Governance and Artificial Intelligence (AI)

What Do We Mean by Governance and AI?

Governance and Artificial Intelligence (AI) refers to the integration of AI technologies into public administration and decision-making, while ensuring that such use remains aligned with:

• Democratic principles
• Constitutional values
• Accountability and transparency

In simple words, the question before the State is not whether to use AI, but how to use AI responsibly in governance.

👉 AI is a tool, not a substitute for democratic judgment.

Role and Potential of AI in Governance

(a) Efficient Service Delivery

AI enables:

• Automation of routine administrative tasks
• Faster processing of applications
• Reduced human workload

Governance value: Efficiency, speed, and consistency in public service delivery.

(b) Education

AI supports personalised and adaptive learning.

Example:

• NCERT has used AI-based metadata tagging in NROER resources to improve discoverability and customised learning pathways.

Governance value: Equity in education and improved learning outcomes.

(c) Healthcare

AI expands access and accuracy in healthcare.

Example:

• NITI Aayog, in collaboration with Department of Biotechnology (DBT), developed a cancer image database to aid AI-based diagnostics.

Governance value: Better healthcare reach, especially in underserved regions.

(d) Agriculture

AI supports predictive and preventive governance.

Example:

• National Pest Surveillance System uses AI to:
  • Predict pest attacks
  • Issue early warnings

Governance value: Farmer welfare, food security, and risk reduction.

(e) Inclusivity and Language Access

AI bridges linguistic barriers.

Example:

• Bhashini enables digital services in multiple Indian languages.

Governance value: Inclusive governance and digital empowerment.

National AI Initiatives in India

(a) IndiaAI Mission

• A flagship initiative to build AI infrastructure.
• Includes deployment of 10,000+ GPUs for → Startups, Research, Public sector AI applications

Governance relevance: Democratization of AI computing power.

(b) BharatGen

• A government-funded project to develop a multilingual, multimodal foundational AI model.
• Tailored to India’s linguistic and cultural diversity.

Governance value:

• Reduces dependence on foreign AI models
• Ensures cultural and contextual relevance

(c) Digital India Bhashini

(National Language Translation Mission)

• AI-powered real-time translation platform.
• Facilitates interaction across Indian languages.

Governance value: Linguistic inclusion in digital governance.

(d) Global AI Partnerships

• India is a member of Global Partnership on Artificial Intelligence (GPAI).
• Participates in US–India AI collaborations.

Governance value: Norm-setting, ethical AI leadership, global cooperation.

AI in Governance: Indian Use Cases

(a) India Urban Data Exchange (IUDX)

• Platform enabling secure data sharing among urban stakeholders.

Governance impact → Improved urban planning, Better service delivery, Evidence-based decision-making

(b) Ideal Train Profile – Indian Railways

Entity: Indian Railways

• AI-driven system to:
  • Analyse passenger demand
  • Optimise train occupancy and revenue

Governance value: Efficiency and optimal use of public assets.

(c) DigiYatra

• Biometric-based boarding system at airports.

Governance value: → Seamless passenger experience, Reduced congestion, Enhanced security

(d) State-Level Innovations

• Telangana:
  • Partnered with Meta to integrate generative AI into e-governance.
• Arunachal Pradesh:
  • Uses generative AI to analyse Monthly Development Reports from 67 departments.

Governance value: Data-driven and outcome-oriented administration.

Challenges in AI-Driven Governance

(a) Fragmented Data Ecosystem

• National Data Governance Framework Policy is yet to be fully implemented.
• Data silos reduce AI effectiveness.

(b) Infrastructure Gaps

• As per Internet and Mobile Association of India (IAMAI, 2023):
  • Around 45% Indians lack internet access, especially in rural areas.

⚠ AI without connectivity worsens inequality.

(c) Absence of AI-Specific Law

• Unlike the EU AI Act, India lacks a dedicated AI legislation.

⚠ Risk of unregulated deployment and accountability gaps.

(d) Skill Shortage

• NASSCOM estimates a shortage of 1.4 lakh AI professionals.

(e) Privacy and Data Protection Risks

• AI systems rely heavily on personal data.
• Example:
  • Aadhaar data leak allegations affected 81.5 crore individuals.

⚠ Trust deficit can undermine digital governance.

Way Forward: Responsible AI for Governance

(a) Ethical Oversight Mechanisms

• Regular risk assessments
• Bias and fairness metrics
• Mandatory human-in-the-loop supervision

(b) Data Sovereignty and Protection

• Strong enforcement of the Digital Personal Data Protection Act, 2023.
• Special attention to cross-border data flows.

(c) Transparency and Explainability

• Algorithmic audits
• Model cards
• Use of diverse and representative datasets

✔ Essential for public accountability.

(d) Skill Development and Inclusion

• Expand AI education to:
  • Rural and underserved regions
  • Government officials and frontline workers

(e) Public–Private Collaboration

• Build shared AI infrastructure.

Example: 10,000+ GPUs under the IndiaAI Mission.

Concluding Governance Insight

Artificial Intelligence can make governance → Faster, Smarter, More inclusive
But without ethics, regulation, and accountability, it can also make governance → Opaque, Exclusionary, Unaccountable

👉 Therefore, the real challenge is not building intelligent machines, but ensuring intelligent governance.
This balance between innovation and democratic control is the essence of Governance and AI for UPSC.