Ethical AI
NEWS: Prime Minister Narendra Modi advocated that India’s AI model should promote local and indigenous content as well as regional languages.
He also stressed that AI developed in India must be ethical, unbiased, transparent, and grounded in strong data privacy principles
WHAT’S IN THE NEWS?
Context and Significance of the Roundtable
The Prime Minister chaired a high-level roundtable with Indian AI start-ups ahead of the AI Impact Summit 2026, scheduled to be hosted in India in February.
The interaction reflects the government’s intent to:
Position India as a global AI innovation hub, not merely a technology consumer.
Align AI development with societal transformation, inclusive growth, and public welfare.
The Prime Minister underscored AI’s potential to:
Enhance productivity across sectors
Improve governance outcomes
Address developmental challenges at scale
India’s Technology and AI Ecosystem: Current Status
Scale and Growth of the Technology Sector
India’s technology sector revenues are projected to cross USD 280 billion annually, reflecting strong momentum.
Over 6 million professionals are employed in the technology and AI ecosystem, making it one of the world’s largest digital workforces.
Global Standing in Artificial Intelligence
India ranks 3rd globally in AI competitiveness according to Stanford University’s 2025 Global AI Vibrancy Tool, indicating:
Strength in talent availability
Rapid growth of AI startups
Expanding research and deployment capabilities
Global Capability Centres (GCCs)
India hosts 1,800+ Global Capability Centres, of which 500+ are AI-focused.
GCCs are offshore centres set up by multinational corporations to provide:
1. R&D services
2. Data analytics
3. AI and machine learning solutions
This demonstrates India’s role as a backbone of global AI value chains.
Startup Ecosystem and AI Penetration
India has around 1.8 lakh startups, making it the third-largest startup ecosystem globally.
Nearly 89% of new startups launched last year integrated AI into their products or services.
This indicates AI has moved from experimentation to mainstream business adoption.
Enterprise Adoption of AI
According to the NASSCOM AI Adoption Index, India scores 2.45 out of 4.
About 87% of enterprises are actively deploying AI solutions.
Leading sectors in AI adoption:
1. Industrial and automotive manufacturing
2. Consumer goods and retail
3. Banking, Financial Services and Insurance (BFSI)
4. Healthcare
Together, these sectors contribute nearly 60% of AI’s total economic value in India.
Artificial Intelligence: Conceptual Overview
Artificial Intelligence refers to the capability of machines to perform tasks that typically require human intelligence.
Core features include:
1. Learning from data and experience
2. Pattern recognition and prediction
3. Decision-making and problem-solving
AI systems rely on:
1. Large datasets
2. Algorithms and neural networks
3. Large Language Models (LLMs)
With iterative training, AI systems improve reasoning, communication, and contextual understanding, approximating human-like cognition in specific domains.
Key Concerns and Challenges in AI Adoption
Bias and Discrimination
AI systems trained on biased or incomplete datasets may:
1. Reinforce social inequalities
2. Discriminate against vulnerable or marginalised groups
This raises ethical concerns, especially in areas like policing, recruitment, and credit allocation.
Data Privacy and Protection
Despite the Digital Personal Data Protection Act, 2023, India lacks:
1. Detailed sector-specific rules
2. Strong safeguards for sensitive citizen data
Large-scale AI deployment increases risks of surveillance and misuse of personal information.
Accountability and Legal Ambiguity
An unresolved “accountability gap” persists:
· If AI causes harm, responsibility is unclear among developers, deployers, and the state.
This complicates liability frameworks in governance, healthcare, and justice delivery.
Overdependence on Technology
Excessive reliance on AI can:
1. Undermine human judgment and empathy
2. Ignore socio-cultural context
Particularly risky in sectors like judiciary, healthcare, and social welfare.
Cybersecurity Risks
AI systems are vulnerable to:
1. Hacking and data poisoning
2. Adversarial attacks manipulating outputs
This can compromise national security and critical infrastructure.
Dependence on Foreign Technology
Heavy reliance on external AI platforms and cloud infrastructure risks:
1. Strategic vulnerability
2. “Digital colonisation” through data and technology dependence
Strengthening indigenous AI capabilities is therefore critical.
Government Initiatives to Build a Sovereign AI Ecosystem
IndiaAI Mission (2024)
The IndiaAI Mission has an outlay of ₹10,300 crore over five years.
Core objectives include:
1. Democratising access to AI computing resources
2. Supporting startups and academic research
A flagship component is the creation of a common computing facility with 18,693 high-end GPUs.
Indigenous AI Models and Language Technologies
BharatGen
Launched in 2024 as the world’s first government-funded multimodal LLM initiative.
Focuses on:
1. Text, speech, image, and video processing
2. Public service delivery and governance use-cases
Sarvam-1 AI Model
A large language model with 2 billion parameters.
Optimised for Indian languages, supporting ten major languages.
Designed to enable vernacular AI applications.
Hanooman’s Everest 1.0
Developed by SML, this multilingual AI system:
1. Supports 35 Indian languages
2. Plans expansion to 90 languages
Aims to improve inclusivity and linguistic accessibility.
Bhashini Platform
Bhashini is an AI-powered language platform.
Provides:
1. Translation
2. Speech-to-text and text-to-speech tools
Reduces language barriers in governance and service delivery.
AI Centres of Excellence
Dedicated AI hubs are being established to:
1. Promote research and innovation
2. Support startups and skill development
These centres act as regional innovation ecosystems.
AI and India’s Digital Public Infrastructure (DPI)
India’s DPI combines public funding with private innovation.
Foundational pillars include:
1. Aadhaar
2. UPI
3. DigiLocker
AI is being integrated into DPI to:
1. Enhance financial inclusion
2. Improve targeting of welfare schemes
3. Strengthen governance efficiency
AI in Judiciary: e-Courts Project
The Supreme Court of India initiated the e-Courts Project to modernise judicial administration.
Phase III focuses on:
1. AI-enabled case management
2. Predictive analytics for workload allocation
3. Improved administrative efficiency
The objective is not to replace judges, but to augment judicial decision-making.
Conclusion: Strategic Way Forward
India’s AI strategy combines:
1. Infrastructure expansion
2. Indigenous model development
3. Startup ecosystem support
4. Ethical and inclusive governance
Practical AI applications in agriculture, healthcare, education, and governance demonstrate tangible public value.
By balancing innovation with regulation and sovereignty, India is laying a strong foundation to:
1. Become a global AI leader
2. Advance the long-term vision of Viksit Bharat 2047
Source: https://www.newindianexpress.com/nation/2026/Jan/09/india-needs-localised-ethical-ai-pm