Current Affairs 06 February 2026

  1. Illegal Coal Mining Tragedy in Meghalaya
  2. Cyberchondria and Health Misinformation
  3. Sodium-Ion Battery Technology
  4. Motion of Thanks in Parliament
  5. Artificial Intelligence Racing Ahead of Regulation
  6. India AI Stack


What is Coal Mining ?
  • Coal mining is extraction of coal seams for energy and industry, conducted through open-cast or underground methods, regulated in India by central mining, labour, and environmental laws.
  • Coal remains India’s primary baseload energy source, supporting thermal power, steel production, and cement, making mining economically important but environmentally and socially sensitive.

Relevance

GS-1 (Geography & Society):

  • Mineral geography of North-East, fragile hill ecosystems, humanenvironment interaction in mining regions.
  • Vulnerability of migrant and informal labour in hazardous sectors.

GS-3 (Economy, Environment, Disaster Management):

  • Coal economy vs sustainability trade-offs.
  • Environmental impacts: acid mine drainage, deforestation.
  • Mine disasters and safety regulation.
Definition
  • Rat-hole mining involves digging narrow horizontal tunnels, often barely one metre high, where miners crawl to extract coal manually, common in Meghalaya’s hilly coal-bearing areas.
Why Practised ?
  • Practised due to thin coal seams, private land ownership patterns, low capital requirement, and quick returns, despite severe safety, health, and environmental risks.
Legal Status
  • NGT banned rat-hole mining in 2014, citing environmental damage and unsafe labour conditions, but illegal operations continue due to weak enforcement and local economic dependence.
Meghalaya’s Coal Geology
  • Meghalaya has tertiary coal deposits in fragmented seams within fragile hill ecosystems, making mechanised mining difficult and encouraging small, unsafe, manual extraction methods.
Terrain Constraints
  • Steep slopes, high rainfall, and loose soil increase risks of flooding, tunnel collapse, and landslides, turning unscientific mining sites into high-risk zones for workers.
Mine Safety Basics
  • Scientific mining requires ventilation, structural supports, gas monitoring, and emergency exits, which are usually absent in illegal rat-hole mines, raising accident probability.
Labour Profile
  • Workers often include migrant and economically vulnerable populations, accepting hazardous conditions due to limited livelihood options and informal employment arrangements.
Use of Explosives
  • Use of dynamite or explosives in unregulated settings increases risks of blasts, toxic fumes, and tunnel instability, especially without certified handlers or safety protocols.
Land and Forests
  • Unregulated mining causes deforestation, soil erosion, and landscape degradation, permanently altering fragile hill ecosystems and reducing ecological stability.
Water Pollution
  • Coal mining generates acid mine drainage, contaminating rivers with heavy metals and acidity, harming aquatic life and affecting downstream communities’ water quality.
Constitutional Position
  • Mining and mineral development fall under Union regulation (MMDR Act), but land and local enforcement involve States, requiring coordinated governance for effective control.
Enforcement Challenges
  • Illegal mining persists due to monitoring gaps, local political economy, difficult terrain, and livelihood dependence, weakening regulatory effectiveness despite formal bans.
Response Framework
  • Rescue operations involve State Disaster Response Force, police, and medical teams, focusing on evacuation, medical aid, and site stabilisation in hazardous underground conditions.
Preventive Approach
  • Prevention requires strict licensing, regular inspections, worker registration, and closure of illegal mines, alongside alternative livelihoods to reduce economic reliance on unsafe mining.
Development vs Safety
  • The tragedy highlights conflict between livelihood needs and human safety, where economic desperation often pushes workers into life-threatening informal sectors.
State Responsibility
  • Welfare state principles require government to ensure safe working conditions, environmental protection, and sustainable livelihoods, not merely post-disaster compensation.


Triggering Incident
  • A filicide case in Bhilwara, Rajasthan, where a mother killed her children fearing terminal illness after consuming online medical misinformation, highlighted extreme consequences of unchecked digital health content.
Broader Relevance
  • With 1+ billion Internet subscriptions in India, social media has become a major health information source, raising concerns about misinformation-driven anxiety, self-diagnosis, and erosion of trust in medical systems.

Relevance

GS-2 (Governance & Social Sector):

  • Public health communication, digital governance, platform regulation.
  • Mental health as policy concern.

GS-3 (Science & Tech):

  • Algorithmic amplification, AI-driven recommendation systems.
  • Digital literacy and misinformation.
Definition
  • Cyberchondria refers to excessive, anxiety-driven online health searches where individuals repeatedly seek medical information online, leading to heightened fear of serious illness despite limited clinical evidence.
Origin of Term
  • The term combines cyber” (digital space) and hypochondria” (illness-anxiety disorder), indicating technology-amplified health anxiety rather than a new psychiatric disorder category.
Clinical Nature
  • Considered a behavioural and cognitive pattern linked to health anxiety, obsessive checking, and reassurance-seeking, sometimes overlapping with anxiety or obsessive-compulsive spectrum conditions.
Traditional Hypochondria
  • Hypochondria involves persistent fear of illness despite medical reassurance, traditionally triggered by bodily sensations, media reports, or anecdotal experiences, even before the Internet era.
Digital Amplification
  • Cyberchondria intensifies these fears because search engines and social media provide vast, decontextualised medical information, often highlighting worst-case scenarios.
Recommendation Systems
  • Social media algorithms prioritise engagement-based content, promoting sensational or fear-inducing health videos because they generate longer watch time and user interaction.
Personalisation Loops
  • AI-driven feeds track pauses, clicks, and watch duration, then recommend similar content, creating echo chambers that repeatedly expose users to alarming medical claims.
Engagement Bias
  • Research shows misleading medical content often achieves higher engagement than accurate information, making algorithms unintentionally amplify misinformation.
What is Medical Misinformation ?
  • Medical misinformation is false, misleading, or unverified health-related information presented without scientific consensus, often simplified to appear authoritative or relatable.
Source Patterns
  • A large share of misleading health content is produced by non-professionals, influencers, or anecdotal storytellers rather than certified medical practitioners.
Limits of Online Diagnosis
  • Online searches cannot replace clinical examination, patient history, and diagnostic testing, which doctors use to differentiate between common symptoms and serious disease.
Anxiety Spiral
  • Since many symptoms overlap across diseases, search results often highlight severe illnesses like cancer, triggering catastrophic thinking in vulnerable individuals.
Conspiratorial Thinking
  • When institutions like medicine feel like “black boxes,” people may turn to simplified or conspiratorial explanations, which provide psychological comfort and perceived control.
Authority Bias
  • People tend to trust information that appears authoritative online, even if credibility is weak, making them vulnerable to persuasive but inaccurate medical claims.
Digital Health Literacy
  • Low health and digital literacy limits people’s ability to evaluate sources, understand probabilities, or distinguish correlation from causation in medical claims.
Platform Responsibility
  • Platforms have misinformation policies, but enforcement is inconsistent; algorithms are designed for engagement, not public health outcomes.
Mental Health Impact
  • Cyberchondria can increase anxiety, stress, unnecessary medical visits, and mistrust in doctors, burdening both individuals and healthcare systems.
Family and Social Consequences
  • Extreme anxiety-driven decisions can affect families and children, showing misinformation is not only informational risk but also a social and ethical concern.
Responsible Health Seeking
  • Verified medical sources, second opinions, and consultation with qualified doctors are essential to counterbalance algorithm-driven misinformation exposure.
Role of Awareness
  • Public awareness campaigns on digital health literacy and mental health can reduce vulnerability to misinformation-driven panic.


Strategic Debate in India
  • India is reassessing battery strategy due to import dependence and critical mineral risks in lithium-ion, with sodium-ion emerging as a viable alternative for energy storage and EV transition.
Energy Transition Relevance
  • As batteries underpin EVs, grid storage, and digital devices, technology choice directly affects India’s energy security, manufacturing self-reliance, and clean energy transition goals.

Relevance

GS-3 (Science & Tech, Economy, Environment):

  • Energy storage innovation, battery chemistry.
  • Critical minerals dependency and supply-chain resilience.
  • Clean energy transition and EV ecosystem.
What is a Battery ?
  • A battery is an electrochemical device that stores energy through reversible chemical reactions, converting chemical energy into electrical energy via movement of ions between electrodes.
Key Components
  • Every battery contains anode, cathode, electrolyte, and current collectors, which together enable ion flow internally and electron flow through an external circuit.

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Working Principle
  • Lithium-ion batteries function by lithium ions shuttling between graphite anode and metal-oxide cathode, offering high energy density and long cycle life.
Strengths
  • High energy density, low self-discharge, and mature manufacturing ecosystem made Li-ion dominant in EVs and electronics globally.
Structural Constraints
  • Li-ion depends on lithium, cobalt, nickel, and graphite, minerals concentrated in few countries, creating supply, price, and geopolitical vulnerabilities.
What is Sodium-Ion Technology ?
  • Sodium-ion batteries operate similarly to Li-ion but use sodium ions as charge carriers, with sodium sourced from abundant materials like soda ash and salt deposits.
Material Advantage
  • Sodium is abundant, geographically diversified, and low-cost, reducing critical mineral dependence and exposure to global commodity volatility.
Current Collectors
  • Na-ion uses aluminium for both electrodes, unlike Li-ion which needs copper on anode, lowering cost, weight, and corrosion-related risks.
Specific Energy (Wh/kg)
  • Specific energy measures energy stored per unit mass; Na-ion is lower because sodium atoms are heavier than lithium, affecting weight-to-energy ratio.
Practical Gap
  • Performance gap narrows when cell design optimises other componentsweight, and some Na-ion chemistries approach lithium iron phosphate (LFP) levels.
Thermal Stability
  • Sodium-ion cells show lower peak temperatures during thermal runaway, reducing fire and explosion risks compared to conventional lithium-ion cells.
Transport Safety
  • Li-ion is classified as Dangerous Goods requiring charge limits during transport, while Na-ion can be stored at zero volts safely without degradation.
Production Lines
  • Existing lithium-ion factories can be adapted for sodium-ion with minor changes, lowering capital barriers and enabling dual-chemistry production flexibility.
Moisture Sensitivity
  • Na-ion requires deeper vacuum drying during production because residual moisture affects performance more strongly than in lithium-ion cells.
Capacity Trends
  • Around 70 GWh Na-ion capacity exists globally (2025), projected to reach nearly 400 GWh by 2030, indicating commercial-scale momentum.
Cost Outlook
  • Long-term projections indicate Na-ion could undercut Li-ion costs by 2035, especially for stationary storage and low-range mobility segments.
PLI Scheme
  • India’s PLI for Advanced Chemistry Cells (2021) allocated ~40 GWh capacity but is currently lithium-focused, with limited upstream mineral processing ecosystem.
Import Dependence
  • Limited domestic lithium reserves and refining capacity mean continued import reliance, increasing strategic vulnerability.
Best Use Cases
  • Sodium-ion suits grid storage, two- and three-wheelers, and stationary applications, where cost, safety, and cycle life matter more than ultra-high energy density.
Energy Security
  • Sodium-based systems reduce reliance on imported critical minerals, strengthening long-term supply chain resilience.
Industrial Opportunity
  • Early adoption can help India build domestic battery manufacturing ecosystem, avoiding late-entry disadvantage seen in lithium-ion sector.
Policy Support
  • Technology-neutral incentives, R&D funding, and standards recognition can support diversified battery ecosystem.
Ecosystem Development
  • Developing domestic materials, components, and recycling infrastructure is key for long-term sustainability.


Recent Instance
  • Lok Sabha passed the Motion of Thanks on the Presidents Address amid Opposition protests and adjournments, with debate continuing despite Prime Minister’s absence during part of the discussion.
Procedural Significance
  • The episode renewed attention on parliamentary conventions, executive accountability, and rules governing the Motion of Thanks, a key constitutional practice in India’s Parliament.

Relevance

GS-2 (Polity):

  • Article 87, parliamentary procedures, executive accountability.
  • Role of Speaker, conventions vs rules, deliberative democracy.
Article 87
  • Article 87 of the Constitution mandates the President to address both Houses at the first session after each general election and at the first session each year.
Purpose of Address
  • The address outlines the governments policies, priorities, and legislative agenda, functioning as a statement of intent by the executive to Parliament.
What is Motion of Thanks ?
  • Motion of Thanks is a formal parliamentary motion moved in each House to thank the President for the Address and discuss its contents.
Nature of Discussion
  • Debate allows MPs to critique government policies, omissions, and achievements, making it one of the widest-ranging discussions in Parliament.
Moving and Seconding
  • The motion is moved and seconded by ruling party MPs, after which members across parties participate in debate and propose amendments.
Amendments
  • MPs may move amendments highlighting policy failures or omissions; adoption of an amendment symbolically signals political disapproval of government.
Prime Minister’s Reply
  • Conventionally, the Prime Minister replies to the debate, addressing issues raised; this reply represents the government’s official response.
Confidence Dimension
  • Though not formally a no-confidence motion, defeat of Motion of Thanks is seen as serious political setback indicating loss of majority support.
Accountability Tool
  • Provides early-session platform for executive accountability, allowing Parliament to review government’s agenda.
Speaker’s Authority
  • Speaker regulates proceedings, maintains order, and may adjourn House during disorder, ensuring decorum under Rules of Procedure.
Parliamentary Privilege
  • Disruptions, slogan-shouting, or entering the Well of the House may be treated as breach of decorum and privilege, though political protests are common.
Conventions
  • PM’s presence during debate and reply is a strong convention, but Constitution does not legally mandate continuous presence during entire discussion.
Democratic Norms
  • Parliamentary democracy relies on mutual respect, debate, and dissent, not only numerical majority.
Westminster Model
  • Motion of Thanks originates from British parliamentary practice, where monarch’s speech is similarly debated.
Deliberative Democracy
  • Motion of Thanks embodies deliberative democracy, enabling comprehensive policy review at start of parliamentary year.
Opposition’s Role
  • Opposition uses debate to highlight governance gaps and represent alternative viewpoints, strengthening democratic scrutiny.


Global Governance Push
  • The United Nations announced an Independent International Scientific Panel on AI to guide global governance, reflecting rising concern over AI’s cross-border risks and uneven national regulations.
Technological Leap
  • Simultaneously, emergence of bot-only platforms like Moltbook, where AI agents interact without humans, signals rapid evolution of autonomous digital ecosystems beyond traditional regulatory control.

Relevance

GS-3 (Science & Tech):

  • AI governance, emerging tech regulation, AI agents, deepfakes.

GS-2 (IR & Governance):

  • UN-led global governance, multilateral norm-setting.
  • Tech geopolitics and AI race.
What is AI ?
  • Artificial Intelligence refers to computer systems performing tasks requiring human intelligence, including learning, reasoning, language processing, perception, and decision-making.
Core Subfields
  • AI includes machine learning, deep learning, natural language processing, and computer vision, which enable pattern recognition and adaptive performance from data.
Meaning
  • AI governance involves laws, policies, standards, and ethical norms guiding AI development and deployment to ensure safety, fairness, accountability, and transparency.
Why Needed ?
  • Because AI affects economies, elections, security, and rights, unregulated systems can produce large-scale societal harm or cross-border externalities.
UN Role
  • The UN acts as a multilateral platform for norm-setting, similar to climate or nuclear governance, aiming for shared principles rather than binding global AI laws.
Pact for the Future
  • The panel is mandated under the UNs Pact for the Future, focusing on science-based advice for global public goods and emerging technologies.
Geopolitical Competition
  • Countries view AI as strategic infrastructure influencing economic power, military capability, and technological leadership, intensifying global competition.
Investment Surge
  • Massive public and private investments in AI reflect its role in productivity growth, digital economy, and national security systems.
Misinformation
  • Generative AI can create deepfakes, synthetic media, and automated propaganda, complicating information integrity and democratic processes.
Labour Disruption
  • Automation threatens routine cognitive and manual jobs, creating transitional unemployment and skill mismatches.
Surveillance
  • AI-powered analytics enable mass surveillance and profiling, raising civil liberty and privacy concerns.
Bias and Ethics
  • Algorithms trained on biased data can produce discriminatory outcomes, affecting fairness in hiring, lending, and policing.
What are AI Agents ?
  • AI agents are autonomous software entities capable of perceiving environments, making decisions, and performing tasks with minimal human intervention.
Functional Scope
  • They handle tasks like document drafting, data analysis, scheduling, and system coordination, increasingly acting as digital assistants.
Concept
  • Bot-only platforms allow AI-to-AI communication, where agents post, evaluate, and respond to each other without human participation.
Significance
  • Such spaces test how AI systems behave collectively, raising questions about control, accountability, and emergent behaviours.
Pace Mismatch
  • Technology evolves faster than law-making because policy processes require consensus, consultation, and legislative cycles, while AI innovation is market-driven and rapid.
Jurisdiction Limits
  • Digital systems operate across borders, making national regulations insufficient for global AI platforms.
Human Oversight
  • Ethical AI emphasises human-in-the-loop decision-making, ensuring accountability and value alignment.
Digital Autonomy Risks
  • Fully autonomous systems risk reduced human control and opaque decision chains, challenging traditional liability frameworks.
Multi-Stakeholder Governance
  • Effective governance requires cooperation among states, industry, academia, and civil society.
Principle-Based Regulation
  • Safety, transparency, accountability, and fairness can serve as core guiding principles even amid rapid innovation.


Policy Push
  • India is advancing a population-scale AI Stack under the IndiaAI Mission, integrating data, models, compute, infrastructure, and energy to democratise AI and reduce dependence on foreign ecosystems.
Development Significance
  • The AI stack approach positions AI as public digital infrastructure, similar to Aadhaar or UPI, aiming to deliver inclusive, sovereign, and scalable AI-led development.

Relevance

GS-3 (Science & Tech, Economy):

  • Digital public infrastructure, AI ecosystem, semiconductor push, compute capacity.
  • AI for agriculture, health, governance.

GS-2 (Governance):

  • IndiaAI Mission, digital sovereignty, inclusive tech policy.
What is an AI Stack ?
  • An AI stack is the end-to-end ecosystem of technologies and infrastructure required to build, train, deploy, and scale AI applications from data collection to user delivery.
Purpose
  • It ensures AI systems are scalable, reliable, and deployable at population level, converting research innovations into real-world services across sectors.
Meaning
  • The application layer includes user-facing AI services such as chatbots, diagnostics tools, translation apps, and advisory platforms that convert AI capability into usable solutions.
Agriculture Use
  • AI advisories support crop planning, pest control, and input optimisation, with state deployments reporting productivity gains of 30–50%, improving farm incomes and resource efficiency.
Healthcare Use
  • AI supports early detection of TB, cancers, and neurological disorders, strengthening preventive healthcare and reducing diagnostic delays in resource-constrained regions.
Education Use
  • AI integration through NEP 2020, DIKSHA, and YUVAi promotes digital and AI literacy, preparing students for future technology-driven labour markets.
Governance Use
  • AI in e-Courts Phase III and IMD forecasting improves translation, case management, and disaster prediction, enhancing transparency and citizen service delivery.
Meaning
  • The model layer is the core intelligence layer, where algorithms learn patterns from data to generate predictions, language processing, recognition, and decision support.
Sovereign Models
  • India is developing indigenous foundation and multimodal models to ensure cultural, linguistic, and policy alignment rather than relying solely on foreign-trained models.
IndiaAIKosh
  • IndiaAIKosh hosts 5,700+ datasets and 250+ models, serving as national AI repository to support startups, research, and public-sector innovation.
Language Inclusion
  • Platforms like Bhashini and Sarvam AI strengthen Indian-language AI, enabling voice interfaces and multilingual governance services in a linguistically diverse country.
Meaning
  • Compute layer provides high-performance processing power needed to train large AI models using GPUs, TPUs, and specialised AI chips.
IndiaAI Compute
  • The IndiaAI Compute Portal offers 38,000 GPUs and 1,050 TPUs at subsidised rates, lowering entry barriers for startups and academic institutions.
Supercomputing
  • Systems like PARAM Siddhi-AI and AIRAWAT support NLP, climate modelling, and drug discovery, strengthening domestic research capacity.
Semiconductor Push
  • The ₹76,000 crore Semiconductor Mission and indigenous processors like SHAKTI and VEGA aim to build long-term hardware self-reliance.
Meaning
  • This layer includes data centres, broadband, fibre networks, and 5G, enabling fast data transfer and reliable AI deployment.
Connectivity Scale
  • 5G covers 99.9% districts and 85% population, supporting real-time AI services and IoT-based applications.
Data Centre Capacity
  • India holds ~960 MW capacity (3% global share), projected to reach 9.2 GW by 2030, reflecting AI-driven infrastructure growth.
Investment Momentum
  • Large investments by global firms in Indian data centres strengthen digital sovereignty and domestic hosting of AI workloads.
Meaning
  • AI systems require continuous, high-volume electricity, making energy reliability and affordability critical for AI scaling.
Power Availability
  • India’s installed capacity exceeds 500 GW, with energy shortages at only 0.03%, ensuring reliable power for data centres.
Clean Energy Link
  • Over 51% capacity from non-fossil sources aligns AI growth with climate commitments and sustainable development.
Grid Stability
  • Pumped storage and battery systems enhance grid flexibility, supporting AI centres operating alongside renewable energy variability.
Digital Sovereignty
  • A domestic AI stack reduces reliance on foreign platforms, ensuring data control, regulatory alignment, and strategic autonomy.
Inclusive Growth
  • Population-scale AI enables targeted welfare delivery, productivity gains, and service efficiency, supporting inclusive development.
Ecosystem Integration
  • Success requires coordination across policy, research, industry, and energy sectors to prevent siloed AI growth.
Responsible AI
  • Ethical safeguards, data protection, and transparency are essential to maintain public trust and fairness in AI deployment.

February 2026
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