Context: As of 2024, over 85 countries, including China, Canada, South Korea, European Union, African Union etc. have released National AI strategy documents for regulation of Artificial Intelligence. However India adopts a flexible – mission mode approach lacking a Comprehensive AI policy to address emerging challenges associated with AI.
India’s Current Approach to AI Governance
1. Background
AI’s rapid advancement brings ethical, legal, economic, and societal challenges, making AI governance a global priority.
India has chosen a mission-driven and flexible approach, rather than a legislative or rigidly strategic one.
2. Current Status
a. Absence of a National Strategy
No officially approved National AI Strategy or legislation specific to AI exists.
The NITI Aayog’s 2018 document titled “National Strategy for Artificial Intelligence” remains non-binding:
It lacks formal endorsement, an implementation plan, or dedicated budget.
b. Mission-Based Framework
India is currently pursuing the IndiaAI Mission which emphasizes:
Innovation and foundational models
Skill development
Trustworthy and safe AI ecosystems
Multiple initiatives are underway, but operate under a broader, non-legislative umbrella.
c. Expert Advisory Committee
A panel of experts is tasked with drafting governance recommendations.
However, there’s uncertainty over whether these will lead to official frameworks or remain as internal advisory mechanisms.
3. Analysis of India’s Approach
a. Strengths
Flexibility: India’s approach is adaptable to:
Rapid technological changes
Global shifts in AI governance
Evolving trade and geopolitical interests
Public sentiment and emerging risks
b. Gaps and Limitations
Lack of a Unified Vision:
No comprehensive articulation of India’s AI priorities, goals, or accountability mechanisms.
No Institutional Continuity:
AI efforts lack a constitutional or statutory mandate, making them ad hoc and potentially unsustainable.
Opaque Deployment in Key Sectors:
Sectors like healthcare, education, finance, and governance are adopting AI without adequate public transparency or impact assessments.
Societal Risks Unaddressed:
No mechanisms for civic dialogue or public oversight on:
Algorithmic bias
Data sourcing
Workforce displacement
Real-World Implications:
AI-generated misinformation has already led to real-life violence in India.
This highlights the urgent need for ethical safeguards and regulatory accountability.
What Can Be India’s Approach to AI Governance?
1. Global Models of AI and Data Governance
a. Centralised, Cross-Sectoral Model
Examples: EU’s GDPR, China’s PIPL.
Features:
Uniform regulations across all sectors.
Central oversight authority.
Emphasis on user rights, transparency, and data protection.
Relevance to India:
India’s Digital Personal Data Protection (DPDP) Act, 2023 mirrors this model.
DPDP could serve as a foundational framework for future AI-specific regulations.
b. Decentralised, Sector-Specific Model
Example: United States.
Features:
Regulations vary by industry (e.g., healthcare, finance).
Encourages innovation through less regulatory burden.
Involves multiple agencies with domain-specific expertise.
Applicability in India:
Could be useful for sectors with diverse AI maturity levels, but may lead to regulatory fragmentation.
c. Technology-Specific Legislation
Example: China’s approach to Generative AI and Deep Synthesis.
Features:
Laws targeted at specific AI technologies.
Controls on content generation, algorithm transparency, and platform accountability.
Learning for India:
Helps proactively address emerging risks, especially in areas like misinformation and deepfakes.
2. A Hybrid Framework: The Indian Way Forward
India could develop a hybrid model, balancing innovation with regulation, and tailored to its unique socio-economic context.
a. Build on DPDP Act, 2023
Use it as a central regulatory spine.
Ensure data privacy, consent, and accountability are extended to AI systems.
b. Sector-Specific Guidelines
Introduce domain-focused rules in sensitive sectors like:
Healthcare: Medical AI transparency and liability.
Finance: Algorithmic audits for risk and bias.
Governance: Ethical use in public services and surveillance.
c. Technology-Specific Rules
Create targeted rules for Generative AI, facial recognition, autonomous systems, etc.
Set norms for:
Content authenticity
Bias detection
Responsible development and deployment
d. Institutional and Ethical Oversight
Establish an AI Ethics and Oversight Authority to:
Monitor AI use cases.
Promote transparency and algorithmic audits.
Enable public consultation and civic engagement.
e. Promote Innovation and Indigenous Development
Create regulatory sandboxes to test new AI solutions.
Incentivise homegrown AI research and open models like those under the IndiaAI Mission.