What does AI mean for rural communities? 

Artificial Intelligence (AI) is a relatively new but rapidly growing technological field, and it is increasingly shaping the way we interact with the world. How is AI benefitting rural communities, and what more could it do? Jessica Sellick investigates. ………………………………………………………………………………………………..

AI technologies can be found in a wide range of everyday applications – from virtual assistants and navigation software, through to online banking and facial recognition. Artificial Intelligence (AI) is being deployed across many sectors to assist in tasks such as decision making and improving productivity. What is AI, and how can it be deployed to benefit rural communities? 

What is Artificial Intelligence? There is no universally agreed upon definition of artificial intelligence (AI) or AI technologies. Academics Haroon Sheikh, Corien Prins and Erik Schrijvers describe how it can be defined according to algorithms; by the imitation by computers of the intelligence inherent in humans; as a technology that enables machines to imitate various ‘complex human skills’; or an imitation or stimulation of something we do not yet fully understand ourselves: human intelligence! In seeking to define AI, research by psychologists, behavioural scientists and neurologists amongst others have opened up discussions around autonomy, human intelligence and innovation. All of this has led Haroon Sheikh and colleagues to adopt an open definition of AI where it applies to ‘systems that display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals’.  The Alan Turing Institute defines AI as ‘intelligence, such as reasoning, making decisions, learning from mistakes, communicating, solving problems, and moving around the physical world’. 

Back in March 2023 the Government published the UK Science and Technology Framework. This set out the Government’s strategic vision for AI as one of 5 critical technologies. The Framework described AI as ‘machines that perform tasks normally performed by human intelligence, especially when machines learn from data how to do those tasks’. The Framework reinforced the Government’s commitment to delivering the National AI Strategy published back in September 2021, which was focused around the UK being an AI superpower, supporting the transition to an AI-enabled economy, and ensuring the governance of AI technologies is right.   

The Office for Artificial Intelligence (Office for AI), part of the Department for Science, Innovation & Technology (DSIT), defines AI as ‘the use of digital technology to create systems capable of performing tasks commonly thought to require intelligence’. As a research field it spans philosophy, logic, statistics, computer science, mathematics, neuroscience, linguistics, cognitive psychology and economics. While the field is constantly evolving, it has two distinct characteristics: (i) it involves machines using statistics to find patterns in large amounts of data; and (ii) it involves the ability to perform repetitive tasks with data without the need for constant human guidance.   

In August 2023, the Office for AI published a policy paper on AI regulation. This further described AI systems, or AI technologies, as ‘products and services that are adaptable and autonomous’. The paper sets out how the Government intends to support innovation while providing a framework to ensure risks are identified and addressed. In October 2023 the House of Commons Science, Innovation and Technology committee opened an inquiry into the governance of AI. 

More recently, in November 2023, the Prime Minister hosted a global summit on AI safety. This resulted in 28 countries agreeing to the Bletchley Declaration on AI safety. The UK and United States Governments also announced new National AI Safety Institutes. 

Why is AI capable of, and what is driving its use? AI is being used for linguistic tasks, computer tasks, and to improve the performance of robots. Some examples include:

  • Natural language processing (NLP): AI is being used for speech-to-text converters, online tools that summarise text, for chatbots, and for speech recognition and translation. 
  • Programming computer systems: AI is being used on computer systems to assist in the interpretation of images and videos (e.g. facial recognition, medical imaging, video surveillance) and to develop driverless vehicles. 
  • Robotics: AI is being used on machines to carry out actions (e.g. medical robots performing surgery, self-navigating drones).  

These AI developments are possible because of advances in data, computing power and investment. Machine learning (ML) systems are training on larger data sets. For example, ChatGPT 3.5 was trained using 300 billion words obtained from internet text. The amount of computing power used to train ML models has increased – a Foundation Model released in 2020 used 600,000 times more computing power than a comparable Model in 2012. In March 2023 the Government announced a £900 million investment towards a new supercomputer, known as Isambard-AI, and hosted at the University of Bristol. UK Research and Innovation (UKRI) also announced a £10 million investment to a group of universities (including Bristol) and Hewlett Packard Enterprise, and a £30 million award to the Science and Technology Facilities Council’s laboratory in Cheshire. 

While these advances already are (and will be) impressive, of greater significance is their uptake by society. Discussion has moved on from digital transformation to generative AI whereby our everyday lives are increasingly being shaped algorithmically.  And while some citizens are excited by the possibility of AI saving time and handling mundane or difficult tasks, others worry about the loss of human jobs and fear AI will become so powerful it will outsmart people. 

There are also concerns around the concentration of Large Language Models in the hands of a few technology companies; privacy and data protection around the use of data to train AI models (data mining or harvesting at a mass scale); the ability of AI models to generate false information; and a lack of skills in the current UK workforce to develop AI.    

How are rural communities using AI – and what do they have to gain? 

McKinsey has estimated that generative AI technologies have the potential to add between $2.6 trillion and $4.4 trillion annually to the global economy. AI is already found across a wide range of sectors and services. Some examples of how AI is being developed or deployed in a rural context include: 

  • Agriculture: choosing optimum crops for weather conditions, monitoring crops, improving resource efficiency, monitoring livestock and detecting the early signs of disease, employing automated workers or tasks (e.g. robotic fertilisers). 
  • Transport: monitoring deliveries or traffic status, self-driving or autonomous vehicles, or ‘pay with your face’ to use public transport. 
  • Education: for adaptive tutoring or instructional assistants that explain difficult concepts to students; to undertake automated routine tasks around lesson planning; and drafting communications with parents/carers and the local community.  
  • Healthcare: medical imaging, patient monitoring, or identifying patients at higher risk of developing certain conditions. 
  • Policing: gathering intelligence, predicting crime and facial recognition tools.
  • Weather: to generate scenarios or develop algorithms that mine social media posts to learn from the language used and the pictures shared to deduce whether a weather event is happening and to what extent. 

While many of the issues rural communities face may be shared by their urban counterparts (e.g. access to services, economic development, climate change), how they are experienced and the solutions that are needed are different in a rural context. While much of the focus to date has been on the deployment of AI in agriculture, there are opportunities for rural communities to engage AI in other new and novel ways – with examples ranging from building weather/climate resilience through to reducing the impact of social isolation.  

Is there an (emerging) urban-rural AI gap? Some researchers suggest data is more likely to be harvested from rural residents than their urban counterparts. For example, a study by Douglas Leith on data being harvested by smart phones revealed Android phones give Google 20 times more data that iPhones send to Apple. On the one hand, this suggests that where android phones are more dominant in rural areas the user’s data is more likely to be harvested. But on the other hand, this potentially exposes rural users to some technological downsides regarding data privacy, protection and ownership. 

Similarly, once gathered, some evidence suggests data is more likely to be used to develop initiatives that benefit urban areas. For example, a scoping review by Jose Guerra and colleagues found that public health AI projects primarily used community based surveillance data collected from rural areas, with the harvested data often used to design public health initiatives of benefit to urban areas. 

Closing this rural AI gap is part of a much broader discussion around digital infrastructure and connectivity, rural proofing within policy-making and investment decision-making, and skills and expertise.  

Data analysis by Ofcom shows that while there has been an increase in gigabit-capable coverage in both urban and rural areas across the UK, there has been a greater increase in urban areas. This is because some providers tend to focus their roll-out on urban areas. Similarly, while the majority of homes and businesses have access to at least decent broadband on a fixed line connection, around half a million premises, mainly in rural areas, currently do not. While Ofcom is seeing small improvements in 4G coverage (partly as a result of initiatives such as the Shared Rural Network), the lack of reliable digital infrastructure and connectivity in some rural areas continues to hamper the ability of communities and businesses to benefit from AI.

Back in October 2022 the White House published a blueprint for an AI Bill of Rights to make automated systems work for the American people. This recommended that systems be made and audited with ‘consultation from diverse communities’, including communities in rural areas which have traditionally been underserved. The blueprint therefore gives rural communities are opportunities to shape the trajectory of these technologies. Back in the UK, while the Government and devolved administrations have published a range of documents and announced investments in AI, alongside a much wider range of parliamentary interest and activity, to what extent is rural proofing taking place? Defra, for example, has developed an AI Community of Interest for colleagues in Defra who are interested in building and sharing knowledge about AI.

There appears to be little consistency in the terminology used to describe the knowledge, skills and roles of AI. While research from Accenture found that 34% of UK organisations had scaled up AI technologies in response to COVID-19, only 27% of UK business leaders described their non-technical workforce as being well-prepared to leverage new technology. The Alan Turing Institute has highlighted the AI skills gap – running from early years through to senior positions in academia and industry. Without consistency in terminology or data collection, it is currently difficult to understand the skills gaps being experienced across the UK. It would be good to recruit and retain AI workers in rural areas – having more AI expertise in rural communities could help to both increase its benefits and take-up but also lead to greater understanding of its challenges.    

Where next? The big shift taking place here is not only around data, models, and tools or their capabilities; but their uptake and the profound ways in which they are impacting society. While many of us are starting to think about AI and its implications, if we don’t keep pace with developments, and indeed shape the agenda, AI is going to create its own vision of rural communities.  …………………………………………………………………………………………………

Jessica is a project manager at Rose Regeneration and a senior research fellow at The National Centre for Rural Health and Care (NCRHC). She is currently evaluating a service that supports older people to maintain their independence; and reviewing neighbourhood based initiatives (NBI). Jessica also sits on the board of a charity supporting  rural communities across Cambridgeshire. 

She can be contacted by email jessica.sellick@roseregeneration.co.uk. 

Website: http://roseregeneration.co.uk/https://www.ncrhc.org/ 

Blog: http://ruralwords.co.uk/ 

Twitter: @RoseRegen