Beyond AI users: Why the government needs AI translators

Indian government officials have more than 3.3 crore course completions on AI and emerging technologies in the iGOT Karmayogi platform. That is a large and growing base of AI users across government. The harder question is who, inside a ministry or a district office, can tell when an AI system is confidently wrong, and who makes sure a citizen does not pay for it.

India has already set the direction. MeitY’s India AI Governance Guidelines call for human oversight, accessible grievance redressal, and training for officials in AI risk management and responsible procurement. Putting these principles into daily practice calls for a new kind of professional: the AI translator.

What is at stake

In the Netherlands, the tax authority built a self-learning model to spot fraud in childcare benefits. One of the factors it weighed was a parent's nationality. About 26,000 parents were wrongly accused and told to repay their benefits in full, and in January 2021 the Dutch government resigned over the scandal.

The engineers had built what they were asked to build, and officials acted on risk scores they did not question. Yet a benefits expert, asked whether nationality belonged in a fraud model, would likely have objected at once. Nobody asked. The failure sat in the gap between the technology and the administration, and no one was responsible for that gap.

Part of the difficulty is that AI systems can sound certain even when they are wrong. A language model presents an invented citation in the same calm tone as a real one. A hesitant colleague usually sounds hesitant, but a machine offers no such cue.

Courts are seeing the same problem. A public database of court decisions involving AI-fabricated material, maintained by the researcher Damien Charlotin, grew from about 200 cases in mid-2025 to nearly 1,600 by June 2026. India's Supreme Court has taken a firm stand. In July 2026, it set aside NCLT and NCLAT orders that relied on judgments which do not exist and called for zero tolerance towards unverified AI-generated precedent.

Careful checking would have caught these errors, but checking takes time that busy professionals rarely have, and citizens are seldom in a position to check at all. In 2022, a passenger in Canada asked Air Canada's chatbot about bereavement fares and was told of a refund option the airline did not offer. When the dispute reached a tribunal, the airline tried to distance itself from that answer and was held liable. A citizen asking a government chatbot whether she qualifies for a scheme should never be in that position, which is why protection has to be built into the system itself.

A changing role for the civil servant

AI is changing what officers do. More of their work will involve directing and checking systems that produce the first draft, the first sort or the first flag. Every officer will need to understand AI well enough to use it with judgement. Departments will also need a specialist layer of people who understand it deeply enough to shape it.

That is the translator's role. A translator knows enough about how AI systems work to anticipate where they may fail, enough about administration to see what a failure would cost a citizen, and enough of both to change the design before a system goes live.

Much of the work is bringing the right people together. On a government AI project, the technologist watches an accuracy score. The domain expert knows the rules and, more importantly, the exceptions. The frontline officer sees a flag she must act on this morning, and the administrator sees a metric moving in the right direction. The translator gets all four into one room before the contract is signed and asks two questions: where could this go wrong, and what happens to the citizen when it does? In the Dutch case, that one conversation might have been enough.

Designing for error

Conventional software gives the same output for the same input, and a wrong answer is a bug to fix. AI works on probabilities, so some errors will occur however good the system is, and good design plans for them. Each system needs a clear fallback for when it is unsure, with certain decisions reserved for a human signature. Citizens need a grievance route with a deadline and staff behind it, one that is easy to find and able to reverse a decision. Officers need real authority and enough time to exercise it, since no one can properly review two hundred flags an hour.

Some governments are already writing such safeguards into their rules. In the United States, federal agencies must designate Chief AI Officers, and AI uses classed as high-impact must go through pre-deployment testing and human oversight, with a route for affected people to seek remedy.

Building the cadre in three steps

First, build the talent. Translators can come from two directions: experienced officers who add serious technical grounding, and technologists who spend enough time in districts and departments to understand how administration works. A dedicated translator track, developed through Mission Karmayogi and the IndiaAI Mission together with universities, non-profits and industry, could give both groups a common standard, backed by a recognised certification.

Second, place translators where decisions are made, in ministry and state digital units and close to procurement. Before an AI system that affects citizens' entitlements is bought, a translator would review how it could fail and confirm that a fallback and a working grievance route are in place. Starting with a few departments that deliver high-volume services would create working models that others can adopt.

Third, give the role standing. Translators need the authority to pause a deployment that is not ready, and a career path that values problems caught early as much as systems launched.

The benefits would reach well beyond government. A better-informed buyer steers vendors towards products that work in the field, and Indian AI firms that design for oversight and redressal from the start will be better placed to serve governments at home and abroad.

India is already producing AI users in government at remarkable scale. The next step is to add AI translators alongside them: people who can say when a confident system is wrong, and make sure the citizen is protected when it is.

(Kamal Das is Dean, Wadhwani Government AI Academy, Skills Development Network.)

(The opinions expressed in this article are those of the author and do not purport to reflect the opinions or views of THE WEEK.)