UK Government’s AI Plan Raises Concerns Over Patient Data

Ambitious Plan to Foster Innovation Through AI

The UK government’s new plan to foster innovation through artificial intelligence (AI) is ambitious. Its goals rely on the better use of public data, including renewed efforts to maximise the value of health data held by the NHS. Yet this could involve the use of real data from patients using the NHS. This has been highly controversial in the past and previous attempts to use this health data have been at times close to disastrous.

Patient Data and Anonymity Concerns

Patient data would be anonymised, but concerns remain about potential threats to this anonymity. For example, the use of health data has been accompanied by worries about access to data for commercial gain. The care.data programme, which collapsed in 2014, had a similar underlying idea: sharing health data across the country to both publicly funded research bodies and private companies.

Poor Communication and Failure to Listen

Poor communication about the more controversial elements of this project and a failure to listen to concerns led to the programme being shelved. More recently, the involvement of the US tech company Palantir in the new NHS data platform raised questions about who can and should access data.

The Importance of Public Trust

The new effort to use health data to train (or improve) AI models, similarly relies on public support for success. Yet perhaps unsurprisingly, within hours of this announcement, media outlets and social media users attacked the plan as a way of monetising health data. “Ministers mull allowing private firms to make profit from NHS data in AI push,” one published headline reads.

The Trustworthiness Recognition Problem

These responses, and those to care.data and Palantir, reflect just how important public trust is in the design of policy. This is true no matter how complicated technology becomes – and crucially, trust becomes more important as societies increase in scale and we’re less able to see or understand every part of the system. It can, though, be difficult, if not impossible, to make a judgement as to where we should place trust, and how to do that well.

Signalling Theory and Overcoming the Trustworthiness Recognition Problem

The challenge of trust motivates what we call the “trustworthiness recognition problem” which highlights that determining who is worthy of our trust is something that stems from the origins of human social behaviour. The problem comes from a simple issue: anyone can claim to be trustworthy and we can lack sure ways to tell if they genuinely are.

Conclusion

If someone moves into a new home and sees ads for different internet providers online, there isn’t a sure way to tell which will be cheaper or more reliable. Presentation doesn’t need – and may not even often – reflect anything about a person or group’s underlying qualities. Carrying a designer handbag or wearing an expensive watch doesn’t guarantee the wearer is wealthy.

Luckily, work in anthropology, psychology and economics shows how people – and by consequence, institutions like political bodies – can overcome this problem. This work is known as signalling theory and explains how and why communication, or what we can call the passing of information from a signaller to a receiver, evolves even when the individuals communicating are in conflict.

FAQs

Q: What is the UK government’s plan to foster innovation through AI?
A: The plan aims to use public data, including health data held by the NHS, to train or improve AI models.

Q: What are the concerns about patient data?
A: Patient data would be anonymised, but concerns remain about potential threats to this anonymity, including access to data for commercial gain.

Q: What happened to the care.data programme?
A: The programme collapsed in 2014 due to poor communication and a failure to listen to concerns about the use of patient data.

Q: What is signalling theory?
A: Signalling theory is a concept that explains how people and institutions can overcome the trustworthiness recognition problem by demonstrating their commitment to the public through communication and action.