For over a decade, my work has focused on one core idea: AI will only serve society well when the next generation is equipped not just to use it, but to question it, improve it, and use it ethically. Building a responsible AI future requires embedding education, ethics, and inclusion into every layer of national AI strategy – because tomorrow’s innovators are already here.

Inclusion as infrastructure

AI is often described as a tool for efficiency or productivity, but at its heart, it reflects human values. The data we use, the assumptions we make, and the people we include (or exclude) determine whether technology strengthens or fractures our society.

At Teens in AI, we run programmes for 12–18-year-olds in over 100 countries. In 2024, 80% came from diverse ethnic backgrounds, 69% attended state schools and 57% identified as girls. These numbers show the diversity of voices shaping future-facing AI.

In the UK, women make up just 22% of the AI workforce and people from minority ethnic backgrounds only 15% of tech roles.

As we come to the end of 2025 and move into 2026, a key part of our strategy is to ‘teach the teachers’. We provide educators with ready-to-use materials and resources so they can introduce AI concepts confidently, even without prior experience. By equipping teachers as facilitators and learners, we help schools embed AI literacy sustainably, ensuring understanding spreads far beyond any single programme.

Behind every AI use case is a decision: Who builds it, whose assumptions shape it, whose voices are absent. When we start AI literacy early, we embed ethical reasoning, critical thinking, empathy and inclusion into the design process – and that matters for fairness, accountability and social trust.

Why Governments must rethink AI Education

Governments worldwide are grappling with how to build AI capacity responsibly. In the UK, the Department for Science, Innovation and Technology has acknowledged that addressing the AI skills gap is critical to meeting the ambitions set out in the National AI Strategy. Yet progress remains uneven, with most initiatives focusing on adult reskilling or postgraduate study rather than early education. This risks leaving a generation behind.

The World Economic Forum’s Future of Jobs Report 2025 warns that 85 million jobs may be displaced globally, while 97 million new roles will emerge – demanding creativity, ethics, and digital fluency developed long before university

I regularly contribute to government discussions on AI education policy. Most recently, at the UKAI Roundtable at Parliament, I joined policymakers to emphasise the urgent need to equip young people with the knowledge and confidence to thrive in an AI-driven world. There was a shared understanding that to future-proof our economy, we must start with schools – but this requires long-term investment and collaboration across government, industry, and civil society.

On 5 November 2025, the UK Government published its response to the Curriculum and Assessment Review: Building a World-Class Curriculum for All – a long-awaited step towards embedding AI literacy and digital competence across the national curriculum. The response signals an important shift in recognising that AI is not just a technology issue but an education one. I welcome this direction. It’s been a long time coming.

For years, many in education and technology have called for a curriculum that keeps pace with social and technological change. The Review’s emphasis on AI, digital literacy, and critical thinking reflects a growing consensus that early AI education is essential not only for future employability but for civic understanding and ethical reasoning. As these recommendations move from policy to practice, collaboration between government, educators, and social innovators will be vital to ensure that every young person benefits from this shift.

For the past decade, we have shown how equipping young people early with ethical and practical understanding of tech and AI can inspire innovation and social good. The government’s commitment to creating an agile curriculum and embedding technology across all subjects aligns closely with what we see daily in classrooms worldwide: when young people understand AI’s real-world context, they develop curiosity, empathy and confidence to lead responsibly in the future workforce.

The UK Government’s White Paper on AI Regulation proposes a pro-innovation model, where regulators apply five principles: Safety, Accountability, Transparency, Fairness, and Contestability. While legislation is not yet fully in place, this framework underscores why early education in AI literacy and ethics matters. Globally, frameworks such as UNESCO’s AI Competency Guidelines for Schools are setting a new benchmark for how countries embed AI understanding into national curricula.

If AI is to serve the people, not just profit, we must reimagine what progress looks like. For governments, this means treating AI literacy as a civic skill – as essential as reading or numeracy – and ensuring every young person, regardless of background, has the chance to participate.

What the Private Sector can do differently

The private sector has a unique opportunity to drive social impact while addressing its own talent shortages. The UK AI Opportunity Action Plan highlights that demand for AI skills far outpaces supply, with 60% of businesses citing recruitment as their biggest barrier to AI adoption.

This year’s wave of layoffs reveals a short-sighted pattern. As organisations replace junior roles with AI systems, they may gain short-term productivity but lose long-term capacity to grow human expertise. Every algorithm trained today still depends on people who can ask better questions tomorrow. Forward-thinking companies recognise that sustaining innovation means investing in young minds, not eliminating them.

This is where partnership matters. We collaborate with global organisations including Sage, Capgemini and Red Hat to strengthen AI education while advancing social good. Together, we co-design real-world challenges that equip young people with the skills, ethics and confidence to build responsible AI solutions. For our partners, this is not philanthropy – it is strategic investment in a diverse, future-ready workforce that reflects the values and needs of a fairer digital economy.

When organisations share mentorship and technical expertise, young people learn not just how to build with AI, but why to build responsibly. In turn, businesses gain imaginative, ethical, and globally aware insights from the next generation.

Education as the foundation for responsible AI

AI will continue to evolve faster than most institutions can regulate it. The only scalable safeguard is education – for students, teachers, parents, policymakers, and employers. We must move from ‘awareness’ to ‘agency’.

That is why our focus is not just on teaching coding. We integrate the United Nations Sustainable Development Goals into every programme, fostering a culture of ethical inquiry and social responsibility. When young people understand bias, fairness and sustainability, they carry these principles into every innovation they create. Our alumni now lead university projects, apprenticeships and careers in AI ethics, data science and policy – proof that early investment builds lifelong impact.

The choices we make today about who gets to learn, create and lead with AI will define the values that underpin our future. As we look ahead to 2026, our ambition is to make AI literacy as universal as literacy itself – accessible across subjects, languages and cultures. If we want technology that reflects the best of humanity, we must invest in the people who will build it. The time to act is now – not when the gap has already widened, but while we still have the chance to shape it together.

Read More Workforce & AI Skills

The post Who gets to shape the future of AI? Building the foundations for a fair future appeared first on Digital Leaders.

2025 marks a decisive moment in our relationship with artificial intelligence. Across governments, industries, and communities, we are asking not only what AI can do — but who it serves and who it might exclude.

For years, the promise of AI innovation has coexisted with a growing awareness of its blind spots: bias, lack of representation, and the replication of structural inequalities through data. The challenge now is to ensure AI evolves as a tool for social repair, not reinforcement of injustice.

As an academic working on the intersections of AI, community safety, and digital inclusion, I have seen both the harm and the hope that accompany technological progress. The future depends on whether we can make AI more human-centred, transparent, and inclusive.

Seeing the streets differently: Turning hate into insight

Through my project StreetSnap, we are implementing image recognition to identify and analyse hateful graffiti in public spaces – turning what is often dismissed as vandalism and nuisance into real-time data about belonging and exclusion. 

By combing image recognition, practitioner reporting and creative arts interventions through the sister project of Flip the Streets, StreetSnap enables local authorities and residents to respond together, replacing hate with creativity.

This work reimagines what AI can be. Instead of a distant, data-hungry system, it becomes a lens of empathy – mapping the stories that shape our shared spaces. 

If you can’t see the data, you can’t see the problem – but if you can’t see the people behind the data, you can’t solve it.

Listening as data: The power of lived experience

Bias in AI is rarely just a technical flaw; it reflects the social inequalities embedded in our datasets. Communities most affected by discrimination are often the least represented in the data used to design digital systems. 

That insight inspired the work that is being developed on the Lived Experience Repository of Racism in Wales – a soon to be open digital archive that gathers existing studies and testimonies from people who have experienced racism and exclusion. This platform ensures that policy, research and innovation are guided by real stories, not abstract statistics. 

The lesson is simple: inclusion starts with listening. StreetSnap listens to the language of the streets; the repository listens to the language of lived reality. Both show that narratives are data too – essential for designing technology that reflects, rather than erases, human experience. 

Beyond bias: Building accountability into AI

Much of the public conversation about AI ethics remains focused on mitigating bias, but we need to go further. Accountability means asking who participates, not just how the algorithm performs. 

In these projects, I work with artists, young people, community safety teams and policymakers to co-design how data is gathered and interpreted. When diverse groups are part of the design process, the outcomes are not just more ethical – they are more trusted, relevant and resilient. 

True AI accountability cannot be achieved through audits alone. It depends on shared ownership, where communities shape the tools that shape their lives and experiences.

Digital inclusion as democratic infrastructure

Digital inclusion is often described as an access issue – broadband, devices, or skills – but it is also a democratic issue. If participation in digital systems determines access to services, safety and opportunity, then inclusion is foundational to social justice. 

Projects like StreetSnap and the repository demonstrate how inclusive AI begins with inclusive story telling. By valuing local knowledge, creativity and lived experience, we are not only collecting better data – we are redefining whose experiences matter in the digital public sphere.

This approach echoes the broader shift in responsible AI practice: from designing systems for people to designing them with people. 

The opportunity ahead: AI for human connection

The next phase of AI development must move beyond efficiency toward empathy. To build AI for good, we must reimagine what ‘good’ looks like in practice.

That means:

• Embedding ethical reflexivity into every AI project — continually asking who benefits, who is visible, and who is missing.
• Merging data and creativity, making social issues visible and actionable through digital storytelling.
• Empowering communities to co-create and govern the technologies that impact them.
• Bridging sectors — academia, policy, tech, and the arts — to ensure innovation serves the public good.

AI can, and should, help us see ourselves more clearly — not just predict outcomes, but reflect our values.

Key takeaways for leaders

• Prioritise representation: Build datasets that capture the diversity of lived experience, not just convenience or scale.
• Design with empathy: Invite communities into the design process to ensure AI reflects the realities it seeks to address.
• Invest in digital inclusion as social infrastructure: Equity in data access and participation is essential for ethical innovation.

Looking forward

Recognition in the Digital Leaders AI 100 list reflects not only technological progress but a growing movement toward ethical, community-driven innovation. 

As we look ahead, the most transformative AI systems will not be those that think like humans, but those that help humans think more compassionately about one another.

Read More AI for good

The post Reclaiming digital spaces: Building inclusive AI through lived experience and street data appeared first on Digital Leaders.

For Unilink, a long-standing provider of secure digital solutions across justice and public safety, AI represents both an extraordinary opportunity and a complex responsibility. Over the last 30 years, we’ve built and deployed digital solutions for prisons, probation services, immigration detention, and secure hospitals. Now, the inclusion of AI across our product suite is opening new possibilities; not simply the automation of routine tasks, but the augmentation of decision-making, personalised offender support, risk modelling, and operational insight.

From digital transformation to digital justice

The Ministry of Justice’s (MoJ) digital strategy sets out an ambitious vision: a justice system that works better for everyone, through the smarter use of data, automation, and design. In 2025, this vision is taking shape. AI and digital technologies now support everything, from digital court processes and secure communication to case management and prisoner engagement. 

AI’s real potential, however, lies in turning complex, fragmented data into meaningful insight. Machine learning models can identify trends in case workloads, forecast resource needs, or highlight where individuals may be at risk of disengagement. Predictive analytics can improve safety and planning, while automation reduces the administrative burden and frees staff to focus on more humane services that are grounded in evidence and insight.

Ethical and transparent by design

However, as AI becomes more deeply embedded in justice operations, ethical and responsible deployment is essential. The sector deals with some of society’s most sensitive personal data, and the consequences of algorithmic decisions can be profound. We believe that for digital justice to succeed, ethical governance and human oversight must remain central.

That means explainable AI – systems that can show how and why a decision was reached. It means rigorous data governance that ensures quality, security, and fairness. And it means maintaining human oversight at every stage. Technology can support better judgment, but it should never replace it. Embedding these principles will help to ensure public trust, protect individual rights, and deliver genuinely fair outcomes. 

Human-centred services

Digital justice is about people as much as technology. When designed around user needs, it can transform the experience of both service users and staff, and make justice more accessible and empowering. Adaptive interfaces can support users with low digital literacy or those for whom English is a second language. Chatbots or virtual assistants can provide 24/7 access to essential information. Data visualisation tools can give frontline workers a clearer view of progress, helping them to intervene earlier and more effectively. 

Crucially, digital services can help individuals to build the digital confidence and skills that extend beyond the justice system, supporting rehabilitation, employability, and reintegration into society, and enabling long-term positive change. In this context, technology isn’t just modernising justice, it’s humanising it. 

Innovations that are shaping 2025 and beyond

Looking ahead, Unilink is focused on three key innovation strands for 2025 and beyond:

1. Adaptive digital services – in-cell devices or wing kiosks that don’t simply offer menus or book visits but learn from user interactions to improve accessibility and engagement
2. Predictive analytics for smarter operations – using AI to anticipate demand and improve resource allocation
3. Integrated data ecosystems – securely connecting justice, health, and rehabilitation data to provide a holistic view of an individual’s journey.

These innovations demonstrate just how AI can enhance transparency, improve outcomes, and make the justice system work better for everyone that it serves.

Towards a digital and ethical future

Across the UK, justice agencies and technology providers are collaborating to make this vision a reality. Companies such as Unilink, which develops secure digital systems for justice, are helping to turn AI’s promise into practical tools that support both efficiency and rehabilitation, from intelligent self-service and communications platforms to data-driven operational insight. The challenge now is to scale innovation responsibly, ensuring that, as technology becomes more capable, it remains accountable, explainable, and human-focused.

Digital innovation has always been about solving real-world problems. In the justice sector, this means using technology to promote fairness, reduce harm, and improve lives. AI is simply its latest manifestation. It’s not being introduced as a replacement for human interaction but as a way to make those interactions more meaningful, informed, and effective, and it must be guided by empathy, ethics, and human insight. 

In our view, the justice system of the future will be digital first, but human always, to enable a smarter, fairer justice system where data informs decisions, digital empowers people, and technology serves the public good. 

Read More AI for good

The post AI and digital innovation – Building a smarter, fairer justice system appeared first on Digital Leaders.

As data scientists, our role is to develop models that are accurate, efficient, and fair. However, when the AI systems are deployed into production environments, the importance of explainability increases, especially when a system’s decision-making can impact people’s lives. Helping our clients trust and understand the AI’s decision-making process is critical, particularly in highly regulated industries where ethical considerations are mission-critical. Equally, business leaders, product managers, and policymakers all need to be able to explain and justify the outcomes of AI models to customers, regulators, and the public, ensuring that AI systems are developed and used ethically and responsibly.

A lack of transparency can lead to reduced trust, legal complications, and unintended biases, negatively impacting both individuals and organisations.

In this piece, we explore how we use Explainable AI (XAI) techniques at Informed Solutions and how they can be leveraged to encourage a culture of transparency.

Why is explainable AI (XAI) important?

High Value, Trust Based Innovation

Users need to be able to trust the recommendations and decisions produced by AI systems to use them effectively, and if a user is unable to understand how an AI arrived at its conclusion, they are less likely to rely on it.

This lack of trust can have serious consequences. In healthcare, for instance, doctors may be hesitant to act on an AI’s diagnosis if they don’t understand the reasoning behind it, potentially leading to missed or incorrect treatments. Moreover, when users don’t trust AI systems, they are less likely to adopt them, which limits AI’s broader impact across industries. This can hinder innovation, prevent businesses from realising operational efficiencies, and slow down the transformation of industries that AI has the potential to revolutionise. Without XAI, therefore, the potential value of AI tools could be compromised.

Regulatory Compliance

Regulations such as GDPR mandate that AI-driven decisions be interpretable, especially in high-risk domains. Companies that fail to ensure AI explainability may face legal consequences, financial penalties and reputational damage, so it is essential that data scientists consistently uphold XAI as a standard when working on new systems.

Identifying Bias

The data used to train an AI model may contain biases, and if the decision-making process isn’t transparent, it becomes challenging to detect and correct discriminatory patterns. As such, Explainable AI helps organisations audit their models, identify and adjust biases, ensure fair outcomes and reduce the risk of harm from decision-making.

Improving Performance

AI models can sometimes generate unexpected outputs, and when they lack transparency, diagnosing and fixing these errors becomes difficult. Explainability, therefore, allows developers and data scientists to trace errors, refine model performance, and continuously improve system accuracy.

Five strategies to improve explainability

1. Choose Interpretable Models

Some AI models are inherently more interpretable than others due to their simple rules and relationships. Decision trees, for instance, are models that make decisions by splitting data into different branches based on certain rules. It’s like a flowchart where each node (or “question”) divides the data into smaller, more specific groups based on the values of the features (like age, income, etc). These decisions continue until the tree reaches a “leaf”, where a final decision or prediction is made.

At Informed, we follow a philosophy akin to Occam’s Razor—choosing the simplest, most interpretable model that still meets the required level of accuracy. For instance, when tackling a straightforward binary classification problem, we would favour a classical machine learning technique, such as logistic regression, over a more complex deep neural network, provided it delivers sufficient predictive performance.

2. Implement Explainability Techniques

For complex models, XAI techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) can provide insights into AI predictions by showing the ‘why’ behind the prediction or recommendation.

SHAP values, for instance, help explain how individual features influence a model’s prediction. In simple terms, SHAP assigns a “weight” to each feature, showing you exactly how much each feature (like “age” or “income”) influenced the final decision. This method is based on a concept from game theory, where each player’s contribution is calculated in a fair way.

LIME, on the other hand, works by creating a simple, interpretable model that mimics the behaviour of a more complex model for a specific instance or prediction. For example, if you’re trying to figure out why an AI recommended a specific product to you, LIME can create a simplified version of the AI for that one recommendation and show you which parts of your preferences (such as past purchases) were most influential in the decision.

At Informed, some of the problems we tackle involve complex relationships that necessitate the use of “black box” deep learning models. In these cases, interpretability techniques like SHAP and LIME enable us to look beneath the surface and better understand the factors driving specific predictions, helping us maintain transparency even with more advanced models.

3. Establish Transparent AI Governance

Organisations should establish AI governance frameworks that define clear guidelines for transparency. For instance, they should include guidance on documenting model development processes, as well as maintaining audit trails and ensuring explainability standards are met across AI implementations.

At Informed, our AI Charter commits us to putting ethics, safety, responsibility, and security at the core of everything we do. This means designing AI solutions that are safe, transparent, robust, and fair from the outset. Explainability plays a central role in upholding these values across our organisation. To support this, our “Well-Assured Framework” provides a comprehensive checklist that guides our team through key considerations, ensuring every solution we deliver is trustworthy and aligned with our principles.

4. Provide User-Friendly Explanations

AI explanations should be tailored to their audience. For example, a data scientist may need in-depth mathematical insights, whereas an end-user would require simpler, more intuitive explanations. Creating role-specific transparency, therefore, ensures that all stakeholders can meaningfully interact with AI systems.

Because we work closely with our clients, it’s essential that we communicate our models in a way that’s accessible and easy to understand. By prioritising explainability from the outset of a project, we’re able to offer clear, concise overviews of how models are developed, ensuring our clients remain informed and confident in the solutions we deliver.

5. Conduct Regular Audits

By setting up proactive alerts and tracking key indicators, such as shifts in data distributions, unexpected model behaviour, or signs of bias, we can respond promptly and appropriately. This not only helps maintain the integrity and performance of our models over time but also reinforces our commitment to delivering responsible and trustworthy AI solutions that adapt as real-world conditions change.

Conclusion

As AI usage becomes ubiquitous and continues to shape critical decision-making processes, ensuring explainability and transparency is a necessity. Organisations that prioritise XAI principles will not only comply with regulatory requirements but also build trust, mitigate risks, and enhance AI performance. Therefore, by adopting these practices, we can continue to ensure that AI systems are transparent, reliable, and more widely trusted and adopted.

Read More Bias &Ethics

The post The importance of implementing explainable AI appeared first on Digital Leaders.

Principles first

Unilink has been investigating the use of AI for over two years and we have always been guided by an ethical and transparent framework. How does the AI make decisions? What biases exist and how are they countered? AI cannot be considered for serious areas such as criminal justice without this framework. Even then, we view AI not as a replacement for professional judgment but as a mechanism to enhance it. Every AI-enabled product we develop must be explainable and auditable, and meet the normal business standards of GDPR, ISO 27001, Cyber Essentials Plus and Ministry of Justice digital service guidelines.

We design our systems to make the reasoning behind AI-generated outputs clear and traceable, ensuring human oversight remains active throughout. The operation of AI is logged to maintain accountability and security is built in at every level through multi-factor authentication, role-based access controls and encryption of sensitive data both in transit and at rest. Crucially, human involvement continues to guide all significant decisions, keeping professional judgment at the centre.

Real-world impact

Our partnership with Professor Theo Damoulas at the University of Warwick is central to our ethical AI strategy. This collaboration focuses on developing explainable models trained on large-scale justice system data. These models are designed to uncover behavioural signals that could indicate risk, inconsistency and opportunities for intervention, all while ensuring transparency and accountability in how insights are generated and used.

Our work includes rehabilitation and risk modelling and detecting behavioural trends. For example, if an individual begins disengaging from support programmes, the system could raise a flag for early intervention. Conversely, consistent positive behaviour could be recognised to inform decisions about incentives or progression.

Embedding AI internally

AI is not only transforming our products, it is changing how we operate internally. Our development teams report productivity improvements of over 30% using tools that support coding, documentation and testing. Other departments, including HR and business development, are using AI to process applications and produce content more efficiently.

To encourage adoption, we have introduced an internal framework that recognises and rewards AI proficiency. Staff are supported to build confidence with new tools, and those leading the way are designated as AI Champions to help others upskill.

Supporting, not replacing, people

We are clear that AI is a support layer, not a substitute for people. In justice, decisions carry serious consequences, and the human element is irreplaceable. Our goal is to provide staff with better tools that help them focus on what matters most, which is supporting individuals, ensuring safety and improving outcomes.

Where resistance arises, we treat it as an opportunity to listen. Not everyone is comfortable with rapid change, so we provide clear explanations of how the technology works and where the guardrails are. Over time, this builds trust and increases confidence. Today, all Unilink staff use AI, and we are proud of the adoption achieved. As a company that depends on innovation, AI is vital to our future.

Shared responsibility and progress

Justice has often lagged behind other public services in adopting modern technology, but we now have the opportunity to lead with intention. Ethical AI is not about gaining a competitive advantage but about building systems that reflect the values of fairness and accountability.

If you are working in justice and exploring how AI can improve decision-making or service delivery, we would welcome collaboration. By working together, we can ensure that justice technology remains not only effective but also principled.

Read More Bias &Ethics

The post Designing ethical AI for public good appeared first on Digital Leaders.

While public and media attention often focuses on a narrow set of applications, such as surveillance, enforcement or fictional scenarios like those in the Tom Cruise film Minority Report, concentrating only on these factors alone risks overlooking AI’s broader potential to improve justice services in more constructive, human-centred ways.

At Unilink, we view AI not as a standalone solution but as a multi-functional foundational layer that supports smarter decision-making and helps simplify complex processes for users and practitioners alike.

Intelligent self-service tools

We have already begun integrating AI functionality into our existing platforms. For example, our whole self-service platform is being enhanced with chatbots that help users navigate. These tools can answer questions, suggest appropriate next steps and even personalise support. For prison staff, this means fewer routine enquiries and more time spent on higher-value, interpersonal work.

Real-time translation

AI is also helping to reduce access barriers in custody settings, where linguistic diversity often creates communication challenges between staff and individuals who speak little or no English. With AI-powered translation now integrated into our systems, we can offer real-time translation, both written and spoken, across almost all languages, including Arabic, Urdu and many others. Such translation solutions not only help staff and prisoners understand each other, but also make key services and support more inclusive.

In international contexts, this capability becomes even more important. We are beginning to deploy systems that allow translated instructions, messages and FAQs to be accessed in the user’s preferred language, increasing engagement and reducing miscommunication at scale. Answers to FAQs can be read by the user or the system can speak the words in the required language.

As with all our AI interfaces, we prioritise plain language and accessibility to ensure tools are usable for people with a range of literacy and digital skill levels.

From reaction to prediction

More broadly, AI is helping us shift from reactive models to more predictive, proactive ones. Our AIM (Alert, Intervene, Monitor) platform is designed to identify changes in behaviour patterns that might otherwise go unnoticed. For example, it is able to recognise someone who is regularly attending appointments, avoiding conflict and engaging with rehabilitation programmes and many other factors. These trends can then inform decisions around early interventions, with a focus on supporting progress rather than merely reacting to incidents.

Real-world impact

Each of these examples demonstrates how AI can shift how the justice system operates. Unilink is committed to ensuring transparency of AI recommendations and to addressing the risk of inaccuracies or hallucinations in an ethical way.

Designed to support professionals

Our aim is not to replace professional judgment but to support it in line with MoJ guidelines. Each AI feature we implement is intended to ease operational pressure and surface insights that might otherwise be overlooked. In a sector where decisions directly affect people’s lives, this kind of support is essential.

Crucially, we do not view AI as a finished product but as a flexible capability that must be applied carefully and refined through real-world use. That is why we are collaborating with justice partners and frontline experts to ensure our tools meet both technical standards and day-to-day operational needs.

Read More Bias & Ethics

The post AI as infrastructure for fairer justice appeared first on Digital Leaders.

In a time of rising inflation, growing consumer debt, and increasing financial vulnerability, the UK financial sector is undergoing a quiet but profound transformation.

We worked alongside FinTech Scotland and the University of Glasgow to create a whitepaper called ‘Consumers at the heart of innovation: Financial health evaluation in the UK’ which explores how emerging technologies, regulatory frameworks, and new models of consumer engagement are redefining financial health evaluation.

The post Transforming financial health by bringing consumers to the heart of innovation appeared first on Digital Leaders.

In 2025, that phrase feels woefully irresponsible – particularly as we see digital technologies advance at unprecedented speed, often causing real-world harm for citizens and the planet.

It is often claimed that Artificial Intelligence (AI) will be the biggest disruptor the world has seen since the Industrial Revolution. A multi-faceted technological colossus set to reshape every corner of society and business, yet still largely a mystery to most.

Experimental sketch of the ecosystem – showing complexity.

Investment into the AI industrial complex is staggering. There is a global race for AI superpower status, with countries such as Saudi Arabia, the UK and France squaring up to the US and China, to be a major force. And guess what, people are moving fast and breaking things; massive data centres are being built which consume enormous amounts of energy, algorithms are being trained on biased data, and the threat of an employment crisis is very real – with low-wage workers and women set to be disadvantaged the most.

Globally, governments are actively investing in AI to streamline public services, improve efficiency and to better serve citizens. However, AI readiness and the ability, or inability, of countries to adopt and regulate these technologies will also shape international competitiveness, equity between citizens and countries, ethical standards and future governance.

While its past, current and potential impact is alarming, the future of AI is not predetermined. It takes many human decisions to set the course and decide how things evolve and who sets to benefit; but ethical governance is needed to develop ethical policies and procedures.

In the UK, the Government published its AI Opportunities Action Plan in January, outlining plans to maximise AI’s potential to drive growth and benefit people across the UK – including advocating for its use in the public sector. The plan notes the potential for AI to radically change public services by automating routine tasks, making services quicker and more efficient, and making better use of Government data to target support at those that need it.

Both National and Local Government bodies are being encouraged to adopt AI within their services and processes. Photo: Bridgend District Council.

“But it also brings with it risks that must be managed to effectively support adoption and maintain public trust,” the plan acknowledges, citing fairness, safety and privacy. Indeed, it was a major point of discussion at the Digital Leaders Public Sector Insights AI Week in March, where I gave a presentation on digital innovation in uncertain, complex and emergency environments.

With so much at stake, it has arguably never been more important not to move fast and break things. But we can still move quickly – carefully. With that in mind, this article will reflect on the debate, while drawing on guidance, toolkits and principles to highlight the importance of care-ful innovation and good governance.

Arm yourself with knowledge

The need to arm ourselves with knowledge was a key takeaway from the Digital Leaders event, and one that underpinned my presentation.

The ever-changing narrative around AI and its sociotechnical impacts can lead to feelings of overwhelm; many of us feel daunted by the intensification of automation across all sectors, organisations and individual practices – whether you are working in it or observing from afar.

AI chatbots and automation tools are increasingly being pushed on citizens within digital workspaces and personal communication – Microsoft, Google Gemini, WhatsApp – often without explicit consent. It has become our individual responsibility to improve our knowledge and understanding of these tools and technologies; to think critically, challenge hype and, fundamentally, not accept a tech-determinist roadmap from tech companies at the helm of AI – many of whom, arguably, will be thinking about the financial bottom line and power that comes with it.

AI adoption in government

Thankfully, many responsible leaders are advocating for tools to better support the public procurement of AI. One such person is Professor Alan Brown of Exeter University, who is also AI director at Digital Leaders and held a fellowship at the Alan Turing Institute.

Discussing how to find the balance in digital power dynamics, Brown says we must first acknowledge the political dimensions of AI-based technologies and not pretend they are neutral tools: “When evaluating new AI technologies, my personal approach is to focus not just on defining ROI and efficiency goals, but also describing their governance implications: Who gains authority and who loses it? What values are encoded in this system? What dependencies are we creating? Whose interests are prioritized by default?”

These are all critical questions that need answering truthfully and thoughtfully.

Albert Sanchez-Graells, Professor of Economic Law and Co-Director of the Centre for Global Law and Innovation (University of Bristol Law School), meanwhile, recognises another problem: there is a gap in public sector digital skills preventing the public buyer from adequately understanding the technologies it seeks to buy. Therefore, “the public buyer risks procuring AI it does not understand, which is already a widespread phenomenon in the private sector.”

It doesn’t help that there is so much noise, marketing hype and complexity surrounding it all. On my own quest for clarity, I have come across some useful resources, which I have rounded up below to help others feel less overwhelmed and more informed.

Navigating AI with confidence

Representing a significant stepchange in responsible AI adoption, the UK Government published a playbook for AI in February. The playbook – designed specifically to offer public sector organisations to use AI safely, effectively and securely – includes 10 principles that should be upheld when using AI. These range from understanding the capabilities and limitations of AI, to working with commercial colleagues from the outset, to simply having the skills and expertise needed to implement AI solutions.

Again, Professor Alan Brown has published an in-depth analysis of the playbook, which I would highly recommend reading. According to Brown, while the principles reveal a thoughtful approach to balancing innovation with responsibility, there are additional considerations in the context of digital transformation at large, complex organisations – for instance, integration with legacy systems and cross-department coordination.

Signalling a national commitment to responsible AI use, similar guidance has been produced in Wales and Scotland. The Welsh version of the guidance includes a range of examples of successful uses of AI in the public sector – from helping to diagnose cancer to creating a ‘lost woodland’ dataset – while the Scottish AI Playbook includes a case study about how AI-automated image cropping helped estimate heat loss in homes.

These examples demonstrate the wide-ranging impact and potential of AI, and the importance of ensuring the public sector is equipped with the skills, knowledge and confidence to use it in a way that mitigates risk and maximises positive impact for people and planet.

Cyber Security and specialised toolkits

A major part of the AI education piece is around data and privacy, with cyber security risks intensifying as businesses embed AI into various operations.

According to the National Cyber Security Centre, some of the most dangerous flaws in AI systems are ‘AI hallucination’ (producing incorrect statements), being biased or gullible, creating toxic content and ‘data poisoning’ (susceptibility to being corrupted by manipulating data it is trained on).

These are serious issues that have serious consequences, so it is paramount that tools are developed ethically and security integrated into AI projects and workflows from the outset.

Another significant milestone this year came with the launch of the UK’s Code of Practice for Cyber Security of AI’, which addresses cyber security risks to AI. The Information Commissioner’s Office has also designed an AI toolkit for those seeking to better understand best practice in data protection-compliant AI – to “reduce risk to individuals’ rights and freedoms” caused by their own AI systems.

There are plenty of other specialised toolkits elsewhere, detailing how to harness opportunities in areas such as digital accessibility and civic AI. This toolkit, for example, gives advice on how civil society organisations and local authorities can empower communities to address the climate crisis. These are matters that have a critical real-world impact and which require innovation with care.

Final thoughts

There is no fixed position on AI; it is shifting sands so it is crucial to ask questions and get informed – particularly when employing it in the public sector, where the risk of causing harm is significant.

It is a challenge, but with the right collaboration, partnerships and care, it is possible to deliver digital innovation with demonstrable positive impact. In practice, it requires policymakers to implement robust data privacy and transparency regulations, ensure that labour laws protect fair treatment of workers, and embed diversity and inclusion into government funding frameworks.

It’s a challenge that Calvium is well-prepared to address, as proud members of the Digital Leaders AI Expert platform. As proponents of tech as a force for good, we combine data ethics with ISO and Cyber Essentials Plus accreditation, and clearance for government contracts. This means we help clients adopt AI responsibly, considering impacts at every stage and ensuring innovation is both safe and valuable.

Read More Bias &Ethics

The post Care-ful innovation, the public sector and AI appeared first on Digital Leaders.

I’ve worked with technology for over three decades, writing countless lines of code in various languages for diverse systems. Some projects were slow, painstaking endeavours for mission-critical situations. However, most were developed rapidly to test ideas, gather user feedback, or simply deliver new features. Because of this, I believed I had a strong grasp of the benefits and limitations of rapid application development.

The post The promises and pitfalls of vibe coding appeared first on Digital Leaders.

In April, the Government Digital Service (GDS) added sustainability to the Government Design Principles, this marked public acknowledgment of the large amount of energy, water and materials required to run digital services. GDS advises practitioners to consider both short- and long-term environmental impacts when designing and delivering services.

However, barriers for organisations to measure and reduce the environmental impact of their use of AI remain. In this article, our focus now turns from the “why” to the “how” – how we can do something about these environmental impacts to ensure the sustainability of AI and the implementation of AI for sustainability outcomes.

What’s the definition of Sustainable AI?

Sustainable AI contains two related topics:

1. Sustainability of AI: The practice of reducing the negative environmental impact of the use of any form of AI.
2. AI for sustainability: The use of AI technologies to address environmental, ecological, and social challenges and promote sustainable development.

Measuring, reducing and prioritising impact using the STAR Framework

We’ve looked at the landscape surrounding the development of AI – the large and growing issues around AI’s environmental impact – but organisations are struggling to measure and act to reduce it. Alongside this, AI has the potential to be used for sustainability outcomes, but businesses need help to prioritise and implement them.

To solve these problems, we have developed the STAR Framework (shown above). The Framework guides organisations through a four-step process to identify the scale of risk and opportunity around Sustainable AI, supporting the achievement of business objectives with this at the forefront.

Here, we break down the stages of the process and provide some examples of what can be done in each to create a holistic approach to Sustainability in the context of AI:

Strategy

Firstly, it is crucial to understand what the organisation in question is trying to achieve or what the business objectives are that AI may be able to solve? What, if any, AI strategy is in place on AI currently?

As part of this, we engage stakeholders to align with the overall business strategy and goals to ensure AI use cases are identified and validated against a business need. We would identify what business and environmental factors are important to the organisation in question – is AI’s impact on water usage, carbon, hardware or others the most relevant.

For example, we can help understand the energy requirements, associated emissions and sustainability of the infrastructure used for AI development and deployment, considering whether the system is optimised for energy efficiency. We can also set a strategy to realise sustainability benefits (direct and indirect) considering AI’s potential to address environmental issues. We also get clarity around the operating environment, associated regulation (present and future), government commitments where relevant and provide confidence that our plan aligns to these influencing factors.

Tally

The next phase is all about quantifying and measuring the impact of AI in the particular context of the organisation. We understand the macro numbers from our previous piece but it’s critical to get a view on the individual business in question to be able to produce a targeted action plan in the later steps. What are the impacts on the ground and how can we reduce them? Also, considering the correct tooling and solution to make a tangible impact on the situation, helping the organisation track progress moving forward.

We’ve created a measurement approach aligned to the organisation’s current and future use of AI by leveraging a tailored combination of 30 open-source tools developed by Sopra Steria. There are a number of open-source calculators out there to help with measurement activity, with AI Energy Score, EcoLogits and ML CO2 Impact notable as particularly useful. Implementing Sustainable AI measurement tooling allows us to quantify current risks and opportunities of using AI, setting a solid foundation for future implementations. Implementing Sustainable AI measurement tooling allows us to quantify current risks and opportunities of using AI, setting a solid foundation for future implementations.

Action

Once we have a baseline on our actual usage from Steps 1 and 2, and a view on the measured impact of current and predicted future AI programmes, what are the best practice methods we can employ to put a plan in place to define a tangible reduction in environmental impact of AI?

We tailor action from 31 different best practices that have been developed alongside government, industry and academia. These best practices have been ranked and categorised based on the reduction in environmental impact achieved, the implementation effort and the lifecycle stages they are relevant to. Best practice spans improvements to the design, data collection, training, governance and user awareness.

Review

In the final step of the STAR Framework, we re-baseline and re-measure to analyse the benefits of the work that’s been done in the previous steps. Out of this, a roadmap and options for continual improvement continue the journey. This may be a simulated, scenario-based step depending on the scale/profile of the organisation or if it is too soon to see a tangible difference. We re-measure the impact of the action taken and review against strategy to identify and reprioritise further use cases for development.

What’s next? Let’s explore it together

If the above has resonated with you or sounds like we can help with your challenges around implementing AI sustainably, please do reach out directly to us. We’d love to discuss your thoughts on the topic, different perspectives from your own experience on what we have discussed, or problems we might be able to help you with.

Look out for more content from the Sustainable AI team within Sopra Steria Next over the coming months!

In Summary

• AI can harm or help the planet, depending on how it’s used.
• Organisations sometimes struggle to measure AI’s impact, but awareness and expectations are growing.
• The STAR Framework offers a clear process to make AI more sustainable.
• Using the right tools and actions, companies can reduce AI’s environmental footprint.

Read More Bias &Ethics

The post Sustainable AI: Measuring, reducing and prioritising impact appeared first on Digital Leaders.