI adoption in governance, defence, and agriculture.
  • AIRAWAT: India’s AI supercomputer, ranked among the top 100 globally in 2024, supports governance analytics.
  • IndiaAI Mission (2024): ₹10,000 crore initiative to foster AI startups, skilling, and public sector adoption.

Recent Developments in India

India’s AI ecosystem has seen significant progress, driven by policy and innovation:

• 2023:
  • The DPDP Act was implemented, strengthening data governance for AI systems.
  • AI-based crop yield prediction was rolled out in 100 districts under PM-KISAN, improving farmer incomes by 10%.
• 2024:
  • AIRAWAT supercomputer supported 50 AI governance projects, including urban planning and health diagnostics.
  • AI chatbots on UMANG expanded to 20+ regional languages, handling 1.5 million queries monthly.
  • India-US iCET collaboration launched joint AI governance pilots.
• 2025:
  • IndiaAI Mission initiated 50 AI pilot projects in healthcare (e.g., cancer screening) and agriculture (e.g., pest detection).
  • Bengaluru’s AI-driven Adaptive Traffic Control System reduced congestion by 15%, adopted in 10 cities.
  • Ethical AI guidelines updated, mandating annual audits for high-risk systems in governance.
  • G20 AI dialogues, led by India, emphasized global ethical standards.

Challenges in AI Adoption for Governance in India

India faces several hurdles in scaling AI for governance:

• Infrastructure: Limited high-performance computing and cloud infrastructure hinder AI deployment. Only 30% of government agencies have AI-ready systems in 2025.
• Skilled Workforce: India has ~50,000 AI professionals, far below the 200,000 needed by 2030. Public servants lack AI literacy, slowing adoption.
• Data Quality: Incomplete or biased datasets, especially in rural health and agriculture, reduce AI accuracy. Standardized data protocols across states are absent.
• Ethical and Regulatory Gaps: Slow implementation of ethical AI audits delays trust-building. Balancing innovation with privacy remains challenging.
• Public Trust: Resistance to AI due to fears of surveillance (e.g., facial recognition) or job losses requires transparent communication.

India’s Strategic Context

• Opportunities:
  • India’s 3,000+ AI startups (2025) drive innovation in governance solutions.
  • AI addresses socio-economic challenges, like healthcare access for 1.4 billion citizens.
  • Global leadership via QUAD and G20 collaborations strengthens India’s AI influence.
• Challenges:
  • Countering China’s AI-driven governance models, which prioritize surveillance over privacy.
  • Bridging the digital divide to ensure rural access to AI services.
  • Mitigating AI misuse in elections, with 2 million deepfake incidents reported in 2024.

Future Outlook

• Short-Term (5–10 Years):
  • AI integration in all 100 smart cities by 2030, improving urban governance.
  • Expansion of AI-driven welfare schemes to 500 million beneficiaries.
  • Indigenous AI models for 22 regional languages to enhance inclusivity.
• Long-Term (10–15 Years):
  • AI-quantum convergence for advanced governance analytics, such as real-time disaster prediction.
  • Fully automated citizen services with human oversight, reducing administrative costs by 30%.
• Global Implications:
  • India’s ethical AI frameworks could influence global standards, especially in the Global South.
  • Harmonized regulations will be critical to balance innovation and risk.