Context: Chinese People’s Liberation Army (PLA) has initiated the integration of Artificial Intelligence (AI) into its military operations particularly in non-combat support roles.
Relevance of the Topic: Prelims & Mains: Applications of AI in Defence.
Applications of AI in Military Operations:
AI in Military Healthcare: DeepSeek’s large language model (LLM) is being used in PLA hospitals, armed Police (PAP) and national defence mobilisation units to provide:
treatment plan suggestions to doctors.
healthcare of senior Chinese officials and military officers.
AI in Military Training:
As a support tool to assist military commanders, rather than autonomously making battlefield decisions.
Assisting in physical training programs, and exercise plan creation for paramilitary forces.
AI in Psychological Support: Psychological counseling to help soldiers cope with stress and anxiety.
Future Prospects:
Analysts predict that AI models like DeepSeek will soon be used for:
Battlefield intelligence gathering and surveillance.
Real-time data processing for enhanced situational awareness.
Improving combat strategy through predictive analysis and decision-making support.
PLA has already explored AI for drone swarm tactics, pilot training simulations, and combat scenario analysis.
Low-cost AI model: making it more accessible than competitors like ChatGPT.
Lower computational requirements: making it more scalable for military and civilian uses.
Indigenously built by China: ensures data security by storing and processing information on local servers, and reducing external cybersecurity threats.
Context: Grok, an AI chatbot developed by xAI, has sparked controversy due to its unfiltered responses on X (formerly Twitter). Some responses included misogynist slurs, misinformation claims, and politically charged statements highlighting the growing need for AI regulations to prevent misinformation and ensure accountability.
Relevance of the topic:
Prelims: Safe Harbour Principle.
Mains: Need to regulate AI.
Concerns associated with Generative AI and its Regulation
Amplify Misinformation: Generative AI systems if trained on biased data or developed with inherent biases, will generate biased outputs and prejudicial content. Since Grok allows direct publishing onto a social media platform (X), this content can spread unchecked and has a potential risk of faster dissemination of misinformation.
Lack of Transparency: AI Algorithms often have a black-box approach, i.e., one cannot thoroughly explain how the variables led to the resulting prediction. E.g., Grok’s AI-generated replies to users do not always carry citations or links to sources/web pages, limiting verifiability. This can further amplify misinformation.
Risk of Censorship: This may lead to companies self-censoring just due to fear of regulatory actions by governments. That creates a chilling effect on freedom of expression and can inhibit innovation.
Accountability for AI-generated output: Article 19(1)(a) grants freedom of speech but with reasonable restrictions.But these rights apply only to humans, and not AI systems. AI responses are machine-generated and lack personal intent. This makes it difficult to set legal accountability and determine liability for the responses made by AI.
Extension of Safe Harbour to AI: Social Media Intermediaries get safe harbour under the Section 79 of IT Act 2000. Grok is not a human user, but a computer program producing answers from massive internet data. This raises the question ofwhether safe harbour can be extended to AI-generated content.
Safe Harbour Principle:
Safe harbour is a legal provisionthat provides protection from a liability or penalty. Under Section 79 of IT Act 2000, intermediaries such as X, Meta etc. are protected from any legal liability for content posted on their platforms by the users.
As per the law, since the content posted on the social media platforms is owned by the users and does not belong to such companies, the provision gives them protection from prosecution.
Way Forward
Moderation of AI-bots by Developers: Developers should be more transparent about the datasets used for training to ensure diversity, and conduct thorough red-teaming and stress testing of AI bots to mitigate potential harms.
Extend liability on the deployer in the event of wilful neglect and when no adequate measures are taken to moderate outputs. However, liability on deployers may depend on a case-to-case basis. E.g., In 2024, Air Canada was directed by a civil court to honour a false refund policy made up by an AI chatbot on its website.
Strengthen International collaboration and regulations for the responsible development and deployment of generative AI models and Chat bots.
Legislators must continue to strike a balance between ethical duty, protecting digital rights and free expression and technological innovation.
Context: Tata Communications announced the launch of Vayu, an AI-powered cloud solution for enterprises.
Relevance of the Topic:Prelims: Key facts about Vayu: AI-Based Cloud Solution.
Vayu: AI-Based Cloud Solution
Developed by: Tata Communications Limited
Type: AI-based cloud solution
Aim: To tackle rising cloud costs, multi-cloud complexities, and Al infrastructure demands.
Key Features of Vayu Al Cloud:
Unified ecosystem: Guarantees seamless data accessibility across different environments (cloud to edge, data to AI, and security to connectivity) while maintaining data integrity.
Provides Integrated services: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), GPU-as-a-service, AI platforms, security, cloud connectivity, and professional services.
Provides on-demand access to high-performance NVIDIA GPUs (eliminating costly infrastructure investments) and enabling seamless Al model training, fine-tuning, and deployment at scale.
Transparent pricing model: No data egress charges (for transferring data to the internet or another cloud provider) or other hidden fees. Reduces costs by 15-25% compared to large cloud service providers.
Relevance for India's Digital Economy:
Supports businesses in the intelligent enterprise era by optimising AI workloads and cloud-based services.
Aligns with India's push for digital transformation, data localisation, and cost-efficient cloud adoption.
Enhances India's position in cloud computing and AI-driven business solutions, contributing to technological self-reliance.
Context: ICRA Limited, Indian Credit Rating firm, has forecasted that India’s data center operational capacity will go up to 2,000-2,100 MW by March 2027, from over 1,150 MW in December 2024. This would require an investment of ₹40,000-45,000 crore in the next two years.
Relevance of the Topic: Mains: India’s Computing & AI infrastructure- Data centers; Government Initiatives
What are Data Centers?
Data centers are highly specialised facilities designed to house computing systems and their related components, such as, physical hardware, servers, networking equipment and storage systems.
Utility:
Process, store, and distribute data for various applications and services, such as websites, cloud computing, and enterprise operations.
Empower organisations to handle large volumes of data securely and efficiently, and enable cloud computing to function seamlessly.
Potential of Data Centers in India:
India aims to become a global hub for AI innovation and data center development.
Current capacity:
India's data center capacity is approximately 1,255 MW at present (March 2025).
India holds 20% of global data but only 3% of data center capacity.
Expansion potential of data center in future, due to:
Increasing digitalisation and data consumption.
Rise in demand for AI and generative AI projects.
Nationwide roll-out of 5G.
Need for edge computing to allow data processing on devices.
Data localisation initiatives (store data within National borders).
Concentration of data center in India:
About 95% of the existing data center capacity is in metros cities (Mumbai, Chennai and Hyderabad).
Mumbai has >50% of current capacity due to its central location, reliable power and cable landing stations.
Government Initiatives in this Regard
1. Data Localisation Rules: India’s laws mandate that certain data be stored locally, such as:
Reserve Bank of India's Directive (2018) mandates payment system providers to store entire payment data (transaction details, customer information and related data) within India.
IRDAI (Maintenance of Insurance Records) Regulation, 2015 requires covered organisations to store insurance data within India.
The draft Digital Personal Data Protection Rules focus on targeted data localisation, addressing children's online age verification challenges, and data protection.
Digital Personal Data Protection Act permits cross-border data transfers to all countries, unless restricted by the Central Government by notification.
2. Digital India Mission:
Digital India campaign launched in 2015, aims at the development of secure and stable digital infrastructure (including data centers), delivering government services digitally, and universal digital literacy.
3. IndiaAI Mission:
The Rs 10,370 croreIndiaAI Mission aimsto:
establish a computing capacity of more than 10,000 GPUs.
help develop foundational models with a capacity of more than 100 billion parameters trained on datasets covering major Indian languages for priority sectors like healthcare, agriculture, and governance.
The idea is that if such an infrastructure exists in the country, start-ups could plug into it for developing AI systems.
Of the total outlay, Rs 4,564 crore has been earmarked for building computing infrastructure.
Generative AI-led high computing requirements present a new wave of demand for data center capacity. Favourable regulatory policies coupled with an infrastructure status for the data center sector would support strong growth prospects in India.
Context: The Ministry of Electronics & IT (MeitY) haslaunched AI Kosha, a secured AI datasets platform, along with the IndiaAI Compute Portal and other initiatives to accelerate AI innovation and research in India.
Relevance of the Topic: Prelims: AI Kosha- Features.
AI Kosha
AI Kosha is a newly launched platform to provide non-personal datasets for developing Artificial Intelligence (AI) models and tools.
It is part of the IndiaAI Mission (₹10,370 crore initiative) to strengthen India’s AI ecosystem.
At launch, AI Kosha contains 316 datasets, primarily focused on language translation tools for Indian languages.
Key Features:
AI Dataset Repository: Hosts over 300 datasets and 80+ AI models for research and development.
AI Sandbox Environment: Provides an integrated development environment (IDE) with tools and tutorials for AI model training.
Content Discoverability: Uses AI-readiness scoring to help researchers identify relevant datasets.
Security & Access Control: Features data encryption (at rest & in motion), API-based secure access, and real-time malicious traffic filtering.
Permission-Based Access: Allows tiered access for different user groups like researchers, startups, and government bodies.
Government’s role in AI data Aggregation
Indian government has previously attempted to aggregate public data for technological development:
Open governance data platform (data.gov.in): Hosts 12,000+ datasets from various government agencies.
Chief Data Officers: Appointed across ministries to encourage data sharing for research and policy-making.
Non-personal data sharing proposal (2018-2020):
A committee, led by Infosys co-founder Kris Gopalakrishnan, explored the possibility of compelling firms to share non-personal data (e.g., ride-sharing traffic data).
Faced resistance from private players, delaying implementation.
Potential Impacts of AI Kosha
Boost to AI research and development: Provides open datasets to Indian start-ups and research institutions, reducing dependence on foreign AI models.
Enhanced language processing capabilities: Strengthens AI-based translation and voice recognition tools for regional Indian languages.
Better public policy decisions: Government access to aggregated health, census, and satellite data can improve policymaking in education, healthcare, and disaster management.
Strengthening India’s AI competitiveness: Positions India as a major AI player, reducing reliance on foreign tech giants like OpenAI and Google.
Challenges
Limited private sector participation: Previous efforts to obtain non-personal data from private companies faced strong opposition.
Data privacy issues: While AI Kosha only includes non-personal data, concerns remain about data security and misuse.
Infrastructure limitations: Despite government-backed GPUs, India still lags behind global AI leaders in high-performance computing capacity.
AI Kosha will enable the development of indigenous AI tools and models. However, ensuring private sector collaboration, data security, and robust computing infrastructure will be key to its success.
Context: The Central government is set to procure an additional 14,000 Graphics Processing Units (GPUs) under the IndiaAI Mission. The recently launched IndiaAI Compute Portal provides access to 18,000 GPUs procured under the IndiaAI mission. The expansion aims to bolster India’s AI computing infrastructure, particularly required for training large and small language models (LLMs and SLMs), which form the backbone of generative AI applications.
Relevance of the Topic: Prelims: Graphics Processing Unit (GPU);IndiaAI Mission
What are Graphics Processing Unit (GPUs)?
GPU is an electronic circuit (high end chip or processor) that can perform mathematical calculations at high speed.
It was originally designed to speed computer graphics and image processing on various devices (like mobile phones and personal computers etc.)
Presently, GPU is an essential enabler of emerging and future technologies such as machine learning (ML), artificial intelligence (AI) and blockchain.
By performing mathematical calculations rapidly, a GPU reduces the time needed for a computer to run multiple programs.
About IndiaAI Mission
IndiaAI Mission is an initiative of the Ministry of Electronics and Information Technology (MeitY).
Total outlay: Rs 10,370 crores
Aim: To build a comprehensive AI ecosystem that fosters innovation by democratising computing access, enhancing data quality and developing indigenous AI capabilities.
Key Pillars: IndiaAI Mission seeks to develop:
IndiaAI Compute Capacity: Establish a computing capacity of 10,000 or more GPUs, via public-private partnerships, offering AI services and resources.
IndiaAI Innovation Centre: Develop and deploy indigenous Large Multimodal Models (LLMs) and domain-specific foundational models, with a capacity of >100 billion parameters, for priority sectors like healthcare, agriculture, and governance.
IndiaAI Datasets Platform: Streamline the access to high-quality non-personal datasets for AI innovation.
Responsible AI development.
A major portion of the total scheme outlay has been earmarked for building computing infrastructure. The idea is that if such an infrastructure exists in the country, start-ups could plug into it for developing AI systems.
Recent Developments under the IndiaAI Mission and AI Ecosystem
Scaling AI Compute Infrastructure (IndiaAI Compute Portal):
The India AI Mission was launched with an initial target of 10,000 GPUs.
Presently, India has a formal compute facility of 14,000 GPUs, which is soon expected to be scaled to more than 18,000 GPUs.
It will allow end users to access a wide range of high-end GPUs and facilitate AI research and innovation in the country.
Opening Access to affordable High-Performance Computing:
India has pioneered the launch of an open GPU marketplace, making high-performance computing accessible to startups, researchers, and students.
A new common compute facility is to be launched soon allowing researchers and startups to access GPU power at a highly subsidised rate of ₹100 per hour, compared to the global cost of $2.5 to $3 per hour.
Robust GPU Supply Chain:
The government has selected 10 companies to supply the GPUs, ensuring a robust and diversified supply chain.
Indigenous GPU Capabilities:
India aims to develop its own globally competitive GPU within the next 3-5 years, reducing reliance on imported technology.
AI Kosha:
Recently launched AI Kosha (IndiaAI Datasets Platform) under the IndiaAI Mission.
It is India's first government-backed non-personal dataset platform to enable AI model training.
Non-personal data includes data from agriculture department, weather forecasting, logistics, and datasets from Bhashini (government platform for Indian language translation).
The government is talking to private players for them to contribute non-personal data for the platform.
Strengthening Semiconductor Manufacturing:
India is advancing semiconductor manufacturing, with five semiconductor plants under construction.
These developments will not only support AI innovation but also reinforce India’s position in the global electronics sector.
Context: Artificial Intelligence (AI) encompasses technologies that simulate human thinking and decision-making. While basic forms of AI have existed since the 1950s, AI adoption has advanced rapidly in recent years. However, the rapid development of AI comes with environmental consequences.
Relevance of the Topic: Prelims: Environmental consequences of Artificial Intelligence.
Expansion of AI market
The global AI market is valued at $200 billion and is projected to contribute up to $15.7 trillion to the global economy by 2030. E.g.,
Announcement of the Stargate Project by the US, involving more than $500 billion in AI infrastructure investments over four years.
In India, Reliance is planning to build the world’s largest data centre in Jamnagar, in partnership with Nvidia.
India has also announced plans to build its own LLM (large language model) to compete with DeepSeek and ChatGPT.
However, rapid expansion of AI brings not only opportunities but also risks, particularly at environmental costs.
Environmental impact of AI
High Energy Consumption and Carbon emission:
Data centres (the backbone of AI operations) consume enormous electricity and contribute 1% of global greenhouse gas emissions.
A simple search request made through ChatGPT (an AI-based virtual assistant) consumes 10 times the electricity of a Google Search, as reported by the International Energy Agency.
Training advanced AI models, such as GPT-3, can emit up to 552 tonnes of carbon dioxide equivalent — comparable to the annual emissions of dozens of cars.
E-Waste Crisis and Environment Destruction:
Rapid expansion of data centres is also fuelling a growing e-waste crisis, which often contains hazardous substances, like mercury and lead.
Microchips that power AI need rare earth elements, which are often mined in environmentally destructive ways.
Water Depletion:
Data centres use million litres of water during construction and, once operational, cool electrical components and maintain operational temperatures.
To mitigate these environmental risks, governments and the private sector must proactively work towards embedding sustainability into AI ecosystem design.
Way Forward
Adoptingstandardised global procedures to measure the environmental impact of AI.
Governments can develop regulations that require companies to disclose the direct environmental consequences of AI-based products and services.
Tech companies can make AI algorithms more efficient, reducing their demand for energy, while recycling water and reusing components where feasible.
A study by Google has found that the carbon footprint of LLMs can be minimised by a factor of 100 to 1,000 through optimised algorithms, specialised hardware, and energy-efficient cloud data centres.
Instead of collecting new data or training models from scratch, businesses can adapt pre-trained models to new tasks.
Using energy-efficient hardware and ensuring regular maintenance can also significantly minimise emissions.
Encourage companies to green their data centres, including by using renewable energy and offsetting their carbon emissions.
Locating data centres in areas with abundant supply of renewable resources can help lower the carbon footprint.
At COP29, the International Telecommunication Union emphasised the urgent need for greener AI practices.
Sustainability needs to be incorporated into the very design of the AI ecosystem to balance innovation and environmental responsibility. This will harness the transformative potential of AI without compromising the Earth’s future.
Context: Microsoft CEO Satya Nadella highlighted Project Farm Vibes in Baramati, Maharashtra, showcasing how AI-driven solutions improved crop yield by 40% and reduced fertilizer use by 25%.
Relevance of the topic:
Prelims: AI- Applications
Mains: AI in Agriculture
About Project Farm Vibes
What is it?
A suite of AI-driven agricultural technologies developed by Microsoft Research to enhance farming efficiency, sustainability, and productivity.
Uses satellite data, IoT sensors, drones, and AI algorithms to generate actionable insights for farmers.
Organisations associated: Microsoft Research & Azure AI Team, Agricultural Development Trust, Baramati, Oxford University AI Researchers.
How AI transformed agriculture in Baramati?
Sensor Fusion Technology: Integrated real-time data from drones, satellites, and soil sensors to optimise farm operations.
AI-Powered Insights: AI analysed soil moisture, temperature, pH levels, and humidity, offering data-driven recommendations.
Vernacular AI Assistance: Farmers accessed AI-generated advice in their local language, making technology more accessible and user-friendly.
Precision farming: Spot fertilisation techniques reduced chemical use by 25%, improving soil health and sustainability.
Climate-responsive farming: AI monitored weather patterns and field conditions, enabling better water management and crop scheduling.
Impact on Agriculture:
40% increase in crop yield: AI-driven insights led to better farming practices and higher productivity.
25% reduction in fertilizer costs: Precision farming minimized chemical overuse, improving cost-effectiveness.
50% water conservation: AI-enhanced irrigation strategies optimized water usage, making farming more sustainable.
Shorter crop cycle: Sugarcane harvest time reduced from 18 to 12 months, increasing profitability for farmers.
12% reduction in Post-harvest losses: AI applications streamlined logistics and storage, cutting wastage.
Role of Artificial Intelligence in Agriculture
Precision Agriculture (Enhancing productivity and efficiency):
AI technologies, such as machine learning, drone applications, and remote sensing, are revolutionising farming practices.
These innovations enable precise monitoring of crop health, soil conditions, and weather patterns, allowing farmers to make informed decisions.
These allow for targeted interventions, such as precise application of water and fertilizers.
Data-driven innovations:
By analysing vast amounts of data, AI systems can recommend optimal planting times, crop rotations, and irrigation schedules. It helps in conserving water, reducing chemical usage, and maintaining soil health.
For example, drones equipped with hyperspectral imaging can detect nutrient deficiencies and pest infestations early.
The concept of Hybrid Agricultural Intelligence (HAI), which combines farmers’ indigenous knowledge with AI, is particularly promising for smallholder farmers in India.
Climate-Smart Agriculture:
AI can predict weather patterns and provide early warnings forextreme weather events, enabling farmers to take preventive measures.
AI-based systems can optimise resource use, such as water and fertilizers, to adapt to changing climatic conditions.
AI-Powered Solutions in Agriculture
Kisan e-Mitra Chatbot:
An AI-powered tool designed to assist farmers with queries related to the PM Kisan Samman Nidhi scheme.
It supports multiple languages and is evolving to provide information on other government programs.
National Pest Surveillance System:
AI and Machine Learning (ML) are utilized in the National Pest Surveillance System to detect crop issues early.
It helps in timely interventions, reducing crop losses due to pests and diseases.
IoT-based Irrigation systems:
Indian Council of Agricultural Research (ICAR) has developed IoT-based irrigation systems tested in the field for selected crops.
These systems optimize water usage, ensuring efficient irrigation.
Crop health monitoring:
AI-based analytics, using field photographs and satellite data, assess crop health.
It monitors weather and soil moisture conditions, particularly for rice and wheat, enabling farmers to make informed decisions.
Concerns in integration of AI into Agriculture
Challenges for smallholders: Small landholdings in India pose a challenge for the adoption of AI technologies, which are often designed for larger farms.
Ensuring affordable and accessible AI tools for smallholder farmers is crucial.
Technological infrastructure and costs: The high costs of AI technologies and the need for robust technological infrastructure are significant barriers.
Skill deficiency: There is a need for specialized skills to operate and maintain these technologies.
Context: A team of researchers from Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi) have come up with AI-powered data integration and predictive analytics tools (AMRSense and AMROrbit Scorecard) to understand patterns of antibiotic resistance in real time.
Relevance of the Topic: Prelims: Key facts about Antimicrobial Resistance (AMR); Role of AI in combating AMR.
What is Antimicrobial Resistance (AMR)?
Antimicrobial resistance is the resistance acquired by any microorganism (bacteria, viruses, fungi, parasite, etc.) against antimicrobial drugs (such as antibiotics, antifungals, antivirals, antimalarials) that are used to treat infections.
Microorganisms that develop antimicrobial resistance are sometimes referred to as superbugs. Due to AMR, standard treatments become ineffective, infections persist and may spread to others.
The World Health Organisation (WHO) has identified antimicrobial resistance (AMR) as one of the top threats to public health.
Role of AI tools in combating Antibiotic Resistance
The AI-driven tool AMRSense has been deployed to use routine data that is generated in hospitals to generate accurate and early insights on antimicrobial resistance at the global, national, and hospital levels.
Utility:
Bridge gap in community-level AMR data collection and evidence-based management.
AI-assisted data recording: Empowers community health workers (CHWs) for accurate and simplified data collection.
Data integration by creating a unified AMR data ecosystem through the integration of data on antibiotic sales, consumption, and WHO Net-compliant surveillance data, using open-source tools and APIs.
Predictive analytics by using federated analytics across the One Health Ecosystem for integrative insights on AMR.
AMROrbit Scorecard for monitoring and evaluating AMR trends to guide targeted interventions and demonstrate the benefits of data collection.
Promote AI-driven or AI-enhanced antimicrobial stewardship.
Limitations: Lack of consistent surveillance data including antibiotic sales data, patient records etc.
Context: In the last decade, the number of submissions from drugmakers that include an AI or machine-learning component has seen an exponential rise. In January 2025, the U.S. Food and Drug Administration (FDA) proposed draft guidelineson the use of artificial intelligence (AI) to assess the safety and effectiveness of drugs.
Relevance of the Topic:Mains: Role of AI in Drug-testing- Applications & Limitations.
Limitations of Conventional Testing & Need for AI
Dependence on animal models:
Pharma industries have traditionally used animal models’ response to various compounds to assess whether a drug can proceed to human clinical trials.
But different populations globally respond differently to drugs and diseases according to age, sex, pre-existing medical conditions, and genetic variabilities etc. This range can not be captured by the response of a homogenous, lab-bred animal population.
E.g., Rats can eliminate some drugs from their bodies much faster than humans. For the same dose level, humans need to be exposed to the drug for a longer duration.
High cost and low success rates:
It takes nearly 10 years and over a billion dollars to develop a drug using conventional (animal-based) processes, which have a success rate of only 14%.
Role of AI in drug development:
Limitations of the AI models
Despite the potential to overcome barriers of conventional testing, AI has its own challenges.
Reliability and bias: Use of biased and/or under-representative data of a target population will also compromise the output. An incorrect prediction could have life-threatening implications.
Lack of Transparency: Algorithms of most AI models are not open to independent scrutiny, nor is the data used to train them easily accessible. Hence the models’ performance can not be assessed as required.
Ethical concerns: There is no legal accountability and responsibility arising out of the decisions made by AI (as AI models can self-learn and adapt over time).
Recent developments in AI-driven drug testing:
US: FDA’s draft guidelines focusses on:
Improving the quality and quantity of data used to train the AI model.
Need to continuously monitor AI models for reliability. Encourage the industry to discuss and design appropriate ways toassess their AI models.
Use of AI in the preclinical stage in particular to assess/approve the safety and effectiveness of a drug (or a compound of interest) before starting human clinical trials.
India: New Drugs and Clinical Trials (Amendment) Rules 2023:
It allowed data generated by advanced computational models to be used to assess the safety and efficacy of new drugs, freeing researchers from relying on animal trials alone.
There is a need for convergence of guidelines issued by global regulators that can help harmonise (i) government policy, (ii) manufacturers’ expectations and compliance burden, (iii) researchers’ strategy, and (iv) consumer safety. This will be crucial for utilising AI to develop a more effective and affordable healthcare system.
Context: Sixty countries, including India, China, Brazil, France, and Australia, signed a joint statement on “Inclusive and Sustainable Artificial Intelligence for Peopleand the Planet” atthe AIAction Summit in Paris, 2025.
Relevance of the topic:
Prelims: AI Action Summit 2025
Mains: AI Regulations- Different approaches, Challenges, Initiatives
AI Action Summit 2025
Held in Paris, France. Co-chaired by India and France.
Focus on: Inclusive and Sustainable AI development.
60 countries signed the joint statement, including India, China, Brazil, France, Canada and Australia.
The United States and United Kingdomwere non-signatories.
Provisions of Joint Statement:
Ensuring accessibility, trust, and safety in AI deployment.
Promoting AI for industrial growth and labour market development.
Encouraging global collaboration in AI innovation.
Building AI capacity in developing countries.
AI should be human-centric, ethical, safe, secure, and trustworthy.
Challenges in global AI governance
Front runners monopoly: US and China are shaping the AI governance discourse, potentially sidelining the specific needs of the Global South.
U.S. stance: Prioritizes AI innovation and deregulation over global ethical frameworks.
Potential conflict: between the European regulatory approach and the U.S. free-market approach.
Different governing models: Diverging AI governance models impact international cooperation on AI standards.
Resource gap: There is a significant resource gap between developed and developing countries, which affects their ability to advance in AI.
India’s Role in AI Governance
Leadership in AI Policy Development:
India’s co-chairing of the AI Action summit highlights its growing global AI leadership.
Active Participation in Global Forums:
India is actively engaged in global platforms like the UN, G-20, and Global Partnership on Artificial Intelligence (GPAI).
India emphasizes equitable AI access and governance for developing countries.
Advocacy for Fair AI Governance:
India has raised concerns about equitable access to AI resources, including data, infrastructure, and knowledge-sharing mechanisms.
Recent Achievements:
India’s leadership in the G-20New Delhi Leaders Declaration and GPAI emphasized fair AI benefits and risk mitigation.
Inclusive Global AI Governance:
India is pushing for an AI governance model that includes marginalized voices from the Global South, focusing on fairness, human rights, and diverse global perspectives.
India’s AI Policies and Initiatives
National Strategy for AI - NITI Aayog’s AI for All:
India’s first comprehensive AI strategy, focusing on social inclusion, innovation, and governance.
