{"id":9972,"date":"2025-07-10T09:48:58","date_gmt":"2025-07-10T08:48:58","guid":{"rendered":"https:\/\/www.gpsj.co.uk\/?p=9972"},"modified":"2025-07-10T09:48:59","modified_gmt":"2025-07-10T08:48:59","slug":"if-ai-is-widening-the-digital-divide-what-can-be-done-to-stop-it","status":"publish","type":"post","link":"https:\/\/www.gpsj.co.uk\/?p=9972","title":{"rendered":"If AI is widening the digital divide, what can be done to stop it?"},"content":{"rendered":"\n

AI\u2019s increasing presence poses a real danger of making an already sizable digital literacy divide worse. Jon Rimmer, CXO at <\/em>Mercator Digital<\/em><\/a>, says governments and organisations have a responsibility to bridge this gap, explaining both why and how.<\/em><\/p>\n\n\n

\n
\"\"
Jon Rimmer<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n

New technology is designed – or at least is meant to be designed – to make life easier. In the UK, for example, people can now renew passports online, file taxes through HMRC\u2019s digital service, receive emergency alerts on their phones, and even attend virtual Jobcentre appointments – all in the name of saving time and improving access.<\/p>\n\n\n\n

However, for those with low digital literacy, advancements in technology can in fact do the exact opposite, further excluding people who are already marginalised.<\/p>\n\n\n\n

According to recent research, 8.5 million people lack basic digital skills<\/a>, of which a large proportion include those living in poverty. In fact, 3.7 million families fall below the Minimum Digital Living Standard<\/a>, facing barriers such as limited internet connectivity, outdated devices, and insufficient digital literacy support.<\/p>\n\n\n\n

Similarly, both older people and those with physical and mental disabilities often encounter accessibility issues that make digital tools frustrating or even unusable. Again, this can be down to equipment and connectivity issues, but lack of confidence or skills to utilise technology also come into play. A survey of people with severe mental illness, for example, found that 42%<\/a> lacked basic digital skills, such as changing passwords or connecting to Wi-Fi.<\/p>\n\n\n\n

While it\u2019s already widely acknowledged that digital exclusion disproportionately affects the above-mentioned groups, even beyond the barriers of access and affordability, 21%<\/a> of people still say they feel left behind by technology.<\/p>\n\n\n\n

These are all high figures that, with the advent of AI, are at risk of rising.<\/p>\n\n\n\n

The impact of AI on digital exclusion<\/strong><\/p>\n\n\n\n

AI of course has the potential to drastically improve public services, healthcare, education, and employment. But, if not carefully designed and implemented, it also risks deepening digital exclusion.<\/p>\n\n\n\n

For those already struggling to use digital systems, AI adds complexity to interactions. Chatbots and automated interfaces, for example, are becoming increasingly common in settings like healthcare and social services, where human interaction is often essential. These tools can confuse users with low digital literacy or those experiencing mental health challenges, creating yet another barrier between vulnerable individuals and the services they need.<\/p>\n\n\n\n

Looking beyond usability, there\u2019s also a deeper structural problem: the data used to train most AI models is inherently biased. These datasets are often pulled from the web, where information has historically been shaped by academic, technical, and hobbyist communities  (think Western, white, middle-class, English-speaking men). As a result, marginalised groups are significantly underrepresented, leading AI systems to reflect and reinforce existing social inequalities; a problem further propagated through continued use.<\/p>\n\n\n\n

And this is not just theoretical. Take the COVID-19 pandemic as an example, where the impact of AI bias was clear as day – an AI system downgraded exam results for 39%<\/a> of students, disproportionately affecting those from disadvantaged schools.<\/p>\n\n\n\n

We\u2019ve seen other examples in healthcare too, with recent research<\/a> showing that AI systems being developed to diagnose skin cancer run the risk of being less accurate for people with dark skin, simply because the data used is predominantly from those with lighter skin. In this case, it\u2019s a disparity with potentially life-threatening implications.<\/p>\n\n\n\n

Four strategies to bridge the digital divide<\/strong><\/p>\n\n\n\n

For me, governments and organisations have a responsibility to address both this bias and the widening digital divide. If not, they risk a huge proportion of the public feeling annoyed, isolated and inadequate – and that\u2019s at best. At worst, there\u2019s a real risk that the needs of the UK\u2019s most vulnerable people are not met; which in turn causes significant implications for education, employment, and health and wellbeing.<\/p>\n\n\n\n

With this in mind, below are some practical strategies for ensuring AI-powered services are intuitive, inclusive, and adaptable to different needs:<\/p>\n\n\n\n

    \n
  1. Embed inclusive research as a foundational step in service design: <\/strong>In order to ensure inclusion from the outset, robust user research is key. When it comes to the design of Government Digital Services, researchers have always held the responsibility of amplifying the voices of marginalised and digitally excluded groups, ensuring that services derived are effective and usable for all. Researchers also play a key role in the ethical and responsible uses of AI, which is something that must continue into every stage of service design. Prioritising researchers\u2019 work here helps uncover the real-world challenges people face, not just assumptions based on the experiences of digital natives.<\/li>\n<\/ol>\n\n\n\n

    While there is already a well-established community of researchers across Government, there is always more that can be done to share findings across Departments that will relate more widely than the individual project to which a researcher is assigned.<\/p>\n\n\n\n

    We also need to continue validating digital services with representative users at every stage of development, using insights from government researchers to shape and test design decisions.<\/p>\n\n\n\n

      \n
    1. Apply the MASTA framework to AI inclusion:<\/strong><\/li>\n<\/ol>\n\n\n\n