Subject: AI

Why in the News

OpenAI is rolling out a separate version of ChatGPT for teenagers, with tighter restrictions on conversations about self harm, suicide, eating disorders and sexual content. The move follows cases in which teenagers who died by suicide had interacted extensively with chatbots beforehand, and it arrives while a United States Federal Trade Commission (FTC) inquiry into seven AI companies is under way. The safeguard depends on estimating a user’s age and on parents choosing to switch controls on, which are the two weakest links in the chain.

What is ChatGPT for Teens?

1. About: It is a more restricted version of ChatGPT into which OpenAI places users it identifies as being under 18.
2. Content limits: The teen version avoids romantic or sexual conversations and places stronger limits around self harm related content.
3. Anti anthropomorphism rule: The chatbot is discouraged from presenting itself as conscious or as emotionally attached to the user.
4. Parental layer: Parents can link their accounts, set usage restrictions and receive alerts in certain situations.

How does OpenAI decide who is a teenager?

1. Signal based estimation: OpenAI uses a combination of signals to estimate whether a user could be under 18.
2. The three signals named: How an account is used, the subjects discussed in it, and how long the account has existed.
3. Override of stated age: ChatGPT can automatically place an account under the teen safeguards even if a different age was entered at signing up.
4. The admitted limit: Age detection systems are not foolproof, and several parental controls depend on families opting in.
5. What that makes the product: The teen version is an attempt to reduce some of the risks emerging around AI companionship rather than a complete fix for them.

Why do AI chatbots pose a different risk from social media?

1. Designed agreeability: AI chatbots are programmed to be agreeable companions that validate users’ feelings, which is not how conventional social media platforms operate.
2. The harm pathway: Unchecked validation can intensify suicidal behaviour and self mutilation among vulnerable children confiding their deepest fears.
3. Neurological vulnerability: Children’s developing brains make them particularly vulnerable to AI systems that create dopamine responses.
4. The combination that matters: The technology is highly responsive, anthropomorphic and adept at mimicking empathy, and for adolescents still developing judgement and a sense of self, that combination proves pernicious.
5. Everyday embedding: For a generation of digital natives, AI is already a sounding board for curiosity and a companion shaping how they learn, communicate and seek reassurance.

What do the litigation and the studies show?

1. The Adam Raine suit: The parents of 16 year old Adam Raine sued OpenAI last year, alleging that ChatGPT had validated his suicidal thoughts and discussed methods of self harm before his death in April 2025.
2. OpenAI’s own admission: The company acknowledged that some of its safety protections could become less reliable over the course of long conversations.
3. The Character.AI settlement: Character.AI and Google agreed this year to settle a lawsuit filed by the mother of a 14 year old who died by suicide in 2024 after extensively interacting with a Character.AI chatbot, with the mother alleging he had developed an intense emotional attachment to the bot.
4. The 2025 United States study: It found that ChatGPT provided dangerous responses to teens discussing self harm, substance abuse and eating disorders, including drafting suicide letters.
5. A second study: It found chatbots suggesting violence, self harm and substance use every five minutes during testing.

What are regulators elsewhere doing?

1. United States, Federal Trade Commission: The FTC, the country’s consumer protection and competition regulator, has opened an investigation into seven AI companies, including OpenAI, over the effects of their products on children.
2. United States, the Meta trial: Meta is facing a trial on the ground that it deliberately designed Facebook and Instagram to exploit young users’ vulnerabilities and to make its platforms addictive.
3. China: It has moved to restrict AI systems that encourage emotional dependence, targeting the companionship design itself rather than the content output.
4. India: It relies on a patchwork of laws, regulations and platform led interventions rather than a dedicated instrument for AI and minors.
5. What the set demonstrates: Two jurisdictions are acting through litigation and inquiry after the harm, one is acting on product design in advance, and India has neither route settled.

Why is a safer chatbot not the same as a safe one?

1. Age prediction is an estimate: The safeguard applies only once the system correctly guesses that the user is a minor, and children can misrepresent their age.
2. Enforcement dependent restrictions: Content restrictions are only as effective as their enforcement, which is not independently observable from outside the company.
3. Track record on earlier controls: OpenAI introduced parental controls last year, and critics quickly demonstrated that these could be easily bypassed.
4. Reactive sequencing: The protections arrived only after sustained public and legal pressure, which is a reminder that children’s online safety cannot be left to Big Tech alone.
5. The tension that remains: A company that profits from engagement is being asked to design against the very property, unconditional validation, that generates the engagement.

Challenges to AI Safeguards for Minors

1. Unverifiable age estimation: The safeguard triggers on inference rather than on verified identity. e.g. OpenAI relying on account usage patterns, discussed subjects and account age to guess whether a user is under 18.
2. Opt in dependence: Protections that require a parent to activate them reach only supervised households. e.g. the parental controls introduced last year that critics demonstrated could be easily bypassed.
3. Safety degradation over long sessions: Guardrails hold in short exchanges and weaken in the extended conversations minors actually have. e.g. OpenAI’s acknowledgement that some protections become less reliable over the course of long conversations.
4. Cross platform substitution: A restriction on one service pushes the user to a less restricted one. e.g. Character.AI, whose chatbot featured in the 2024 death that Google and the company settled this year.
5. Absence of independent testing: Only external researchers have surfaced the failure modes, and they have no standing access. e.g. the 2025 United States study that found ChatGPT drafting suicide letters for teens.
6. Divergent national rules: A globally distributed product faces incompatible obligations across markets. e.g. China restricting emotionally dependent AI systems while India relies on a patchwork of laws and platform led interventions.
7. No liability standard for conversational harm: Existing intermediary law was written for hosted content, not for generated responses. e.g. the Adam Raine suit, which turns on whether a chatbot’s own outputs contributed to a death.

Conclusion

The property that makes chatbots compelling for adolescents, unconditional and empathetic sounding validation, is the same property that turned them dangerous in the Raine and Character.AI cases. ChatGPT for Teens restricts content, discourages the bot from claiming emotional attachment and adds parental linkage, which is a welcome and overdue intervention. It nonetheless rests on age estimation that is admittedly not foolproof and on controls that families must opt into, after the previous generation of parental controls was shown to be bypassable. What remains missing is independent testing, transparency and external scrutiny, alongside digital literacy for the parents the safeguards assume will be watching.

Child Online Safety and Artificial Intelligence Governance in India

1. About: Child online safety covers the protection of minors from harmful content, exploitative design, data exploitation and psychological harm arising from digital products.
2. The distinctive AI risk: Generative systems produce responses rather than host content, so harm arises from the model’s own output and not from a third party post an intermediary can be asked to take down.
3. Companionship design: Systems built to maximise engagement through empathy simulation create attachment, which is why regulation is beginning to target design features rather than only content categories.
4. India’s scale: India has one of the world’s largest populations of internet users under 18, with smartphone access typically arriving before any formal digital literacy instruction.
5. Regulatory posture: India has no dedicated artificial intelligence statute, and obligations flow from the Information Technology Act, 2000, data protection law and platform self regulation.
6. Institutional anchor: The National Commission for Protection of Child Rights is the statutory body that issues advisories and takes cognisance of child rights violations, including online ones.

Laws and Rules Governing Children’s Online Safety in India

1. Information Technology Act, 2000: The parent statute for electronic records, intermediary liability and cyber offences.
2. Section 79: Grants intermediaries conditional safe harbour subject to due diligence, which is the hook for content obligations.
3. Section 67B: Penalises the publication and transmission of material depicting children in sexually explicit acts.
4. Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021: Impose due diligence, grievance redress and expeditious removal obligations on intermediaries and significant social media intermediaries.
5. Digital Personal Data Protection Act, 2023: Defines a child as a person below 18 and requires verifiable parental consent before processing a child’s personal data.
6. Design prohibition: Bars tracking, behavioural monitoring and targeted advertising directed at children.
7. Protection of Children from Sexual Offences Act, 2012: Criminalises sexual offences against children, including the use of children for pornographic purposes.
8. Juvenile Justice (Care and Protection of Children) Act, 2015: Provides the care, protection and rehabilitation framework for children in need of care.
9. Commissions for Protection of Child Rights Act, 2005: Establishes the National and State Commissions for Protection of Child Rights with powers of inquiry into violations.
10. Bharatiya Nyaya Sanhita, 2023: Carries the general criminal provisions on abetment of suicide and obscenity that apply where a digital product is alleged to have contributed to harm.

Government Initiatives for Child Online Safety

1. IndiaAI Mission: The national programme for compute, datasets, applications and a safe and trusted artificial intelligence pillar covering risk assessment and governance tools.
2. Cyber Crime Prevention against Women and Children scheme: Funds State capacity for handling online offences against women and children, including forensic and training support.
3. National Cyber Crime Reporting Portal: Provides a dedicated reporting channel for child sexual abuse material and other online offences.
4. Information Security Education and Awareness programme: Runs cyber safety awareness for students, teachers and parents through the Ministry of Electronics and Information Technology.
5. Indian Computer Emergency Response Team advisories: Issues public advisories on online safety practices and coordinates incident response.
6. National Commission for Protection of Child Rights advisories: Issues directions to platforms on age assurance, harmful content and child data practices.
7. Cyber Swachhta Kendra: Operates as the botnet cleaning and malware analysis centre supporting safer end user devices.

Key Facts about Children and the Digital Environment

1. The Digital Personal Data Protection Act, 2023 sets the threshold for a child at below 18 years, which is higher than the 13 year threshold under the United States Children’s Online Privacy Protection Act, 1998.
2. The National Commission for Protection of Child Rights is a statutory body constituted under the Commissions for Protection of Child Rights Act, 2005.
3. Safer Internet Day is observed on the second Tuesday of February.
4. The European Union Artificial Intelligence Act, 2024 is the first comprehensive statute to classify artificial intelligence systems by risk tier and to ban specified manipulative practices.
5. The Convention on the Rights of the Child, 1989, to which India is a party, requires protection of children from all forms of exploitation prejudicial to their welfare.
6. General Comment No. 25 (2021) of the United Nations Committee on the Rights of the Child extends child rights obligations explicitly to the digital environment.

Challenges in Regulating Artificial Intelligence Use by Children

1. Verifiable parental consent at scale: The law demands verification without prescribing a workable method that does not itself collect more child data. e.g. the Digital Personal Data Protection Act, 2023 requiring verifiable parental consent for every under 18 user.
2. Mismatched age thresholds: A global product faces a different definition of a child in each market. e.g. India setting the threshold at 18 while the United States Children’s Online Privacy Protection Act, 1998 sets it at 13.
3. Attributing harm to a model output: Causation is contested when the alleged harm is a conversation. e.g. the Adam Raine suit and the Character.AI settlement, both of which turn on whether chatbot responses contributed to a death.
4. Cross border enforcement: Models hosted and trained abroad serve domestic minors with no local establishment to proceed against. e.g. Indian users accessing chatbots operated entirely from other jurisdictions.
5. Absence of a dedicated statute: Regulation runs on instruments written for hosted content and for data, not for generated responses. e.g. India relying on the Information Technology Act, 2000 and platform led interventions.
6. Parental digital literacy gap: Controls assume a supervising adult who understands the product. e.g. first generation smartphone households where the child is the more capable user.
7. Design based harm outside content rules: Engagement optimisation and empathy simulation are not content categories that a takedown regime can reach. e.g. China moving to restrict AI systems that encourage emotional dependence, a design level rather than content level intervention.

Way Forward

1. Independent safety testing: Require third party red team testing of chatbot behaviour with adolescent personas, with results published rather than held by the developer.
2. Statutory age assurance standards: Prescribe a privacy preserving age assurance method so protection does not depend on a company’s own inference or on a child’s self declaration.
3. Default on, not opt in: Make the safest configuration the default for accounts assessed as belonging to minors, so protection does not depend on a parent activating it.
4. Duty of care by design: Place an explicit obligation on developers to design against engagement maximisation and emotional dependence for minors, following the design level approach rather than a content list.
5. Crisis routing obligations: Mandate that any self harm, suicide or eating disorder cue in a minor’s conversation trigger an immediate handoff to a human helpline, with logged compliance.
6. Transparency reporting: Require periodic public reporting of safety failure rates, bypass incidents and the duration effect on guardrail reliability in long conversations.
7. Digital literacy and sensitisation: Build chatbot specific awareness into school curricula and parent outreach, since the risk is a design property that neither group currently recognises.
8. A dedicated Indian instrument: Move from the present patchwork to a clear framework for artificial intelligence products used by minors, backed by the National Commission for Protection of Child Rights and the data protection regulator.

“[2025, GS2, 15 marks] The National Commission for Protection of Child Rights has to address the challenges faced by children in the digital era. Examine the existing policies and suggest measures the Commission can initiate to tackle the issue.”

Why in the News

India ranks among the world’s leading artificial intelligence ready nations, powered by Digital Public Infrastructure and a large innovation ecosystem, while women fall from 43 percent of STEM graduates to 10 percent of senior AI leadership. Every artificial intelligence system begins with data and every dataset begins with people, so a pipeline that loses women at each stage produces systems that reproduce the inequality of the society they learn from.

What is the AI pipeline?

1. Definition: The AI pipeline is the full sequence from data collection through model training and deployment to the decisions the model produces.
2. Not only technical: It is not merely a technological conduit of code, silicon and compute power. It is fundamentally a human pipeline.
3. It starts early: The pipeline begins before the first line of code is written, at the point where data about people is collected or not collected.
4. Where the consequences land: Its outputs shape decisions affecting millions, from loan sanction to clinical recommendation.
5. The failure mode: When people are absent from that data, artificial intelligence inherits those gaps.

What is Digital Public Infrastructure?

1. Definition: Digital Public Infrastructure (DPI) is a set of shared, interoperable digital systems, such as digital identity, payments and data exchange layers, built as public utilities on which both government and private services run.
2. Why it matters here: India’s artificial intelligence readiness is powered by DPI, which also determines whose transactions and records enter the datasets models are trained on.

What is the India AI Mission?

1. Definition: The India AI Mission is the national programme providing compute capacity, datasets, application development support, skilling and startup financing for artificial intelligence in India.
2. Relevance here: It is the vehicle through which artificial intelligence in India can be steered onto the same inclusive path that DPI followed for public welfare.

Where does the pipeline leak women?

1. STEM foundation: Women account for 43 percent of India’s STEM graduates, one of the world’s largest pools of women STEM graduates.
2. Tech workforce: Representation falls to 26 percent in the technology workforce.
3. Advanced AI roles: Only 12 percent of professionals in advanced artificial intelligence roles are women.
4. Senior AI leadership: Women hold just 10 percent of senior artificial intelligence leadership positions.
5. What the sequence shows: At every stage the pipeline leaks talent, lived experience and innovation, so the loss compounds rather than occurring at one bottleneck.

What causes the leakage?

1. Access to the network itself: Only 57 percent of women have independent internet access, compared with 72 percent of men.
2. Nutrition and education: Unequal nutrition and unequal education set the disparity before any career choice is made.
3. Caregiving responsibilities: Unpaid care work removes women from the workforce at the point where advanced technical careers compound.
4. Workplace discrimination: Discrimination at work blocks progression from entry level technical roles into advanced ones.
5. Language barriers: Artificial intelligence education is dominated by English, which excludes those schooled in other languages.
6. School infrastructure: A student cannot pursue robotics where her school lacks the necessary infrastructure, so the exclusion begins well before higher education.
7. Influence, not only presence: A woman who becomes an artificial intelligence engineer often remains the only woman in the room, with limited influence in product design.

What happens to systems built without women in the data?

1. Credit assessment: A self help group member in rural Bihar applying for a micro-loan is scored by models relying mainly on historical male financial patterns, which may underestimate her creditworthiness.
2. Maternal health tools: A community health worker in Gujarat depends on artificial intelligence enabled maternal health tools, and training data that fails to reflect local nutrition and health conditions produces inaccurate recommendations affecting maternal care.
3. The general mechanism: Artificial intelligence automates existing inequalities when trained on incomplete or biased data.
4. The learning relationship: Artificial intelligence learns from society, so an unequal society produces an artificial intelligence that reflects that inequality.
5. Why datasets alone are insufficient: Correcting the output requires more than diverse datasets, because the decisions about what to collect and what to optimise are made by the people in the room.

Does India’s AI readiness conceal an exclusion problem?

1. The readiness claim: India ranks among the world’s leading artificial intelligence ready nations, powered by Digital Public Infrastructure and a thriving innovation ecosystem.
2. The contradiction beneath it: India produces one of the world’s largest pools of women STEM graduates, and women steadily disappear as the artificial intelligence pipeline advances.
3. Formal equality achieved early: When India adopted its Constitution in 1950, it granted women and men universal adult franchise simultaneously, ahead of the sequence followed in several western democracies.
4. Substantive access lagging: That simultaneous political inclusion sits alongside a 15 percentage point gap in independent internet access between men and women today.
5. What the measure of leadership should be: True artificial intelligence leadership cannot be measured only by models, investments or patents. It must be measured by whether artificial intelligence reflects India’s diversity of languages, cultures, socio-economic realities and lived experiences.

What does the corrective path look like?

1. The precedent of scale: India has already shown how technology can advance public welfare at scale, and the India AI Mission offers the opportunity to ensure artificial intelligence follows the same inclusive path.
2. Existing women’s institutions: Across rural India, women’s self-help groups have built strong financial ecosystems through collective savings and entrepreneurship, which is usable financial data and an existing delivery network.
3. Influence changes output: When women occupy positions of influence, the technology itself shifts.
4. Four roles, not one: Women and marginalised communities must participate as researchers, engineers, entrepreneurs and policymakers, not only as subjects in the training data.
5. The constitutional foundation: The commitment to simultaneous inclusion continues through Digital Public Infrastructure, which provides the base for building inclusive artificial intelligence.

Challenges to building inclusive AI

1. Unpaid care work truncates technical careers: Time available for advanced training and long project cycles is unequal, e.g. the Time Use Survey 2019 recorded women spending 299 minutes a day on unpaid domestic work against 97 minutes for men.
2. Device and connectivity gap precedes the skills gap: Independent access, not shared household access, determines who generates data, e.g. the National Family Health Survey 2019 to 2021 found 33.3 percent of women had ever used the internet against 57.1 percent of men.
3. Language exclusion in model and curriculum: English dominant material and models exclude most first generation learners, e.g. Bhashini and BharatGen were set up precisely because Indian language coverage in large models was thin.
4. Data annotation labour has no design voice: The workers who label training data are outside the decisions the data shapes, e.g. annotation work is outsourced at low wages with no representation in product design.
5. No bias audit obligation: Automated decision systems face no statutory fairness testing requirement, e.g. the Digital Personal Data Protection Act, 2023 governs consent and processing of personal data but imposes no algorithmic audit duty.
6. Online safety drives women off the platforms that generate data: Harassment reduces sustained participation, e.g. National Crime Records Bureau data has recorded a rising count of cyber crimes against women.
7. Absence of sex disaggregated public datasets: Models cannot be checked for differential performance where the data does not record the split, e.g. many administrative datasets used for training carry no reliable gender field.

Conclusion

The central point is that the artificial intelligence pipeline is a human pipeline, and the numbers show it losing women at every stage from 43 percent of STEM graduates to 10 percent of senior AI leadership. Diverse datasets alone will not correct outputs shaped by rooms in which women are absent, so participation must extend to research, engineering, entrepreneurship and policymaking. What remains unresolved is the access gap that precedes all of it, with only 57 percent of women holding independent internet access against 72 percent of men.

Why in the News

Content generated by Claude will carry a machine readable marking, after Anthropic signed the transparency Code of Practice under Article 50(2) of the European Union Artificial Intelligence Act. The change extends watermarking from images and video to text itself, where the mark travels with copied text and detection is not reliable. The obligation arises from one regional law but the rollout is global.

What is Anthropic’s new watermarking system?

1. Trigger: The policy was introduced after Anthropic signed the EU AI Act’s Article 50(2) Code of Practice on Transparency of AI Generated Content.
2. Two forms of marking: Watermarks are embedded in text content produced by Claude. Signed provenance metadata is attached to supported files in formats such as .svg, .png and .jpg.
3. Applied at the model level: The text watermark is invisible to users. Anthropic has confirmed that it will not affect Claude’s response.
4. Persistence: The watermark is part of the text, so it travels with the text when it is copied and pasted elsewhere, and may persist through some editing.
5. Coverage of surfaces: Output from the Claude Platform (API), Claude, Claude Code, Claude Cowork and Claude Tag is set to carry the embedded watermarks. The same applies when Claude models are accessed through AWS, Google Cloud and Microsoft Foundry.
6. Detection still incomplete: Anthropic is still working on letting external parties detect the markings, and the rollout announcement did not reveal full technical details.

What is Article 50(2) of the European Union Artificial Intelligence Act?

1. Substance: It requires providers of AI systems that generate synthetic text, audio, image or video to mark their outputs in a machine readable format and make them detectable as artificially generated.
2. Code of Practice route: Signing the associated Code of Practice is the voluntary compliance instrument through which providers demonstrate that they meet the transparency duty.

What is signed provenance metadata?

1. About: It is a cryptographically signed record attached to a file that states the file’s origin and the tool that produced it, so a later viewer can verify where it came from.
2. Weak point: The record is stripped when the file format is converted, which breaks the chain of verification.

Why does watermarking text change the stakes for ordinary users?

1. Everyday written work is now in scope: Professional emails, personal messages, school assignments and workplace deliverables that could once pass as human made may carry an AI watermark.
2. Marginal AI involvement still marks the file: The mark can attach even where Claude’s involvement was close to negligible.
3. Second hand exposure: A human made file that is proofread, translated, summarised or converted by someone else using Claude can still carry a mark in the final output.
4. Non users are exposed: A person who never uses the tool can end up holding marked text produced by a collaborator, which has put non users on edge alongside users.
5. Workflow effect: Millions of customers are reconsidering their use of AI tools and debating at what point human content becomes AI content.

Why does the mark not settle the question of authorship?

1. Both error types admitted: Detecting a Claude mark does not confirm that the work was created by AI. The absence of a mark does not confirm that the work was fully human made.
2. Short text: Short text lengths can throw off the result, since a watermark needs sufficient text to be carried.
3. Post processing edits: Content changes made after Claude processed the text can degrade the signal.
4. Format conversion: Metadata is stripped when a file format is converted, removing the provenance record for images and documents.
5. Unsupported surfaces: Use of a Claude offering that does not yet support AI marking leaves the output unmarked.

What new risks has the announcement itself created?

1. A removal market: Multiple dubious websites offering watermark “removal” or “clean up” services came online within days of the announcement.
2. A repeat of the detector cycle: The earlier rise of AI text detectors was followed by AI text humanisers built to deceive those same detectors.
3. Reputational damage already recorded: Detector outputs have been involved in cases leading to cancelled book deals and social media trolling for authors and bloggers.
4. Tool quality: AI text detection tools remain experimental, fallible and prone to errors, yet are treated as evidence.
5. Credential risk: Users now face the prospect that their own tool damages their professional credentials.

Why do watermarks work for images but not yet for text?

1. Images and video are the solved case: Watermarks give regulators, fact checkers and journalists a reliable way to verify the origin of an image or video and trace it to a specific provider.
2. Text is not: Accurately detecting AI generated text remains uncharted territory, so the same verification logic does not transfer.
3. Circulation outruns labelling: AI generated content is circulated thousands of times on social media unchecked, as content moderation rules have been loosened across the Meta family of apps and X.
4. Users do not look: The average internet user scrolling on a phone misses even visible AI watermarks, and an invisible mark is weaker still.
5. Regulator dependence: A tangible reduction in misinformation and deepfakes requires technology providers and regulators to act together, not a marking standard alone.

Challenges to AI content watermarking

1. Adversarial removal: Paraphrasing, translation and dedicated stripping tools defeat statistical text watermarks. e.g. the removal and clean up websites that appeared within days of the Anthropic announcement.
2. No interoperable standard across providers: A mark from one model tells nothing about content from another, so an unmarked file proves nothing. e.g. the Coalition for Content Provenance and Authenticity (C2PA) standard is adopted by some providers and open source models remain outside it.
3. False accusation of students and writers: Detector outputs are used as disciplinary evidence despite admitted error rates. e.g. OpenAI withdrew its own AI Text Classifier in July 2023 citing low accuracy.
4. Open weight models cannot be compelled: A provider level obligation does not reach models that run on a user’s own machine. e.g. freely downloadable open weight models can generate unmarked text offline.
5. Jurisdictional mismatch: A duty created by one region’s law governs the provider, not the harm suffered elsewhere. e.g. an Indian user injured by unmarked synthetic content depends on a European regulator’s enforcement.
6. Labelling does not stop the harm: A deepfake remains persuasive even when correctly labelled, because the first viewing shapes belief. e.g. the November 2023 deepfake video of an Indian film actor circulated widely before any advisory was issued.

Conclusion

A transparency duty designed for synthetic images and video has been extended to text, where detection is unreliable and the mark attaches to work that may be substantially human. The result is a signal that users cannot see, verify or contest, carrying real reputational consequences. Labelling will reduce misinformation only if detection tools become accurate and platforms act on the marks, neither of which is settled.

Artificial Intelligence Governance in India

1. About: AI governance covers the rules on how AI systems are built, trained, deployed and labelled, and who is liable when they cause harm.
2. No dedicated statute: India regulates AI through existing law and subordinate rules rather than a single AI Act, unlike the European Union’s risk tiered model.
3. Scale: India has one of the largest AI talent pools and developer bases globally and is among the largest markets for consumer AI applications.
4. Institutional anchor: The Ministry of Electronics and Information Technology (MeitY) is the nodal ministry, working through the IndiaAI Mission and advisories to intermediaries.
5. Global positioning: India hosted the AI Impact Summit in New Delhi in February 2026, the successor to the AI Safety Summit series, and is a founding member of the Global Partnership on Artificial Intelligence (GPAI).

Laws and Rules Governing AI Generated Content in India

1. Information Technology Act, 2000: The parent statute for electronic records, intermediary liability and cyber offences.
2. Section 79 grants intermediaries safe harbour subject to due diligence, which is the hook for content labelling duties.
3. Section 66D penalises cheating by personation using a computer resource, used against deepfake impersonation.
4. Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021: Impose due diligence, grievance redress and takedown timelines on intermediaries and significant social media intermediaries.
5. Amendment Rules on synthetically generated information, 2026: Require platforms to label synthetically generated information prominently and to obtain user declarations on whether uploaded content is synthetic.
6. Digital Personal Data Protection Act, 2023: Governs processing of personal data, including data used to train and prompt AI models, with consent and purpose limitation duties.
7. Bharatiya Nyaya Sanhita, 2023: Covers forgery, defamation and obscenity offences that synthetic media can constitute.
8. Copyright Act, 1957: Governs authorship and infringement questions raised by training data and machine generated output.

Back2Basics: European Union Artificial Intelligence Act

1. What it is: The world’s first comprehensive horizontal law on artificial intelligence, adopted by the European Union.
2. Entry into force: 1 August 2024, with obligations applying in phases.
3. Approach: A risk based classification into unacceptable risk, high risk, limited risk and minimal risk, with duties scaled to the tier.
4. Prohibited practices: Social scoring by public authorities, untargeted scraping of facial images and manipulative techniques exploiting vulnerabilities.
5. Article 50: Sets transparency obligations for AI systems that interact with people or generate synthetic content, including machine readable marking of outputs.
6. Extraterritorial reach: It binds providers placing systems on the EU market irrespective of where they are established, which is why compliance measures are rolled out globally.

Government Initiatives

1. IndiaAI Mission: Approved in March 2024 with an outlay of about Rs 10,371.92 crore, built on seven pillars covering compute capacity, innovation centre, datasets platform, application development, future skills, startup financing and safe and trusted AI.
2. Safe and Trusted AI pillar: Funds work on deepfake detection, algorithmic bias audits and AI governance frameworks, and underpins the proposed AI Safety Institute.
3. National Strategy for Artificial Intelligence, 2018: NITI Aayog’s framework identifying healthcare, agriculture, education, smart mobility and smart cities as focus sectors under the AI for All approach.
4. Bhashini: The National Language Translation Mission building open speech and translation datasets across Indian languages.
5. Responsible AI for Youth: A skilling programme for government school students to build AI literacy at scale.
6. Digital India Act consultations: Proposed successor to the Information Technology Act, 2000, intended to address emerging technologies including AI and deepfakes.

Key Facts about AI Content Provenance

1. C2PA: The Coalition for Content Provenance and Authenticity is the main cross industry technical standard for attaching tamper evident provenance to media files.
2. SynthID: Google’s watermarking system for AI generated images, audio, video and text.
3. Deepfake: Synthetic media in which a person’s likeness or voice is replaced or generated, typically using generative adversarial networks or diffusion models.
4. Turing Test: The 1950 benchmark for machine indistinguishability from a human, now inverted by the problem of detecting machine authorship.
5. GPAI: The Global Partnership on Artificial Intelligence was launched in June 2020 with India as a founding member, and India held its chair in 2024.

Challenges in AI Governance in India

1. No binding statutory framework: India governs AI through advisories and subordinate rules that carry weaker enforceability than a statute. e.g. the March 2024 MeitY advisory on under tested AI models was revised within weeks after industry objections.
2. Compute dependence: Frontier model training depends on imported accelerators and foreign cloud capacity. e.g. the IndiaAI Mission empanelled over 18,000 graphics processing units in its first round in January 2025 to close this gap.
3. Data protection enforcement capacity: The Data Protection Board must supervise a very large volume of processors with limited staff. e.g. the Digital Personal Data Protection Act, 2023 rules were notified only in November 2025, years after enactment.
4. Copyright and training data disputes: Ownership of material used to train models is unresolved in Indian law. e.g. the news agency ANI’s suit against OpenAI in the Delhi High Court filed in November 2024.
5. Election integrity: Synthetic audio and video can be deployed at scale during compressed campaign periods. e.g. AI generated voice clips of political leaders circulated during the 2024 Lok Sabha campaign.
6. Algorithmic bias in public service delivery: Models trained on unrepresentative data misclassify beneficiaries. e.g. facial authentication failures for manual workers under Aadhaar based attendance systems.
7. Skill and audit gap: India lacks a trained cadre of independent AI auditors to test high risk deployments. e.g. no statutory conformity assessment body exists comparable to the notified bodies under the EU AI Act.

Way Forward

1. Enact a risk tiered statute: Replace advisory based governance with a law that classifies AI uses by risk and fixes provider and deployer liability.
2. Mandate interoperable provenance: Require adherence to a common content credential standard so a mark from one provider is readable by all platforms.
3. Build public detection capacity: Fund an independent testing facility to benchmark deepfake and text detectors and publish accuracy rates.
4. Protect against false accusation: Bar educational institutions and employers from acting on detector output alone, and require corroborating evidence.
5. Expand sovereign compute: Scale domestic graphics processing unit capacity and public datasets so Indian models are not fully dependent on foreign infrastructure.
6. Strengthen platform duties: Require prominent labelling at the point of display, not only in file metadata, and fix takedown timelines for unlabelled synthetic media.
7. Invest in digital literacy: Run sustained public campaigns so users check provenance labels rather than react to content at first sight.

“[2023, GS3, 10 marks] 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?”

Why in the News?

Recent operations in Ukraine, Venezuela and Iran show AI-fused targeting, autonomous drone swarms and machine-speed strikes compressing engagement timelines and deciding outcomes. This convergence is shifting the basis of military power from hardware inventory to software velocity, exposing India’s defence establishment as structurally unprepared for the shift from a weapons-manufacturing model to a software-enterprise model.

Why is algorithmic precision replacing hardware mass as the decisive factor in war?

1. Simultaneous convergence: AI, autonomy and algorithmic precision are advancing together, not in sequence. Their combined effect multiplies battlefield lethality rather than adding to it.
2. Historic scale of disruption: The deployment of software at unprecedented speed and scale in combat is being compared to a Manhattan Project moment. It marks a comparable inflection point to the arrival of gunpowder and nuclear weapons.
3. Inverted innovation cycle: Software in combat theatres is updated every three weeks. New hardware is fielded only every three months. The traditional hardware-leads-software model has reversed.
4. Institutional identity under strain: The Ministry of Defence has functioned as a platform and weapons factory. This shift requires it to function as a software enterprise instead.

What do recent conflicts and defence-tech ventures reveal about AI-driven warfare?

1. Ukraine (Delta platform): Delta fuses radar imagery, satellite feeds and social media data into one stream. It links to a drone inventory to form a “kill web” that compresses detection-to-neutralisation time to a couple of minutes.
2. Ukraine (drone battlefield economy): Ukraine is procuring eight million drones this year, more than the artillery shells it fired last year. These platforms range from 25 km tactical close air support to 2,500 km strategic strike.
3. Venezuela (US use of Anthropic’s Claude): American forces used the commercial AI model Claude to track the movements of ousted president Nicolás Maduro. This intelligence was synchronised with electronic attacks, cyber exploits and a Delta Force heliborne assault to capture him.
4. Iran (machine-speed targeting): Targeting packages generated at machine, not human, speed enabled strikes that eliminated almost the entire Iranian military leadership within minutes on a single morning.
5. United States (Anduril’s YFQ-44A Fury): A defence-tech startup, not a legacy defence prime, built this AI-powered unmanned fighter jet. It is designed to operate independently or team with crewed aircraft, showing that defence innovation is migrating toward agile startups.

What competitive and structural pressures complicate India’s adaptation to this shift?

1. Chinese software threat: A tool named Mythos functions as a virtual cyber-nuke capable of disabling an adversary’s operating system. This shows offensive capability has moved beyond kinetic weapons into software itself.
2. Chinese hardware race: Huawei is pursuing 1.4 nanometre transistor density by 2031 to challenge Nvidia’s 4 nanometre Blackwell chips. This targets the compute layer that underpins AI-driven weapons systems.
3. Speed as a structural constraint: A three-week software cycle against a three-month hardware cycle cannot be matched by an organisation built around multi-year procurement timelines.
4. Institutional inertia as the central obstacle: The Ministry of Defence’s identity as a weapons and platform manufacturer conflicts directly with the software-enterprise model this warfare paradigm demands. Resolving this conflict is the precondition for everything else.

What sovereign pathways can India adopt to close this gap?

1. Sovereign data fusion: India must urgently build its own AI-enabled data analytics platform in the manner of Delta, rather than depend on external systems.
2. Autonomous coordination software: Software must independently coordinate drone swarms, identify objects of interest, distinguish civilian aircraft and birds from combat platforms, and direct shooters to destroy targets.
3. Drone inventory at scale: India should build a diverse drone inventory with a target of five million units by 2028.
4. Counter-drone kill webs: Laser and microwave counter-drone systems paired with drone-hunting teams should establish AI-enabled kill webs along the LoC and LAC.
5. Space-based ISR: India should crowd low-earth orbit space to transition from persistent surveillance to offensive intelligence, surveillance and reconnaissance.
6. Budget reallocation: At least 40% of the roughly Rs 2 lakh crore modernisation budget for 2027 should go to technological solutions rather than conventional hardware.

Conclusion

The decisive factor in modern warfare is shifting from hardware inventory to algorithmic velocity. Whoever controls faster AI-driven sense-decide-strike cycles gains advantage regardless of platform numbers. India cannot depend on borrowed or externally controlled AI and autonomy systems in a live conflict; it must build sovereign capability across data platforms, autonomous software, drone and counter-drone infrastructure, and space-based ISR. This requires the Ministry of Defence to transform from a weapons-manufacturing body into a software enterprise, a cultural and structural shift whose outcome remains untested.

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 PYQ examines the transformative applications of Artificial Intelligence (AI), its strategic implications, and the challenges arising from its deployment. The article extends AI’s application from the civilian domain to warfare, highlighting how AI-enabled autonomous systems, algorithmic warfare, and human-machine teaming are redefining military strategy, deterrence, and national security.