AI policy leaders’ series: in conversation with Jason Tucker, a researcher at the Institute for Futures Studies and an Adjunct Associate Professor, AI Policy Lab, Department of Computing Science, Umeå
A political scientist by training, we talk to Jason about how he bridges academic research and public policy and his work on the political economy of AI and UK healthcare.
Ensuring academic research supports evidence-based policymaking
I’m particularly fascinated by global politics and how, far from being an abstract concept, it shapes our everyday lives. For the past five years, I’ve been researching the fast-changing global political economy of AI and healthcare and how it is restructuring healthcare systems.
Most recently, I explored these issues in my paper Navigating the Global Politics of AI in Healthcare, written for the British Academy and the Carnegie Endowment for International Peace. Through this work, I collaborate with healthcare organisations from the local to the global level to support the responsible adoption of AI and build more resilient healthcare systems.
The UK reflects many of the broader global trends we see around AI
In the UK, there has been a strong emphasis on adopting AI to improve public services, particularly in response to the pressures facing the NHS. However, there has been much less discussion about where AI may not be the most appropriate solution or where existing non-AI-based approaches simply need greater investment.
Healthcare policy is increasingly shaped not only by domestic priorities but also by global technology markets, geopolitical competition and the concentration of AI capabilities within a small number of predominantly US firms.
The UK has a strong position as a leading middle power in AI and healthcare
It has world-class universities, an innovative AI ecosystem and one of the world’s most valuable public health datasets. At the same time, it operates within a global political economy where advanced AI infrastructure, cloud computing and foundation models are increasingly concentrated among a small number of US firms. That creates both opportunities and strategic dependencies.
The NHS faces immense pressures from underfunding, workforce shortages and growing demand, making AI an attractive solution. However, an AI-first strategy risks overlooking implementation challenges, costs and existing non-technical solutions that may simply require greater investment. A fragmented and poorly secured AI ecosystem would also create vulnerabilities to cyber-attacks and other forms of hybrid conflict. The uptick in Russian-sponsored attacks on European healthcare systems since the full-scale invasion of Ukraine illustrate these risks. Finally, the environmental costs of data-intensive AI remain largely overlooked: yet greatly impacting our health. Policymakers therefore need to ask not simply whether AI should be adopted, but under what conditions, whose technologies are being used and how the UK can build a resilient and sovereign healthcare system.
Once we start accounting for these trade-offs, we can better assess whether the hyperscale approach championed by the US, which the UK has sought to emulate, is the right one. There are alternative pathways that are more sustainable, more resilient, and better support UK sovereignty, while reducing dependence on a handful of foreign companies and states.
Yet, critical voices within the government are drowned out by a techno-solutionist narrative
Both the Sunak and Starmer governments have broadly prioritised rapid AI adoption, reflecting a wider framing that is also advanced by major US technology firms. While innovation is important, adoption should not become an end in itself.
The debate around the NHS’s contract with Palantir illustrates this tension. Indeed, the likely next Prime Minister, Andy Burnham, has stated that when taking office he wants to axe the NHS contract with the US-based company who is now synonymous with providing technology to the Israeli Defence Forces and the US Immigration and Customs Enforcement agency to commit human rights violations. Critics have questioned both the evidence supporting the company’s claims regarding the effectiveness of their technology and the wider political implications of relying on foreign technology providers for critical public infrastructure.
Ensuring meaningful participation
Responsible AI means thinking carefully about where these systems should be used and where they can deliver the greatest public benefit. A central principle of responsible AI is meaningful participation throughout the design, deployment and evaluation of these systems. Patients, clinicians, carers and all communities need to be involved. This does not slow innovation. It helps direct innovation towards areas where it can have the greatest positive public health impact, while building trust and supporting successful adoption.
Looking forward
The UK should be proud of its thriving health AI ecosystem. However, rather than pursuing the idea of becoming a global AI superpower, the greater ambition should be to develop AI that is accountable, resilient and genuinely aligned with the public interest. Achieving this will require the UK to be able to navigate an increasingly complex and treacherous global politics of AI and healthcare.
It’s also crucial to remember that the UK health AI ecosystem competitiveness stems from, in part, the UK being at the forefront of providing quality public health in the past: an edge it lost as of late. Any future where the UK is a leader in AI and healthcare will be based upon a robust and well-funded NHS.