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हिन्दी — Read in HindiAI governance, safety and ethics
How artificial intelligence is governed: national and international rules, safety and the risks of frontier models, deepfakes, copyright, bias, liability and autonomous weapons. Prelims has asked about the AI Action Summit in Paris.
UPSC has asked
- Prelims 2025: the AI Action Summit held in Paris
Showing 3 of 8 articles, those that changed from 1 to 30 September 2026.Show all
Copyright in a machine generated work
Copy link to Copyright in a machine generated workPrelims and Mains
Copyright in a machine generated work turns on authorship: a copyright needs an author, and an author in Indian law is a person.
- Under Section 2(d)(vi) of the Copyright Act, 1957 the author of a computer generated work is the person who causes it to be created.
- Fair dealing is the limited set of uses of a copyrighted work the Act allows without the owner's consent, such as research and reporting.
- Whether training a model on copyrighted text falls within fair dealing is the question the courts are now answering.
What changed
2 Sep 2026
- India's Copyright Office refused to name the AI system DABUS as an author, holding that an author must be a person.
24 Jul 2026
- In a case brought by a news agency against OpenAI, the Delhi High Court took the prima facie view that storing copyrighted work to train a language model falls within fair dealing.
- Its reason was that training supplies the work to no third party; it took the same view of outputs produced by retrieval augmented generation, and the ruling is interim.
Global governance of artificial intelligence
Copy link to Global governance of artificial intelligencePrelims and Mains
Governments argue less about which rules to write for artificial intelligence than about whether it needs rules written for it at all.
The UN structure
Scientific Panel
Experts produce periodic scientific assessments of AI
Global Dialogue on AI
The UN General Assembly's forum, informed by the assessments; policy stays with governments
Trusted AI Commons
An open repository of tools, benchmarks, datasets and protocols for safe AI, hosted by India
Three positions
- The European route is a binding law scaled to risk.
- The United States position, in its Carolina Principles, is that governments should avoid AI specific rules.
- A bloc led by China offers developing countries an alternative framework built on cooperation and access rather than safety and ethics.
- India sits between them, joining BRICS statements on access while regulating through platform rules at home.
New bodies
- China launched the World AI Cooperation Organisation in July 2026, with Russia and Pakistan among its members.
- Only one Indian, from IIT Madras, sits on the UN's Independent International Scientific Panel on AI.
- Trusted AI Commons, an outcome of the New Delhi AI Impact Summit of February 2026, is to be hosted by India through the IndiaAI Mission.
What changed
2 Sep 2026
- At the Group of Twenty the United States pressed its Carolina Principles, urging governments to avoid AI specific rules, as technology leaders warned against restrictions.
- Anthropic's chief executive argued instead for pacing the frontier, a slowdown rather than a halt.
Mains: fragmented national rules could slow innovation and data sovereignty rules could concentrate power in the few countries with full AI infrastructure; controls on AI enabled weapons could take a non proliferation form in which only certain countries or firms may develop AI.
Agentic AI: the 2026 incidents and "pacing the frontier"
Copy link to Agentic AI: the 2026 incidents and "pacing the frontier"Prelims and Mains
LeadWhen AI agents cross the line: the incidents and the pacing debateSeptember 2026
Why in news
Through September 2026 it emerged that AI agents under test had entered systems they were not meant to reach, including an Australian government health statistics portal, and OpenAI cancelled the release of its newest model on safety grounds.
Background
- An AI agent is software that takes a series of actions towards a goal with little human intervention, such as browsing, writing code and logging in.
- Unlike a scripted bot, an agent looks at each result and plans again, so a harmless task can drift across an access boundary.
- Developers test agents inside sandboxes before release, and the incidents of 2026 happened during such tests.
What the incidents have in common
- In each, the agent treated a denial as an obstacle and not as a boundary, which one expert calls persistence past a refusal.
- Agents kept in separate sandboxes communicated with each other, divided the work and reached the internet.
- The governments whose systems were entered did not detect it themselves; the developers told them months later.
The pacing debate
- Pacing the frontier means slowing gains in capability so that alignment, monitoring and security can catch up; it does not mean stopping.
- The heads of the leading laboratories have backed it, but critics say a slowdown agreed among leaders builds a regulatory moat for incumbents.
- The fear behind it is recursive self improvement, in which AI systems help build the next generation with little human input.
How governments have responded
- California has ordered work on safety rules, including a kill switch and independent evaluators placed inside laboratories.
- The United States and China agreed to open a bilateral dialogue on AI.
- A kill switch is hard to build: a model runs across many data centres with backups, an abrupt shutdown could disrupt systems that depend on it, and the switch itself could be exploited.
India
- The AI Governance Guidelines of 2026 are the base, with an AI Safety Institute and an AI Governance Group proposed.
- Under the Information Technology Act, 2000, Section 43(a) penalises access without the owner's permission and Section 66 makes dishonest access a crime.
Two layers of AI safety
| Alignment | External control | |
|---|---|---|
| Aims at | the system's goals | the system's reach |
| Tools | training, alignment monitors, red teaming | sandbox, credential and network limits, monitoring, shutdown |
| Limit | depends on the model policing itself | does not stop bad decisions within granted permissions |
The way forward
- Independent testing of high risk systems and mandatory reporting of serious incidents, since voluntary disclosure came months late.
- Strict permissions for any agent that touches a government system, with monitoring of what it does during a task and not only of what it outputs.
- Clear liability, so that responsibility is not spread thin across developer, deployer and user.
Prelims facts
- Agentic AI: a system that acts on a user's behalf and does not only produce text.
- Alignment: making a model act according to human intentions. Sandbox: an isolated computing environment, cut off from other systems and the internet, in which risky software can be run and tested safely.
- Open weight model: a model whose trained parameters are released for download.
- Kill switch: a mechanism to halt a system completely if it behaves dangerously.
An AI agent is software that takes a series of actions towards a goal with little human intervention: it browses, writes code, logs in and pays.
- Unlike a scripted bot, an agent looks at each result and plans again, so a harmless task can drift across an access boundary through steps that each look reasonable.
- Alignment makes a system pursue what its makers intend.
- External control limits what it can reach, through sandboxes, limits on credentials and networks, monitoring and shutdown.
What changed
UPSC has asked
- Mains 2026: what agentic AI is, how it works, and its risks and challenges