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GS Paper: E-Governance

  • Government to tighten AI labelling rules for social media over ‘unsatisfactory compliance’

    Why in the News?

    The governmentā€™s decision to tighten AI labelling rules marks a clear step-up in digital regulation, triggered by poor compliance from platforms like YouTube, Instagram, and X. Earlier, platforms only needed to show ā€œprominentā€ labels, but now they must display continuous and clearly visible labels throughout the content, making the rules much stricter. This change is important because cases of harmful AI content, such as deepfake images of women created by Xā€™s Grok, have exposed serious gaps in regulation, raising concerns about privacy, dignity, and large-scale misinformation.

    What are the AI Content labelling rules for social media?

    1. The Government of India has notified the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Amendment Rules, 2026 (effective February 20, 2026), making AI content labelling mandatory on social media platforms. These rules are designed to curb the spread of deepfakes, misinformation, and non-consensual sexual content (CSAM).
    2. AI content labelling on social media is the mandatory or voluntary tagging of images, videos, and audio created or altered by artificial intelligence (AI) to distinguish them from human-made content.Ā 
    3. It aims to increase transparency, reduce misinformation (deepfakes), and comply with regulations by using visible labels (e.g., “AI-generated”) or hidden metadata.

    Key Features of the Amended IT Rules (2026):

    1. Mandatory Labelling: Social media platforms must prominently label “synthetically generated” or AI-generated images and videos that appear realistic.
    2. User Declaration: Platforms with over five million users must obtain a user declaration for AI-generated content and conduct technical verification before publishing.
    3. Excluded Content: Routine smartphone photo editing, filters, and film special effects are exempt from mandatory labelling.
    4. Permanent Metadata: Platforms must try to embed permanent metadata or watermarks to trace the origin of AI content.
    5. Takedown Timelines:
      1. 2 hours: Non-consensual deepfakes and intimate imagery must be removed within 2 hours of a complaint.
      2. 3 hours: Other illegal content must be removed within 3 hours of a court/government order.
    6. Loss of Safe Harbour: Non-compliance with these rules can result in the loss of safe harbour protection under Section 79 of the IT Act, making platforms liable for the content.

    Key Proposed AI Labeling Amendments (April 2026) and how do the proposed amendments strengthen accountability of intermediaries?

    1. Continuous On-Screen Labels: The new proposal mandates that AI labels remain continuously and clearly visible throughout the entire duration of the video or audio content, rather than just in the beginning or occasionally.
    2. Expansion of Scope: The labeling requirement applies to “synthetically generated information” (SGI), which includes text, audio, images, and videos created or altered via AI to appear authentic.
    3. Platform Accountability: Social media intermediaries must ensure these labels are present. Failure to comply could lead to a loss of “safe harbour” protection, meaning platforms could be held liable for user-generated content.
    4. User Responsibilities: Users are required to declare if content is AI-generated upon uploading, which platforms must then verify using “reasonable and proportionate technical measures“.
    5. Stricter Takedown Timelines: The proposal includes a heavily reduced takedown timeline, requiring platforms to remove illegal, non-consensual deepfakes within 2 to 3 hours of a lawful order.
    6. Feedback Deadline Extended: The deadline for public feedback on these proposed changes has been extended to May 7, 2026.Ā 

    These moves, which follow initial rules announced in February 2026, are designed to combat the rising misuse of deepfakes and misinformation, ensuring that AI-generated material is easily distinguishable from real content

    What regulatory gap prompted stricter AI labelling norms?

    The primary regulatory gap that prompted stricter AI labelling norms was the transition from a standard of “prominent visibility” to a mandate for “continuous and clearly visible display” throughout the entire duration of the content. 

    1. Unsatisfactory compliance: Social media platforms failed to ensure consistent labelling despite February notification. For instance, only about 30% of AI-generated test posts were correctly flagged across major platforms.
    2. Inconsistent visibility: Labels appeared briefly or were not prominently displayed throughout content duration.
      1. Under earlier guidelines, AI labels often appeared only briefly or were placed in a way that was easily missed by users. The new 2026 amendments specifically aim to eliminate “blink-and-miss” disclaimers by requiring the label to remain on screen from start to finish.
    3. Regulatory dilution: Earlier proposal mandating labels to occupy 10% space was diluted, reducing effectiveness.
    4. Traceability Gaps: To prevent the removal of disclosures, the new norms mandate embedding permanent metadata or unique identifiers into synthetic content to ensure it remains traceable even when shared.Ā 

    What is the significance of redefining Synthetic Generated Information (SGI)?

    Redefining Synthetically Generated Information (SGI) under Indiaā€™s IT Rules 2026 is significant because it shifts from a reactive, general content moderation model to a proactive, AI-specific regulatory framework.

    1. Definition of SGI (Feb 2026 Rules): Refers to information created, modified, or generated using AI tools that can mimic real persons, events, or content.
      1. Includes deepfakes, AI-generated videos, audio, images, or text that appear real.
      2. Focuses on content that can mislead users or distort reality.
    2. Scope in February 2026 Rules:
      1. Broad coverage: Any AI-generated content that resembles real-world entities.
      2. Mandatory labelling: Required ā€œprominentā€ disclosure, but no clarity on duration or format.
      3. Carve-outs included: Routine editing (filters, enhancement, dubbing) excluded as ā€œgood-faith useā€.

    What changes in the Proposed New Rules?

    1. Stricter visibility requirement:
      1. Continuous and clearly visible labelling throughout the content duration.
      2. Removes ambiguity of ā€œprominentā€ labels.
    2. Sharper focus on harm:
      1. Targets SGI that violates laws or leads to misrepresentation of identity/events.
      2. Expands regulatory intent from disclosure for the prevention of misuse.
    3. Platform accountability strengthened:
      1. Requires verification of user declarations about SGI.
      2. Mandates technical safeguards to detect and prevent harmful SGI.
    4. Enforcement mechanism: Platforms must take immediate action (remove, disable access, suspend accounts) upon detection.

    Why is this significant?

    1. Clear classification: Defines AI-generated content as SGI, ensuring regulatory clarity.
    2. Carve-outs provision: Excludes routine and good-faith editing (audio/video enhancement) from SGI definition.
    3. Misrepresentation control: Targets content that violates laws or misrepresents real-world events or identities.

    What risks associated with AI-generated content triggered regulatory urgency?

    1. Deepfake misuse: Grok-generated images of women in revealing clothing raised dignity and privacy concerns.
    2. Misinformation threat: AI content risks distorting facts and influencing public perception.
    3. Identity manipulation: Enables impersonation and false representation of individuals.
    4. Global backlash: Incident led to bans in some countries and forced platform-level corrective measures.

    How does the amendment impact Big Tech platforms?

    1. Enhanced compliance burden: Requires continuous monitoring and enforcement mechanisms.
    2. Liability exposure: Failure to act may attract legal consequences under IT Rules.
    3. User accountability integration: Platforms must ensure users disclose AI-generated content.
    4. Content moderation expansion: Strengthens obligations for proactive detection and removal.

    What are the implications for digital governance in India?

    1. Regulatory evolution: Moves from reactive to proactive AI governance.
    2. Platform responsibility shift: Transfers greater accountability to intermediaries.
    3. Rights protection: Strengthens safeguards for privacy, dignity, and authenticity.
    4. Policy alignment: Aligns with global concerns on AI ethics and misinformation control.

    Conclusion

    The proposed amendments signal a decisive shift towards stricter AI governance, emphasizing transparency and accountability. Effective implementation will determine whether India can balance innovation with safeguards against misinformation and digital harm.

    PYQ Relevance

    [UPSC 2024] Social media and encrypting messaging services pose a serious security challenge. What measures have been adopted at various levels to address the security implications of social media? Also suggest any other remedies to address the problem.

    Linkage: AI labelling rules and SGI regulation fall under GS-3 (Cyber Security, Emerging Technologies), focusing on risks like deepfakes, misinformation, and platform accountability. They also link to GS-2 (Governance) through regulation of intermediaries and GS-4 (Ethics) via concerns of privacy, dignity, and responsible AI use.

  • AI Labelling RulesĀ Ā 

    Why in the News?

    • The Ministry of Electronics and Information Technology proposed stricter AI content labelling norms due to unsatisfactory compliance by social media platforms.

    Key Change

    • Under Information Technology Rules 2021:
      • AI-generated content must have:
      • Continuous and clearly visible labels
      • Displayed for the entire duration of content

    Scope

    • Applies to platforms like: YouTube, Instagram, and X

    Key Term

    • Synthetically Generated Information (SGI):
      • Includes AI-generated audio, video, images
      • Excludes routine editing and quality enhancement

    Platform Obligations

    • Ensure proper labelling
    • Require user disclosure of AI content
    • Remove unlawful content
    • Use safeguards to prevent misuse

    Significance

    • Enhances transparency
    • Reduces misinformation and deepfakes
    • Strengthens digital platform accountability
    [2025] Consider the following statements regarding Al Action Summit held in Grand Palais, Paris in February 2025:Ā 
    I. Co-chaired with India, the event builds on the advances made at the Bletchley Park Summit held in 2023 and the Seoul Summit held in 2024.Ā 
    II. Along with other countries, US and UK also signed the declaration on inclusive and sustainable AI.Ā 
    Which of the statements given above is/are correct?Ā 
    [A] I only [B] II only [C] Both I and II [D] Neither I nor II

  • e-SafeHER ProgrammeĀ Ā 

    Why in the News?

    • The Ministry of Electronics and Information Technology launched e-SafeHER, a large-scale cybersecurity training initiative aimed at empowering 1 million rural women.

    About e-SafeHER Programme

    • A cybersecurity awareness and training programme for rural women
    • Operates under: Information Security Education and Awareness Programme
    • Focus: Bridging gap between digital access and digital safety

    Aim

    • Train 1 million women by 2029
    • Promote safe digital participation
    • Strengthen cybersecurity awareness in: Digital payments and Online livelihoods
    [2017] In India, it is legally mandatory for which of the following to report on cyber security incidents?
    1 Service providersĀ 
    2 Data CentresĀ 
    3 Body corporateĀ 
    Select the correct answer using the code given below:Ā 
    (a) 1 only (b) 1 and 2 only (c) 3 only (d) 1, 2 and 3

  • Understanding India’s internet censorship regime

    Why in the News?

    A recent study testing 294 million domains across six major Internet Service Providers (ISPs) in 2025 reveals significant inconsistencies in website blocking. Despite receiving identical blocking orders, ISPs do not block the same domains. Out of 43,083 blocked domains, only 1,414 were uniformly blocked, highlighting a fragmented censorship regime. This is a major concern because it demonstrates that internet censorship in India is not centrally uniform but ISP-dependent, marking a shift from the assumption of standardised enforcement.

    How does Indiaā€™s legal framework enable internet censorship?

    Indiaā€™s legal framework enables internet censorship primarily through broad executive powers granted by the Information Technology Act of 2000 (IT Act), supported by constitutional, penal, and procedural regulations that prioritize national security and public order.

    1. Information Technology Act, 2000 (ITA): The IT Act is the primary legislation used for digital censorship.
      1. Section 69A: Empowered by the 2008 amendment, this section allows the central government to issue directives to block public access to any information online. Grounds include the interest of sovereignty, integrity, defense of India, security of the state, or public order.
      2. IT Blocking Rules, 2009: These govern the process of Section 69A, allowing for confidential takedown orders, which often lack transparency, limiting the ability of content creators to challenge them.
      3. Section 79 (3)(b): This section dictates that “intermediaries” (like ISPs, search engines, and social media sites) must remove content upon receiving “actual knowledge” or being notified by the government that their platform is being used for unlawful acts. Failure to comply can lead to a loss of “safe harbor” protection, making them liable for user content.
    2. IT (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021: These rules significantly tightened control over online content.
      1. Content Takedown Timelines: Intermediaries must remove “unlawful” content within set timeframes (often within 36 hours, but tighter for specific content) after receiving a complaint or government notice.
      2. Mandatory Grievance Redressal: Platforms must establish an internal mechanism to handle complaints, strengthening the government’s ability to demand removal.
      3. Expedited Removal for Specific Content: Recent amendments (as of 2026) have proposed removing content within as little as three hours.
      4. Traceability Requirement: The rules require messaging platforms to be able to identify the “first originator” of a message, raising privacy concerns.
    3. Licensing Conditions under Telecom Regulatory Framework:
      1. Binding Obligations: Requires ISPs to comply with directions issued by the Department of Telecommunications (DoT) and other competent authorities
      2. Enforcement Mechanism: Non-compliance can lead to penalties, suspension, or cancellation of licenses
      3. Operational Impact: Ensures that censorship orders are effectively implemented at the network level.
      4. Example: ISPs blocking specific domains or services following government directives during security situations.
    4. Confidentiality Clause in Blocking Rules (2009):
      1. Secrecy of Orders: Mandates strict confidentiality regarding blocking requests and directions.
      2. Transparency Deficit: Prevents public disclosure of reasons, scope, and number of blocked websites.
      3. Accountability Constraint: Limits scope for judicial review, public scrutiny, and informed debate.
      4. Example: Users are often unaware why a particular website is inaccessible, as blocking orders are not publicly available. 

    Why does censorship vary across ISPs despite identical orders?

    1. Non-uniform Implementation: ISPs interpret and execute government blocking orders differently based on internal protocols, leading to variation in outcomes.
    2. Technical Discretion: ISPs choose different blocking techniques such as DNS, HTTP, or TLS filtering depending on their technical setup and preferences.
    3. Operational Constraints: Variations in infrastructure capacity, technical expertise, and financial resources influence how effectively orders are implemented.
    4. Compliance Prioritisation: ISPs differ in urgency and strictness while implementing orders, causing delays or partial enforcement.
    5. Lack of Standardisation: Absence of uniform technical guidelines results in fragmented enforcement across networks.

    What technical mechanisms are used for website blocking?

    1. DNS Blocking: Redirects domain queries to false or incorrect IP addresses through DNS poisoning, preventing access at the resolution stage. Example: Access request to example.com gets redirected to an incorrect or null IP address.
    2. HTTP Blocking: Restricts access at the application layer by intercepting HTTP requests and returning error or denial responses.
    3. TLS Blocking: Interferes with encrypted HTTPS connections by blocking or disrupting secure handshakes.
    4. IP Blocking: Blocks specific IP addresses hosting content, restricting access at the network layer.
    5. Key Insight: Most Indian ISPs rely primarily on DNS blocking due to its low cost, ease of deployment, and minimal infrastructure requirements.

    What does the empirical data reveal about the scale of censorship?

    1. 294 Million Domains Tested: Large-scale testing conducted across six major ISPs in 2025 to assess censorship patterns.
    2. 43,083 Domains Blocked: Indicates significant extent of content restriction across networks.
    3. Only 1,414 Commonly Blocked: Demonstrates that very few domains are uniformly blocked across all ISPs.
    4. Inter-ISP Variation: Same blocking orders result in different lists of blocked websites across providers.
    5. Inference: Internet censorship in India operates in a fragmented, inconsistent, and decentralised manner rather than a uniform system.

    What are the implications for users and digital rights?

    1. Unequal Access: Same website may be accessible on one ISP but blocked on another, leading to inconsistent user experience.
    2. Opacity: Users remain unaware of blocking reasons due to confidentiality of government orders and lack of disclosures.
    3. Freedom of Expression: Arbitrary and inconsistent restrictions weaken the protection under Article 19(1)(a).
    4. Accountability Gap: Limited transparency reduces scope for judicial review and public oversight.
    5. Chilling Effect: Uncertainty about access may discourage users from engaging with certain online content.

    Why is DNS blocking problematic as a primary tool?

    DNS (Domain Name System) is the “phonebook of the internet,” translating human-friendly domain names (like example.com) into machine-readable IP addresses (like 192.0.2.1). This system allows users to access websites using memorable names instead of complex numerical addresses, acting as a crucial intermediary for web browsers to find and connect to servers.

    1. Low Precision: Blocks entire domains instead of targeting specific unlawful content, leading to overblocking.
    2. Circumvention Risk: Easily bypassed using VPNs, proxy servers, or alternative DNS services.
    3. Security Risks: DNS poisoning may redirect users to malicious or unintended websites, compromising safety.
    4. Lack of Effectiveness: Ineffective against dynamic or mirror websites that frequently change domains.
    5. Over-Reliance: Excessive dependence on DNS blocking reflects technological limitations in implementing more precise methods. 

    Conclusion

    Indiaā€™s internet censorship regime reflects legal backing but weak procedural uniformity and transparency. Addressing these gaps requires standardised implementation, greater accountability, and judicial oversight to balance state interests with fundamental rights.

    PYQ Relevance

    [UPSC 2013] Discuss Section 66A of IT Act, with reference to its alleged violation of Article 19 of the Constitution.

    Linkage: The PYQ Examines limits of state power over online speech under Article 19(1)(a) and safeguards against arbitrary censorship. Similar to Section 66A concerns, the current internet censorship regime (Section 69A, ISP blocking) raises issues of overreach, opacity, and disproportionate restrictions on digital expression.

  • Home Ministry Sends 290 Takedown Notices Daily

    Why in News

    Union Home Ministry issued average 290 online content takedown notices per day under Information Technology Act 2000, indicating rise in online regulation and cybersecurity threats.

    Key Highlights

    ā€¢ 1,11,185 suspicious online content blocked in 2024 to 25
    ā€¢ 290 takedown notices per day issued by Home Ministry
    ā€¢ Social media platforms must remove content within 3 hours
    ā€¢ Sharp rise in cybersecurity incidents reported

    Legal Provision

    Section 79 of Information Technology Act 2000

    Section 79(1)

    ā€¢ Provides Safe Harbour Protection
    ā€¢ Platforms not liable for user generated content

    Section 79(3)(b)

    ā€¢ Safe harbour removed if
    ā€¢ Platform fails to remove flagged unlawful content
    ā€¢ Government can issue takedown notices

    Nodal Agency

    Indian Cyber Crime Coordination Centre I4C

    ā€¢ Designated on March 13, 2024
    ā€¢ Empowered to issue takedown notices
    ā€¢ Functions under Ministry of Home Affairs

    Sahyog Portal

    ā€¢ Centralised portal for sending takedown notices
    ā€¢ Police agencies across India can issue requests
    ā€¢ Used to coordinate with social media platforms

    3 Hour Rule

    Social media platforms must Remove unlawful content Within 3 hours of receiving order. Order may come from Court, Government agency, and Law enforcement

    Rise in Cybersecurity Incidents

    • According to CERT In
      • 2021: 14.02 lakh incidents
      • 2022: 13.91 lakh incidents
      • 2023: 15.92 lakh incidents
      • 2024: 20.41 lakh incidents
      • 2025: 29.44 lakh incidents
    • Highest incidents reported from National Capital Territory of Delhi

    What is CERT In

    Indian Computer Emergency Response Team

    ā€¢ National cybersecurity agency
    ā€¢ Established under Section 70B of IT Act 2000
    ā€¢ Functions under Ministry of Electronics and IT

    Functions
    ā€¢ Track cyber threats
    ā€¢ Issue alerts
    ā€¢ Incident response
    ā€¢ Cybersecurity coordination

    [2017] In India, it is legally mandatory for which of the following to report on cyber security incidents?
    1 Service providersĀ 
    2 Data CentresĀ 
    3 Body corporateĀ 
    Select the correct answer using the code given below:Ā 
    (a) 1 only (b) 1 and 2 only (c) 3 only (d) 1, 2 and 3

  • [20th March 2026] The Hindu OpED: AI-powered tax governance in India and its challenges

    PYQ Relevance[UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?Linkage: The question examines the use of Artificial Intelligence in healthcare and the associated concerns of data privacy and ethics. Similar privacy and ethical issues arise in AI-based tax governance, where sensitive financial data is processed.

    Mentorā€™s Comment

    The growing application of Artificial Intelligence in tax administration has significant implications for revenue mobilisation and governance in India. The Income Tax Departmentā€™s Project Insight (PI) represents a major shift towards data-driven tax administration. It leverages Artificial Intelligence and data analytics to enhance compliance, detect evasion, and improve revenue outcomes. 

    What is Project Insight (PI) and how does it function?

    1. Project Insight (PI): Establishes a data-driven tax intelligence system to strengthen compliance and enforcement.
    2. Income Tax Transaction Analysis Centre (INTRAC): Processes financial data from banks, GST, property, and securities to generate taxpayer insights
    3. 360-degree Profiling: Integrates multi-source financial data to build comprehensive taxpayer profiles
    4. Non-intrusive Usage of Data to Guide and Enable (NUDGE) Strategy: Uses behavioural nudges such as SMS and emails to prompt voluntary compliance
    5. Compliance Management Centralised Processing Centre: Ensures behavioural monitoring and correction of inaccurate filings

    How does AI improve tax compliance and administrative efficiency?

    1. Voluntary Compliance: Enables self-correction; over one crore revised returns filed since 2021
    2. Targeted Enforcement: Identifies high-risk taxpayers; 19,501 individuals contacted under NUDGE campaign
    3. Automation of Processes: Reduces routine workload; allows focus on complex assessments
    4. Service Delivery: Assists taxpayers in filing returns and resolving queries through automated systems
    5. Efficiency Gains: Reduces refund processing time from 93 days to 17 days

    What are the measurable outcomes of AI-driven tax governance?

    1. Revenue Augmentation: Generates ā‚¹11,000 crore additional tax collection
    2. Foreign Asset Disclosure: ā‚¹1,089 crore declared under foreign income reporting
    3. Digital Asset Tracking: ā‚¹29,208 crore in overseas assets including cryptocurrencies identified
    4. False Claim Correction: ā‚¹963 crore corrected under NUDGE campaign
    5. Additional Tax Payments: ā‚¹410 crore realised from compliance actions
    6. Evasion Detection: ā‚¹70,000 crore suppressed turnover identified since 2019-20
    7. Fraud Techniques Identified: Fake invoices, sales data manipulation, post-billing modifications

    What are the challenges related to data quality and accuracy?

    1. Data Dependence: Ensures outcomes depend on quality and completeness of input data
    2. False Positives: Flags legitimate transactions (e.g., joint family structures, clerical errors) as suspicious
    3. Error Propagation: Inaccurate data leads to flawed enforcement actions
    4. Administrative Burden: Increases grievance redressal workload

    How does algorithmic bias affect fairness in tax enforcement?

    1. Historical Bias Replication: Uses past enforcement data, reinforcing socio-economic disparities
    2. Geographical Skew: Targets specific regions or taxpayer categories disproportionately
    3. International Example: Dutch childcare benefits scandal demonstrates risks of biased AI systems
    4. Equity Concerns: Undermines fairness and trust in taxation

    Why is explainability critical in AI-based tax systems?

    1. Transparency Requirement: Ensures taxpayers understand reasons for scrutiny
    2. Right to Appeal: Facilitates challenge to algorithmic decisions
    3. Human Oversight: Maintains human-in-the-loop for high-impact decisions
    4. Legal Validity: Supports principles of natural justice and due process

    What are the concerns related to data privacy and security?

    1. Sensitive Data Handling: Involves financial and personal taxpayer information
    2. Cybersecurity Risks: Expands attack surface for data breaches
    3. Surveillance Concerns: Enables potential misuse of taxpayer data
    4. Regulatory Gaps: Highlights need for AI-specific safeguards

    Why is institutional oversight necessary in AI governance?

    1. AI Ombudsman Requirement: Establishes independent grievance redressal
    2. Algorithm Audits: Ensures external verification of AI systems
    3. Public Disclosure: Reports false positives and system accuracy
    4. Trust Building: Enhances legitimacy of tax administration

    Conclusion

    AI-based tax governance improves compliance and revenue outcomes. However, risks related to bias, privacy, and accountability require institutional safeguards. A balance between efficiency and fairness remains essential.

  • Jal Jeevan Mission & Sujal Gaon ID

    Why in the News

    The government has launched Sujal Gaon ID for digital mapping of rural water schemes.

    • Approved Jal Jeevan Mission (JJM) 2.0 extension till Dec 2028.
    • Increased total outlay to ā‚¹8.69 lakh crore.

    What is Sujal Gaon ID?

    • A unique digital ID for every rural piped water supply scheme
    • Enables end-to-end mapping (source ā†’ infrastructure ā†’ service area)
    • Integrated under ā€œSujalam Bharatā€ digital platform

    Key Facts

    • 1.64 lakh Sujal Gaon IDs created
    • Linked to 67,000 Sujalam Bharat IDs
    • Covers 31 States/UTs
    • Aim: Real-time monitoring, transparency, and accountability
    [2024] With reference to the Digital India Land Records Modernisation Programme, consider the following statements: To implement the scheme, the Central Government provides 100% funding. Under the Scheme, Cadastral Maps are digitised. An initiative has been undertaken to transliterate the Records of Rights from local language to any of the languages recognized by the Constitution of India. Which of the statements given above are correct? (a) 1 and 2 only (b) 2 and 3 only (c) 1 and 3 only (d) 1, 2 and 3

  • Internet Governance Capacity Building Programme

    Why in the News

    India marked one year of the Internet Governance Internship and Capacity Building Scheme (IGICBS), highlighting its role in preparing young professionals to engage effectively in national and global internet governance forums and represent Indiaā€™s interests.

    Key Institutions Involved

    • National Internet Exchange of India
    • Ministry of Electronics and Information Technology

    About IGICBS

    • A first of its kind capacity building initiative launched in 2024
    • Aims to build human capital in internet governance
    • Targets students and young professionals from Technology, Law, and Public policy.
    • Combines expert led learning, mentorship and internships
    • Bridges policy, technology and academia

    Key Achievements in One Year

    • 10,000 plus participants trained and engaged
    • Creation of a national pipeline of internet governance professionals
    • Strengthened Indiaā€™s presence in global standards and technical forums
    • Increased youth participation in multi stakeholder internet governance processes

    Strategic Significance

    • Enhances Indiaā€™s role in global internet governance
    • Supports a secure, inclusive and resilient digital ecosystem
    • Aligns with Indiaā€™s vision of knowledge led digital governance
    • Builds capacity for engagement in forums such as global internet governance discussions and technical standard bodies

    Prelims Pointers

    • IGICBS is a capacity building and internship programme, not a regulatory body
    • Implemented by NIXI under MeitY
    • Focuses on internet governance, not just coding or IT skills
    • Emphasises youth participation and global engagement
    • Example of soft power through digital leadership
    [2017] In India, it is legally mandatory for which of the following to report on cyber security incidents?Ā 

    1. Service providersĀ 

    2. Data CentresĀ 

    3. Body corporateĀ 

    Select the correct answer using the code given below:Ā 

    (a) 1 only (b) 1 and 2 only (c) 3 only (d) 1, 2 and 3

  • SabhaSaar Initiative

    Why in the News?

    ā€¢ Union Minister informed the Rajya Sabha about the SabhaSaar initiative

    About the Initiative

    ā€¢ AI enabled voice to text meeting summarisation tool
     ā€¢ Launched by the Ministry of Panchayati Raj
     ā€¢ Implemented across States and Union Territories
     ā€¢ Adopted by Gram Panchayats for Gram Sabha and Panchayat meetings
     ā€¢ Operates on AI and cloud infrastructure
     ā€¢ Provisioned through India AI Compute Portal
     ā€¢ Part of the India AI Mission under MeitY

    Key Features

    ā€¢ Structured minutes of meetings from video and audio recordings
     ā€¢ Ensures uniformity in Gram Sabha documentation
     ā€¢ Upload via e GramSwaraj login credentials
     ā€¢ Built on Bhashini platform
     ā€¢ Speech to text transcription, language translation, and automated summarisation
     ā€¢ Supports Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, and English

    Significance

    ā€¢ Strengthens grassroots governance
     ā€¢ Improves transparency and accountability
     ā€¢ Bridges language, literacy, and digital divides
     ā€¢ Enables efficient rural administration and instant access to meeting insights

    Consider the following: (2022)

    1. Aarogya SetuĀ 

    2. CoWINĀ 

    3. DigiLockerĀ 

    4. DIKSHAĀ 

    Which of the above are built on top of open-source digital platforms?Ā 

    (a) 1 and 2 only (b) 2, 3 and 4 only (c) 1, 3 and 4 only (d) 1, 2, 3 and 4

    This 2022 PYQ demonstrates the UPSC’s interest in the underlying technology stack of government initiatives. SabhaSaar is “built on the Bhashini platform” and is “part of the India AI Mission under MeitY”.

  • [8th December 2025] The Hindu OpED: Surveillance apps in welfare, snake oil for accountability

    UPSC RELEVANCE

    [UPSC 2023] E-governance, as a critical tool of governance, has ushered in effectiveness, transparency and accountability in governments. What inadequacies hamper the enhancement of these features?

    Linkage: This question links to GS-2 themes of e-governance, transparency, and accountability. The articleā€™s examples of NMMS, Poshan Tracker, and PDS apps directly show how design flaws and exclusion hinder these very objectives.

    Mentorā€™s Comment

    Surveillance-driven governance is expanding rapidly across Indiaā€™s welfare programmes. Mobile apps promising ā€œreal-time monitoringā€ and ā€œperfect accountabilityā€ are being deployed at scale, often without adequate evidence, capacity, or safeguards. This article critically evaluates the growing reliance on tech fixes in welfare delivery. For UPSC aspirants, it offers an analytical understanding of digital governance, state capacity, accountability frameworks, and ethical concerns, key themes across GS-2 and GS-4.

    Introduction

    Digital tools entered Indiaā€™s welfare architecture as instruments to modernise attendance, prevent leakages, and strengthen accountability. Over time, however, their use expanded without evaluating field conditions such as connectivity, device access, literacy, and administrative capacity. Surveillance apps have produced limited gains, created new exclusion risks, and shifted the burden of accountability onto frontline workers instead of programme designers and administrators.

    Why in the news

    Welfare programmes across India are increasingly mandating surveillance apps, ranging from biometric attendance to compulsory photo uploads, to improve accountability. But a series of recent failures, especially in schemes like the National Mobile Monitoring System (NMMS) and the Poshan Tracker, has exposed deep flaws. For the first time, governments are publicly acknowledging that these apps are producing unreliable data, penalising genuine beneficiaries, and overburdening frontline workers.

    How did biometric attendance become a dominant tool in welfare programmes?

    1. Biometric punctuality enforcement: Introduced to ensure staff attendance; absenteeism led governments to mandate digital attendance, even threatening punitive action. Example: Block in Uttarakhand where nurses faced punishments for late biometric attendance.
    2. Competing administrative tasks: Conscientious officials stayed back late to complete computerised work, leading to poor next-day biometric compliance.
    3. Impact on health workers: In Rajasthan, RCT evidence showed biometric attendance increased absenteeism, not punctuality.
    4. MGNREGA experience: Wage expenditure tied to digital attendance meant workers paid for tasks they did not perform if supervisors manipulated records.

    Why did the National Mobile Monitoring System (NMMS) generate controversy?

    1. Mandatory photo uploads: Required two geotagged photos daily; failure resulted in wages withheld.
    2. Unrealistic conditions: Poor connectivity in remote areas made uploads impossible.
    3. Limited deterrence of fraud: The app could not confirm whether workers were present all day; supervisors were still able to manipulate attendance.
    4. Excessive burden on workers: Workers anxious about upload deadlines; many were forced to return to worksites simply to capture photos.

    How did the Poshan Tracker create disruptions in nutrition schemes?

    1. Mandatory recognition technology: Ministry required Face Recognition Technology (FRT) for THR pack distribution to children and mothers.
    2. Connectivity problems: Anganwadi worker in Haryana, crowd waiting; app warning: ā€œthose who want to eat will continueā€, meaning refusal impossible.
    3. Risk of exclusion: Adivasi worker unable to upload photos; THR packs denied to her centreā€™s beneficiaries.
    4. Extra documentation: Ministry insisted FRT photos must match recorded photographs, adding further layers of control.

    How did ration distribution apps worsen inclusion errors for vulnerable households?

    1. App-based authentication: Some States required biometric or photograph-based verification for the full ration quota.
    2. Penalties for errors: In Jharkhand, uploaded photo mismatch led to partial ration denial.
    3. Burden on elderly/disabled beneficiaries: Those unable to stand for photographs or travel to ration shops lost access entirely.

    Do tech fixes improve accountability in welfare implementation?

    1. Accountability diversion: Apps target frontline workers (anganwadi workers, nurses, teachers) instead of programme designers who control budgets and logistics.
    2. Narrow definition of accountability: Focus limited to procedural compliance rather than service quality.
    3. Over-reliance on automation: Governments assume apps can ā€œproveā€ honesty or dishonesty; instead, structural gaps remain untouched.
    4. Manipulation persists: Despite apps, fraud, delays, and ghost entries continue, because the administrative ecosystem, not workers, drives corruption patterns.

    Limited effect of tech surveillance

    1. User rejection: Nurses in several states stopped using apps mandated by NHM due to technical and workload issues.
    2. False confidence in data: Administrators felt the ANA tool provided proof of malnutrition despite underlying measurement problems.
    3. Infrastructure mismatch: Apps needed smartphones, servers, data connectivity, conditions often absent in rural welfare ecosystems.
    4. Shifting blame: When NMMS and Poshan Tracker failed, ministries blamed ā€œmisuseā€ instead of app design flaws.

    Accountability Without Capacity: A Flawed Approach

    1. Fragmented accountability: Failures frequently attributed to workers; rarely to poor programme design.
    2. Blame-shifting: Ministries argued NMMS failures were due to workers manipulating apps.
    3. Overproduction of technology: Industries push surveillance apps and governments adopt them without field-testing.
    4. Cost to welfare: Data obsession overshadows quality of service delivery, including nutrition, health outreach, and ration reliability.

    Conclusion

    Surveillance apps in welfare promise transparency but frequently deliver exclusion, burden frontline workers, and create a false sense of accountability. The article shows that technological solutions, when applied without understanding field realities, act like ā€œsnake oilā€, seductive yet ineffective. Real accountability requires strengthening administrative capacity, improving worker conditions, and focusing on welfare outcomes rather than digital compliance rituals.