Unlocking science: building AI researchers can trust

Date:
Fri, 01 May 2026 11:01:16 +0000

Description:
Over half of researchers now say they use AI tools in their work, but only
two in ten believe generic AI tools are trustworthy.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Productivity is a pressing problem for many governments, and the UK is no exception.

That pressure is trickling down to British researchers as the driving force propelling the UK towards the top of international R&D rankings, based on the quality and impact of their research. International competition is hotting
up, however, and UK researchers will need to do more to maintain the UKs global standing. Article continues below You may like Rebuilding trust in AI with responsible adoption Why early-career investment and AI training matter for tackling the productivity crisis Trust by design: How much can you really trust your AI agent Maxim Khan Social Links Navigation

SVP for Academic and Government Solutions, Elsevier. Wider trends in the research sector compound this pressure. Globally, only 45% of researchers
said they have sufficient time for research, and just 33% expect funding to increase in the next two to three years.

AI is often held up as a productivity silver bullet. The sheer value of the UKs investment into AI technologies collectively, billions of pounds shows that while productivity is not the only goal, its certainly towards the top
of the list.

The Governments AI for Science strategy specifically champions the idea of using AI to transform scientific productivity and progress. AI for research: not as simple as it sounds Its no surprise that AI tools are being adopted by researchers and integrated into their workflows the problem is that they're being adopted faster than governance can keep up. Over half of researchers
now say they use AI tools in their work, but only two in ten believe generic AI tools are trustworthy. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features
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Compounding these problems are the systemic challenges of AI use generic AI tools tend to simplify responses, smoothing out detail and in the process, erasing a degree of accuracy, transparency and context. For lay purposes, these problems are not insurmountable.

But for researchers, who operate at the cutting edge of reproducibility and scrutiny, its simply not enough. Many talk about measuring AI productivity in terms of what AI can produce vs what a (slower) human can produce.

This ignores the trustworthiness of the AI's output: if a human needs to verify everything the AI tool produces, it unravels all the so-called productivity gains. What to read next AI confidence gap is stalling UK small businesses AI in schools: the fastest route to a more resilient and skilled workforce The AI gap nobody's talking about Building AI tools for research These problems shouldn't prevent researchers from using AI with confidence, but first, the problem needs to be reframed. As with any tool, its about picking the right tool for the job. To date, researchers havent had the right tools.

The solution can be seen in many other fields: specialized tools for specialized jobs. Doctors use ECG monitors, not smartwatches, to measure
heart rates; commercial builders use automated production systems for precision measurements, not a tape measure.

While lay tools will do the job for fitness fans who want to monitor their heart rate, or DIY home improvement enthusiasts, the professionals use industrial-grade products that arent available on the high street.

The same right tool for the right job principle should apply to AI in research. Professional AI tools designed with researchers highly-specialized requirements in mind, that they can importantly trust to do the task at hand.

Once that trust is established, it removes the need for excessive scrutiny of the AIs output. If researchers dont need to waste time on this, its a real productivity gain not one that looks good on paper, but pointless in practice. What does the right AI tool for researchers look like? Firstly,
what it doesnt look like is an assumption you need to ask the researchers themselves. In working closely with the community, weve learned that researcher-grade AI has four key ingredients:

- Researchers want tools that support critical thinking tools that flag any uncertainties in what they have produced, rather than glossing over them.

- AI-generated insights should be contextualized, transparent and traceable meaning that the AI tool shows its thinking and what sources were used to reach those conclusions.

- "Garbage in, garbage out" applies to an extreme degree in research. Peer-reviewed research is the gold standard and it should be brought together on publisher-neutral platforms to ensure that researchers can see the whole picture, not just a part of it.

- Finally, everything must be grounded in a foundation of data privacy , security and responsible use of AI.

In the world of research, AI must jump through more hoops to earn trust. It must be transparent about its sources, protect sensitive data and support human judgment rather than attempt to replace it.

Achieving that goal requires moving beyond generic AI and toward systems
built with rigor and responsibility at their core. We've ranked the best online learning platforms . This article was produced as part of TechRadar
Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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