Where we will focus

AI and biometric technologies are already in use across a wide range of contexts –  from law enforcement, to education, to healthcare. While our regulatory interest spans the full breadth of these developments, this strategy targets three priority situations where: 

• the stakes for people are high;
• public concern is clear; and
• regulatory clarity can have the most immediate impact.

These are high-impact cases with significant potential for public benefit, but they also concentrate many of the risks people care most about:

• the development of foundation models — large-scale models trained on vast datasets and adaptable to a wide range of downstream tasks;
• the use of automated decision-making in recruitment and public services; and
• the use of facial recognition technology by police forces.

By raising standards here, we aim to create clear regulatory expectations and scalable good practice that will influence the wider AI and biometrics landscape.

Within these cases, we consistently see public concern forming around three cross-cutting issues:

• transparency and explainability;
• bias and discrimination; and
• rights and redress.

Transparency and explainability

People expect to understand when and how AI systems affect them. But across generative AI, ADM and FRT, the picture is often unclear.

In recruitment, people want to know when organisations use automated tools and how they make decisions. 

In generative AI, users call for greater clarity on how organisations develop and train tools. 

And in policing, concerns about fairness and privacy are closely linked to how well they can explain and justify FRT decisions.

Bias and discrimination

People are concerned that AI systems can replicate or amplify bias, particularly when trained on flawed, incomplete or unrepresentative information.

In recruitment, people fear that automated tools may reflect and reinforce social inequalities, particularly in how candidates are ranked or filtered. 

In generative AI, users have observed biased outputs and expect developers to take visible action to address them. 

In facial recognition, public trust is shaped by whether systems perform consistently across different demographic groups. Independent testing of facial recognition algorithms demonstrates differential performance according to gender and ethnicity.¹³

Risks of bias and discrimination are particularly acute in speculative AI systems that attempt to infer traits, emotions or intentions from physical or behavioural characteristics — such as emotion recognition tools used in recruitment. Research shows that the science underpinning these systems is highly contested and that their use has the potential to lead to harms.¹⁴

Rights and redress

Public confidence in AI and biometric technologies is shaped most powerfully by what happens when things go wrong.

People are concerned about the serious consequences of inaccurate outputs and unfair outcomes, for example:

• being wrongly identified by facial recognition;
• being overlooked for a job due to flawed automation; or
• losing access to benefits through error-prone ADM. 

In generative AI, models can reproduce sensitive personal information, including medical details or child sexual abuse material.¹⁵  These are not abstract risks — they affect lives and livelihoods.

People want to know that:

• systems are accurate;
• safeguards are in place; and
• there are clear ways to challenge and correct outcomes when harm occurs.

As AI systems become more agentic, the ability to trace decisions back to a human controller becomes harder — and people may struggle to understand how decisions were made or how to challenge them. This creates real risks around accountability and redress. 

 

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13 Face recognition technology evaluation, NIST, 2025.
14 Biometrics: foresight, ICO, 2022.
15 Viewing generative AI and children’s safety in the round, NSPCC, 2025.