Regulating AI seems like an impossible task, but ethically and economically, it’s a vital one

AI has already transformed industries and the way the world works. And its development has been so rapid that it can be hard to keep up. This means that those responsible for dealing with AI’s impact on issues such as safety, privacy and ethics must be equally speedy.

But regulating such a fast-moving and complex sector is extremely difficult.

At a summit in France in February 2025, world leaders struggled to agree on how to govern AI in a way that would be “safe, secure and trustworthy”^[1]. But regulation is something that directly affects everyday lives – from the confidentiality of medical records to the security of financial transactions.

One recent example which highlights the tension between technological advancement and individual privacy is the ongoing dispute^[2] between the UK government and Apple. (The government wants the tech giant to provide access to encrypted user data stored in its cloud service, but Apple says this would be a breach of customers’ privacy.)

It’s a delicate balance for all concerned. For businesses, particularly global ones, the challenge is about navigating a fragmented regulatory landscape while staying competitive. Governments need to ensure public safety while encouraging innovation and technological progress.

That progress could be a key part of economic growth. Research suggests^[3] that AI is igniting an economic revolution – improving the performance of entire sectors.

In healthcare for example^[4], AI diagnostics have drastically reduced costs and saved lives. In finance, razor-sharp algorithms cut risks and help businesses to rake in profits^[5].

Logistics firms have benefited from streamlined supply chains^[6], with delivery times and expenses slashed. In manufacturing, AI-driven automation has cranked up efficiency^[7] and cut wasteful errors.

But as AI systems become ever more deeply embedded, the risks associated with their unchecked development increase.

Data used in recruitment algorithms for instance, can unintentionally discriminate^[8] against certain groups, perpetuating social inequality. Automated credit-scoring systems can exclude people unfairly^[9] (and remove accountability).

Issues like these can erode trust and bring ethical risks.

A well-designed regulatory framework must mitigate these risks while ensuring that AI remains a tool for economic growth. Over-regulation could slow development and discourage investment, but inadequate oversight may lead to misuse or exploitation.

International intelligence

This dilemma is being treated differently across the world. The EU for example, has introduced one of the most comprehensive regulatory frameworks^[10], prioritising transparency and accountability, especially in areas such as healthcare and employment.

While robust, this approach risks slowing innovation and increasing compliance costs for businesses.

In contrast, the US has avoided sweeping federal rules, opting instead for self-regulation in specific industries^[11]. This has led to rapid AI development^[12], particularly in areas such as autonomous vehicles and financial technology. But it also leaves regulatory gaps and inconsistent oversight^[13].

China meanwhile uses government-led regulation^[15], prioritising national security and economic growth. This brings major state investment, driving advances in things such as facial recognition and surveillance systems, which are used extensively^[16] in train stations, airports and public buildings.

These varying approaches demonstrate a lack of international agreement about AI. And they also pose significant challenges for businesses operating globally.

Companies must now comply with multiple, sometimes conflicting AI regulations, leading to increased compliance costs and uncertainty.

This fragmentation could slow down AI adoption as firms hesitate to invest in applications that could become non-compliant in some countries. A globally coordinated regulatory framework seems increasingly necessary to ensure fairness and promote responsible innovation without excessive constraints.

Innovation vs regulation

But again, achieving this kind of framework would not be easy. The impact of regulation on innovation is complex and involves careful trade-offs.

Transparency, while essential for accountability, could mean sharing new technology, potentially eroding competitive advantages. Strict compliance requirements, crucial in industries such as healthcare and finance, can be counterproductive^[17] where rapid development is vital.

Effective AI regulation should be dynamic, adaptive and globally harmonised, balancing ethical responsibilities with economic ambition. Companies that actively align with ethical AI standards are likely to benefit from improved consumer trust.

For now, in the absence of global agreement, the UK has chosen a flexible approach, with guidelines set by independent bodies such as the Responsible Technology Adoption Unit^[18]. This model aims to attract investment and encourage innovation by offering clarity without overly rigid constraints.

With a robust research ecosystem, world-class universities and a skilled workforce, the UK has a solid foundation^[19] for AI-driven economic growth. Continued investment in research, infrastructure and talent are essential.

The UK must also stay proactive in shaping international AI standards. For achieving effective AI governance that is safe and trustworthy, will be key to securing its future as an engine of economic and social transformation.

References

1. ^{^} “safe, secure and trustworthy” (www.bbc.co.uk)
2. ^{^} ongoing dispute (www.bbc.co.uk)
3. ^{^} Research suggests (www.sciencedirect.com)
4. ^{^} healthcare for example (www.sciencedirect.com)
5. ^{^} rake in profits (www.europeanbusinessreview.com)
6. ^{^} streamlined supply chains (www.forbes.com)
7. ^{^} cranked up efficiency (www.irishtimes.com)
8. ^{^} unintentionally discriminate (theconversation.com)
9. ^{^} exclude people unfairly (www.tandfonline.com)
10. ^{^} regulatory frameworks (www.europarl.europa.eu)
11. ^{^} self-regulation in specific industries (www.elgaronline.com)
12. ^{^} rapid AI development (www.weforum.org)
13. ^{^} regulatory gaps and inconsistent oversight (kennedyslaw.com)
14. ^{^} frank60/Shutterstock (www.shutterstock.com)
15. ^{^} uses government-led regulation (www.cambridge.org)
16. ^{^} used extensively (link.springer.com)
17. ^{^} can be counterproductive (www.forbes.com)
18. ^{^} Responsible Technology Adoption Unit (rtau.blog.gov.uk)
19. ^{^} solid foundation (link.springer.com)