AI promises to be a transformative force across many industries, offering immense potential for innovation and growth. However, successfully scaling AI deployments will only be possible if we overcome a major hurdle: Building trust in AI.

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As the UK strives to meet its ambitious decarbonisation targets, the collective efforts of dedicated leaders from diverse fields are crucial. This year’s Net Zero 50 List highlights innovators from SMEs, the public sector, and other key industries who are driving transformative change towards a sustainable future.

Among the List members are pioneers from SMEs developing AI-driven waste recognition software, regenerative farming practices, advisory and certification services for decarbonisation, and digital platforms for carbon footprint management. From the public sector, notable figures included sustainability leads from the Environment Agency and the Crown Commercial Service, alongside other professionals championing sustainable IT, local economic growth, and energy-efficient initiatives in the British Army. You can view the 50 List members here.

The event featured a series of inspiring speeches from prominent figures such as Dr. Mattie Yeta, Chair of the Net Zero 50 List Advisory Panel and Chief Sustainability Officer at CGI, and Sarah Kemmitt, Secretariat Lead at the Net-Zero Banking Alliance, UN. emphasised the critical role of innovative solutions and cross-sector collaboration in achieving the net zero goals.

For me, Lord Deben our host’s address was particularly impactful, highlighting three key takeaways: Firstly, he emphasised the importance of explaining why net zero matters, urging us to communicate that it’s about reducing our planetary impact to nil and transcending party politics for the common good. Secondly, he encouraged celebrating the benefits of a net zero world, such as a healthier, cleaner, and more equitable future with greater opportunities for prosperity. Lastly, he called for discussions on net zero to be in clear, accessible language that resonates with everyone, making the pathway into these discussions barrier-free.

As we reflect on the individual successes celebrated at the House of Lords, it’s clear that reaching net zero is an enormous challenge requiring collective action and a shift in both individual and organisational behaviours. The foundations for a sustainable future are being laid through ambitious actions, robust policies, and coordinated efforts at national, regional, and local levels. The Digital Leaders community, known for its commitment to tech for good and sustainability, continues to lead the charge by fostering conversations and networks around innovative solutions.

The Net Zero 50 List serves not only as a recognition of exemplary work but also as a catalyst for ongoing efforts. By showcasing the UK’s leading initiatives and individuals, we aim to advance the conversation across sectors and build a network dedicated to achieving net zero. The Net Zero 50 List is a testament to the inspiring work of those dedicated to a sustainable future.

To all those who contributed to this remarkable event and to the ongoing efforts towards net zero, thank you. Your work not only inspires but also paves the way for a greener, more sustainable world.

More information is available on the Net Zero 50 List 2024 website. 

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Sabby Gill kicked us off with a summary of the recently-published DL Attitudes of Leaders to AI Survey, highlighting evidence of widespread interaction with AI across government, but also the need for more leadership, data and privacy concerns, and a mixed picture on impact to date, with many citing the need to overcome silos as a key barrier.  Our AI in innovation panel (Number Ten’s Eoin Mulgrew, Informed’s David Lawton, NAO’s Yvonne Gallagher, and DWP’s Shruti Kohli) developed and really pushed the ‘leadership’ and ‘silos’ themes: for me, one of the consistent and stand-out insights of the whole conference was the need to understand whether we’re just doing tactical implementations or real transformation – and it was pretty clear that the consensus in the room was that we’re not really scratching the surface: technical innovation is happening, but very little strategic innovation.

We followed this our first panel with an experiment: ‘There’s an AI for That’ – a sort of rapid speed-dating format where we heard six examples – just three minutes each – of practical AI implementations from the ‘front line’ (thanks to Swindon’s Sara Pena, Norfolk’s Geoff Connell, Natural England’s Alex Kilcoyne, FCDO’s David Gerouille-Farrell, MoJ’s Shelina Hargrove, and CDDO’s Clive Kelman) – great, practical examples of use of AI in streamlining, improving accessibility, co-piloting, integrating, and enabling our public services.  Surprisingly, panellists stuck to the 1-slide/3-minute rule, policed by an implacable Robin Knowles and his trusty alarm clock.

After morning coffee, we held our second panel, ‘AI and ethics in the public sector’, with FDM’s Sarah Wyer, TPXImpact/Nesta’s Imeh Akpan, and AWS’ Himanshu Sahni. The stand-out theme here was the need to acknowledge that AI is merely a mirror to society with all its baked-in inequalities – and therefore of the need to ensure diversity in training AI models. Unfortunately it was felt that more attention is given to ‘user research’ than the sort of ‘social research’ that might address this issue, whilst attention to bias, the need for explainability, and assessment of the usefulness to humans of all AI implementations was seen as a practical framework for addressing some of these issues.

We finished our morning with an excellent ‘fireside chat’ between Malcolm Harbour CBE (Connected Places Catapult) and Rebecca Rees (Trowers and Hamlins) addressing issues relating how the public sector might inject more innovation into the way in which it procures AI.  It was felt that government needs to adopt a more proactive, ‘market-making’ stance to encourage suppliers to meet current needs, and that this might require more creative approaches such as hackathons and much more pre-market engagement.  It was noted that the upcoming Competitive Flexible Procedure should offer a solid framework for such behaviours, hopefully resulting in a less traditionally adversarial relationship between government and its suppliers.

Our afternoon kicked off with a hugely informative keynote from Ollie Ilott, Director of the AI Safety Institute, who provided us with an overview of the Institute’s work, across four themes.  First, we never fully understand the risks, since the capabilities of AI often emerge down the line post-implementation. Second, the institute uses automated benchmarks, red-teams, and automated agents and tools to test for misuse, societal impacts, possible autonomy of AI, and that the present safeguards continue to be sufficient.  Third, the need to upskill people across these areas is an ongoing challenge, and fourth, that the growth of AI is exponential and that it is not easy to focus on the frontier (as we must), since we only evaluate ‘old drops’ of the technology, and thinking in an exponential way isn’t intuitive.

Our third panel session, ‘AI for good and bad’ saw contributions from The Army’s Brigadier Stefan Crossfield, NCSC’s Ollie Whitehouse, Zuhlke’s Dan Klein, and Actionable Futurist’s Andrew Grill.  The stand-out challenge for me came from Andrew, who asked the audience to first raise their hand if they’d tried out Chat GPT (all hands up), and then to raise their hand again if they’d used it again in the past week (all hands down) – the point being that we need to engage more ourselves with the technology to appreciate its positives and its risks. The panel raised a range of fascinating angles on the topic – from Ollie’s question about what trust looks like in a post-truth world to Stefan’s emphasis on leveraging commodity AI (rather than trying to built it within government), and the worrying observation that for our adversaries, ‘the human is not always in the loop’.

Our second and final ‘There’s an AI for That’ panel heard pop-up offerings from Beam’s Seb Barker, Skin Analytics’ Jack Greenhalgh, NHS Resolution’s Niamh McKenna, and Curistica’s Dr Keith Grimes: again, an astonishing range of applications for AI across diagnostics, assessment, accessibility, and documentation.

Our final speaker of the day was Harriet Harman MP, who gave an excellent closing keynote addressing the need to ensure that an AI-powered world is one of equality for all – especially given the ‘techbro’ culture that has prevailed thus far.  She drew attention to Section 149 of the Equality act, in which public authorities are accountable for ensuring that biases are opened up and challenged in datasets – but also that we will need radical change to our processes if we are to implement such much-needed regulation.  Unfortunately, current legislative processes are far too complex and slow to keep pace with AI’s evolution, and Harriet suggested that we may need to grant special statutory powers to the Science, Innovation and Technology and Business and Trade Select Committees in order to fast-track the state’s regulatory response.

All in all, a really enjoyable, packed day of informed views, challenge, and debate.  Thanks to Robin and the Team at Digital Leaders, and our sponsors for the day: Informed Solutions, Connected Places Catapult, Zuhlke and DIgitLab.

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Prof Mark Thompson 17 March 2024

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2024: The World continues its transition towards more resilient, sustainable growth following three years of healthcare threat, which in part have been a catalyst for economic and societal, turbulence.

Our own experience at Informed during this challenging time has seen us in the privileged position of driving and supporting digital change and developing data-led resilience and sustainability services for nationally and internationally-significant public safety, civil defence, and healthcare programmes in support of The Home Office, The National Police Coordination Centre (NPoCC), The NHS Patient Safety Team and most recently the UK Health Security Agency (UKHSA), where we are helping the organisation mature its digital operating model in the aftermath of the Covid pandemic.

Although each of these engagements have different objectives, they state the importance of safe and effective data sharing between agencies for effective collaboration as a common, critical theme. Whether that be capturing and sharing patient incident data across the NHS estate for patient safety improvement, or in capturing data on every fire-related incident into a national reporting system that helps fire and rescue services understand the leading causes of fires in order to reduce fire related casualties.

Our work with the National Police Coordination Centre has seen the advancement of Intelligence-led policing through the development of a cloud-based data integration and digital mutual aid platform that integrates nine regions and 46 police forces, providing superior operational preparedness and response for national scale operations. 

And with the UKHSA, the body responsible for protecting our communities from the impact of infectious diseases, chemical, biological, radiological, and nuclear incidents, and other health threats, our team is helping the agency’s technology arm design, develop, deliver, and support the technology required to prevent, detect, analyse, monitor, and respond to public health threats across the UK. 

Through our work with the teams responsible for critical national infrastructure, we have seen first-hand a very real requirement for continued, smart investments in data sharing capability and capacity that enables and empowers resilient inter-agency network collaboration.

To realise an effective, seamless inter-service collaboration vision, agencies have to provide the integration, collaboration, communication, and information sharing needed to serve both operational staff delivering a service and the needs of citizens and patients these services  must meet. This ability to share and collaborate will enable effective service and safety for citizens wherever and whenever they need it most, and where challenge and need can be most effectively met. This will rely on more of a whole system approach to inter-agency collaboration based on effective data and insights sharing.

While some agencies are trailblazers in the use of data, digital tools and technology, others may lag in their journey of due to the challenges of legacy data estates, leaving data preparedness and readiness the next challenge to address ahead of digital service integration and collaboration.

The quality of data and the ability to share will underpin a more effective (and cost effective) transition to intelligence-led policing, emergency services and healthcare, and each service’s ability to collaborate. The key will lie in the ability for each agency to exchange and interrogate high-quality data, using information transferred within networks in order to make the best, most effective, and most timely decisions about what care, communication, or support is delivered, as well as where and how.

With existing levels of fragmentation and divergence across the NHS and Justice estates, interoperability remains a fundamental building block for new digitally enabled shared services. An effective and interoperable data sharing ecosystem will provide an infrastructure that uses technical standards, policies, and protocols to enable seamless and highly secure capture, discovery, exchange, and utilisation of information, with appropriate controls that ensure proper and effective use. Alongside this, the reshaping of legacy systems with platforms that communicate with, and work better together, can be used to more effectively access and share data.

This ecosystem must also better meet the challenge of sharing unstructured data by employing Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP) techniques to discover and provide relevant information at the point of interrogation and use.  This approach offers significant potential for data sharing, as it allows more meaning and intelligence to be extracted from legacy and unstructured data, and can also support better assessment of data quality and confidence levels in real time, by for example attempting to correlate between different data sets.

In meeting this challenge, data scientists at Informed have worked over the past two years to develop InformedDECISION© – an innovative, first of its kind AI-based decision support platform that enables decision makers to categorise and extract meaning from large, distributed, and unstructured data sources in real time to support and enhance complex decision-making. Importantly, the platform’s ability to unify and extract value from existing unstructured data sources without the need to upgrade systems or take copies of the data could provide a short cut to immediate efficiency and collaboration gains for inter-agency collaboration, whilst significant legacy and data quality challenges are addressed.  The platform is already helping Medical and Environmental decision makers integrate and make sense of large, distributed datasets in real time, employing automated learning to better understand and qualify external datasets as they are used. 

This platform can integrate intelligent workflows that adapt to users’ needs and support complex requests and transactions, with multi-agency case handling delivering seamless and secure case transaction management across multiple organisations through API bridges that efficiently integrate and harmonise data.

As a two-time Queen’s Award for Innovation winner for our ability to accelerate and de-risk digital business change, we understand that the levers that help overcome the challenges of inter-agency collaboration through data sharing across estates lie in; the potential for AI to improve integration of unstructured data; shared and open technical standards that facilitate effective data interoperability; and ultimately investment in quality, decision grade data to support current and future needs.

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Both play critical roles in designing, deploying, and regulating AI systems, and their convergence holds significant implications for the future of policy, especially as we grapple with the risks of advanced AI systems with AI adoption accelerating. With the upcoming AI Safety Bill in the UK, it’s worth exploring how these two areas can support and enhance each other.

AI safety testing: The bedrock of trust

At its core, AI safety is about ensuring systems behave as intended—reliably, predictably, and without harm. Think of it as the crash-test dummy for AI. While safety testing ensures that AI systems are fundamentally sound, AI assurance steps in to make sure we can prove that over time, particularly as regulations tighten and systems evolve.

Take autonomous vehicles, for example. It’s not enough to ensure the car knows how to stop and go; it also needs to handle unpredictable scenarios, from sudden obstacles to extreme weather. That’s safety testing in action—ensuring that AI works robustly across a range of situations. In high-risk sectors like healthcare and finance, safety testing is even more critical. The potential consequences of failure—misdiagnoses, financial fraud—demand that safety isn’t just a check-box exercise but a fundamental pillar of trust in AI systems.

AI Assurance: Proving the case, again and again

Once safety testing is complete, AI assurance steps in. If safety testing is the crash-test dummy, assurance is the process of reviewing the system’s “logbook”—proving the AI not only passed initial tests but continues to perform under evolving conditions. It’s about showing that AI behaves as expected—ethically, consistently, and within the legal boundaries.

As more organisations adopt AI assurance frameworks, we could see assurance become as formalised as financial auditing. AI assurance doesn’t just align with regulation; it is deeply embedded in responsible practices within organisations, ensuring systems remain fit for purpose. This could eventually normalise AI validation as part of business operations, embedding trust and compliance at the core of AI governance.

The Role of the AI Safety Institute and Responsible Technology Unit in the UK

Leading this conversation in the UK is the AI Safety Institute (AISI), which works closely with policymakers, academia, and industry to set robust regulatory frameworks. Their mission? To ensure AI systems are not only safe but beneficial, driving the UK’s leadership in AI governance. But what makes this even more compelling is the complementary work of the Responsible Technology Unit (RTU), which previously operated as the Centre for Data Ethics and Innovation (CDEI).

The RTU has been instrumental in shaping what AI assurance should look like, focusing on transparency, fairness, and risk mitigation. I was lucky enough to be part of the pioneer work as early as 2020. Their AI Assurance Roadmap has provided tools and frameworks for organisations to prove that their AI systems meet ethical, legal, and societal expectations. This work is setting the stage for a future where AI assurance could be as standardised as any other compliance exercise.

By having both the AISI and the RTU under the Department for Science, Innovation and Technology (DSIT), the UK is well-positioned to foster cross-pollination of ideas between safety and assurance. While the two departments have distinct roles, their combined expertise—policy development from the RTU and technical validation from the AISI—paves the way for more joined-up thinking in AI regulation. This holistic approach could place the UK at the forefront of global AI assurance.

Why safety testing could normalise AI Assurance

AI safety testing has the potential to normalise and accelerate AI Assurance. When robust safety testing is established upfront, it becomes the foundation for continuous validation. Rather than treating validation as an afterthought, it becomes embedded in the lifecycle of AI development. This shift could lead to organisations making validation as routine as financial audits.

Imagine a world where AI Assurance is as commonplace as annual reports. By proving that AI systems meet performance and ethical criteria consistently, we can build stronger trust with regulators, businesses, and the public. As a result, innovation will accelerate—because trust is the key to scaling AI responsibly.

As someone who has spent years working on responsible AI, I can’t help but feel a sense of déjà vu. When we first rolled out PwC’s Responsible AI toolkit, the challenges were as real as the excitement. We learned early on that building ethical AI isn’t just about technology; it’s about building trust. And as I always say, trust doesn’t come from promises—it comes from proof. AI safety and assurance are two sides of the same coin on this journey.

In conclusion

As AI reshapes industries and societies, the intersection of AI safety and assurance will be pivotal in ensuring that these systems not only function but do so ethically, safely, and within regulatory frameworks. The work of the AI Safety Institute and Responsible Technology Unit in the UK will be crucial in guiding this process. Their collaborative efforts will not just react to AI risks but anticipate them, setting a global precedent for responsible AI governance.

Ultimately, AI safety testing is about more than preventing harm; it’s about creating a foundation for AI validation to become a formal, standard practice. By embedding these processes into AI development from the outset, we can ensure that trust in AI isn’t just a goal—it’s something we can demonstrate, time and again.

Because in AI, as in everything, trust is everything—and trust comes from showing your work.

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So far so good.

But this structure makes it hard to offer a single point of access. During the pandemic, CAS established the Scottish Citizens Advice Helpline: a central number for advice. To get local advice, clients would have to wait for a callback from their local bureau.

There were a few problems: it could take 24 hours to get a callback; volunteering to staff the service was not popular; advisors wanted to be giving advice, not routing calls; and the system cost nearly £500k per year to run.

Working with Civtech, my team successfully deployed an AI-driven system (thanks to the support from CivTech), which tackled these challenges that: reduced wait times for connecting to a local bureau to under a minute; eliminated the need for staffing, allowing advisors to be redeployed from the contact centre to the frontline; and reduced annual costs below £30k.

CAS said that the project was a significant game changer in bridging the gap between national coverage and local delivery. By smart use of AI, we effectively decentralised a call centre model. The project won a 2023 ScotlandIS Tech for Good Award and was runner-up for DigiLeaders AI Innovation of the Year.

Here’s 3 things we learnt from this project:

Service Design is key to AI
Advances in AI are bringing human-like writing and understanding within reach. Similar to previous tech advances (whether the internet, the computer or even electricity before that), the key issue becomes how to use the technology well. This is a service design question: the challenge is deeply understanding user needs, exploring innovative solutions, and continuously refining the user experience.

Cost-Effectiveness Doesn’t Compromise Quality
One significant takeaway is that AI can not only make operations more cost-effective but also improve the quality of service. The transition from a £500,000 per year system to one costing only tens of thousands without compromising — in fact, improving — response time is a wonderful example of this.

Buy Over Build
Instead of creating a solution from the ground up, leveraging existing, well-funded technologies (like PolyAI, in this project) can speed up implementation and reduce risks. This ‘buy over build’ strategy is a crucial lesson if you are seeking to innovate swiftly.

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When it comes to the public sector, its benefits are significant. GenAI can streamline access to knowledge, making it easier for staff to gather information, understand the correct procedures, and make informed decisions. 

However, as these technologies become more embedded in the public sector, securing them against cyber threats will be an increasingly difficult challenge to meet. As such, cybersecurity professionals will need to update their strategies and skills to protect these digital tools.

The barriers caused by conventional thinking

Traditional methods of cybersecurity usually rely on fixed concepts such as firewalls, patching, and monitoring. While all these techniques have their benefits and are vital, they also have their limitations when it comes to GenAI. These systems are dynamic and adaptive, which makes them hard to secure using conventional techniques.

One example of this is social engineering. Just like human beings, GenAI models can be manipulated to share sensitive information though methods such as prompt attacks. Traditional cybersecurity measures are usually static, which makes them ill-suited to address dynamic threats like these. If we are to address this, we need to explore new options.

Harnessing language as a firewall

Language can serve as a powerful defensive layer when it comes to GenAI. This is especially important given these technologies’ unique vulnerabilities to prompt attacks and various forms of linguistic manipulation.

Carefully developing metaprompts or system prompts can provide a strong first line of defence against these attacks. These are instructions that guide the AI’s behaviour. By creating precise prompts, we can limit the scope of the AI’s responses, which reduces the risk of sharing sensitive information or making damaging statements. For example, well designed metaprompts should respond, in a polite but firm way, to any questions that aim to extract confidential data or provoke inappropriate responses.

Another vital element involves integrating a distinct AI for natural language processing, which evaluates both the input prompts and the resulting outputs to identify contentious or offensive material. This is not solely about screening incoming data, but also thoroughly examining the generated content. For instance, in the scenario where GenAI is responsible for addressing public inquiries, the discrete, specialised AI should intercept any responses it generates that might be seen as contentious or harmful, enabling human intervention in the conversation.

By treating language as a firewall, organisations can introduce an additional security layer tailored to address the specific challenges presented by these emerging technologies. This strategy offers comprehensive oversight and filtration of both inputs and outputs, providing enhanced protection against both traditional and innovative cyber threats.

Comprehensive strategies for governance

Ensuring the security of GenAI systems requires a multifaceted strategy which covers technical, ethical and legal considerations. The implementation of a comprehensive governance plan can assist in formulating principles, guidelines, and standards to promote the secure and responsible utilisation of these technologies.

Collaboration is of utmost importance here. Cybersecurity experts, technologists, and AI ethicists need to join forces to create robust governance frameworks that specifically tackle the distinct challenges presented by these solutions..

Staff training and education

Effective training is essential for navigating the unique challenges brought about by GenAI. It’s imperative to provide updated cybersecurity training to all employees, not limiting it to just technical teams, in order to enhance their awareness of emerging risks. Cultivating critical thinking skills is equally vital, particularly in the context of scrutinising and verifying the generated outputs.

Staff should also be provided with training in requesting source references and comprehending the reasoning process behind AI-generated content. Furthermore, emphasising the significance of data quality and reliance on trusted sources is vital, as these factors hold considerable sway over the quality of outputs and contribute to reducing potential vulnerabilities. As well as this, GenAI presents opportunities for inventive cybersecurity approaches, rendering staff education a two-way exchange between learning and innovation.

Auditing and AI ethics

Conducting audits and ethics reviews serves as key instruments in guaranteeing that these systems function within acceptable limits, particularly as they progress and change over time. Routine evaluations can play a crucial role in pinpointing vulnerabilities and ethical issues unique to these systems. Armed with these findings, supplementary controls and safeguards can be put in place to help alleviate potential risks.

Preparing for the age of GenAI

As GenAI systems become progressively vital in the public sector, creating greater efficiency, productivity, and process compliance, the intricacy of their security likewise escalates. While traditional cybersecurity measures are fundamental, they fall short in addressing the distinctive challenges presented by these dynamic technologies.

The notion of “language as a firewall” is a transformative change in the realm of cybersecurity thinking. It underscores the significance of meticulously designed metaprompts and system prompts as the initial line of defence. Moreover, the inclusion of a discrete AI system tasked with scrutinising both input and output layers adds a comprehensive layer of security, fortifying protection against both traditional and emerging cyber threats.

Frequent audits and ethics reviews continue to be essential in the process of pinpointing vulnerabilities and ethical issues. A comprehensive governance approach, encompassing technical, ethical, and legal aspects, guarantees that all relevant considerations are addressed. Collaborative efforts among cybersecurity experts, technologists, and AI ethicists are pivotal in the creation of sturdy governance frameworks.

Within this ever-changing landscape, cybersecurity experts must perpetually refine their skills and approaches. An active strategy that encompasses linguistic precision, real-time review, routine audits, and comprehensive governance is pivotal for the secure and responsible deployment of GenAI systems. The field must retain its flexibility, continuously learning and adapting to keep pace with emerging challenges in this swiftly advancing technological future.

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The digital divide

• In the UK, there are 2.6 million adults who are completely offline and 20.5 million adults with low or very low digital engagement (Lloyds UK Consumer Digital Index). This indicates that a significant portion of the population must gain digital skills and feel digitally included.

• One-fifth of the UK population lacks Essential Digital Skills for work to access the internet independently (Lloyds Essential Digital Skills Report). This further highlights the digital exclusion experienced by a substantial number of individuals.

• Research shows that people from low-income households are four times more likely to be limited users and more likely to become more excluded over time. This demonstrates how existing vulnerabilities and socioeconomic factors contribute to digital exclusion.

• Two-thirds of people struggling with digital skills say they would be encouraged to improve if they knew support was available, and 64% would undertake training if it would aid career progression (We Are Group). This suggests that access to support and training can help bridge the digital skills gap and reduce feelings of digital exclusion.

• Many services are online-only or offer deals exclusively online, which can lead to higher costs for individuals without digital access. Additionally, digitally excluded customers are more likely to default on payments, indicating the negative financial impact of digital exclusion.

• Assisted digital services can provide access to products and services for customers with limited technological proficiency or impairments, ensuring inclusivity for all customers. This highlights the importance of providing support and assistance to address digital exclusion.

• Customer feedback shows that over 94% of customers surveyed reported an increase in their overall digital confidence and skills after receiving training (We Are Group). This demonstrates the positive outcomes that can be achieved by addressing digital exclusion and providing support.

The role of assisted digital services

While many commercial and public organisations have made strides in improving digital inclusion, Assisted Digital services still need to be more emphasised. These services are designed to support individuals or groups who lack essential digital skills, offering them guidance and assistance in accessing and using digital technologies, including apps, application forms, and other digital communication platforms.

Practical examples of Assisted Digital services can be seen in daily life, such as accessing grants and applying for housing or a passport. Customers who cannot access digital services often face long phone queues and complex, time-consuming processes. However, with Assisted Digital, customers can receive immediate support to engage online, making the process more efficient and accessible. This not only simplifies their lives but also fosters buy-in from customers by demonstrating the tangible benefits of Assisted Digital. How many of us who are digitally savvy report online if our bins have been missed, taking only a couple of minutes of our time? Or need to check when our salary or pension has been paid, check our banking app. Assisted Digital can help get digitally excluded customers to this level.

The power of digital inclusion

Digital inclusion becomes most relevant when individuals see how it improves their quality of life. Consider the convenience of digitally reporting a missed bin collection or checking your banking app for the latest salary or pension payment. Assisted Digital can empower digitally excluded customers to access these services quickly and efficiently, bridging the gap between them and the digitally literate.

Moreover, Assisted Digital does not merely provide a one-time solution; it offers flexibility to customers, adapting to their changing circumstances. This ensures that individuals are not left out or excluded due to a lack of digital skills, supporting them in various aspects of their lives.

Empowering organisations and customers

Assisted Digital services play a vital role in improving customers’ digital journey and experience. By offering necessary assistance and support, these services ensure that all customers, regardless of their digital skill level, can effectively engage with digital services. This empowerment benefits customers and organisations aiming to enhance their customer experiences and outreach.

Digital inclusion is not just a buzzword; it’s a crucial aspect of our increasingly interconnected world. Organisations that recognise the importance of Assisted Digital services can significantly bridge the digital divide and ensure that no one is left behind. If you would like to learn more about how We Are Group can help your organisation implement Assisted Digital services, please do not hesitate to contact us. Together, we can build a more digitally inclusive future for all.

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One theme that particularly stood out was the importance that everyone placed on developing and using AI responsibly and ethically. Society is becoming increasingly aware and questioning of AI and so, rightly, trust and confidence will need to be earned by demonstrating that AI is being developed and used with people’s best interests in mind.

Here at Informed, AI is playing an increasingly significant role in the digital transformation programmes that we deliver for our clients, and in the solutions we provide to our international customer and partner community. We want the solutions we deliver to have a positive impact, and so it’s hugely important to us that we develop and use AI responsibly and ethically. Taking part in the conference made me reflect on how we approach AI assurance, and I wanted to share some of the principles and practices that we have found make a noticeable difference.

How AI assurance and other information assurance function can work shoulder-to-shoulder

Over the last few years, information assurance has become a more integral part of every organisation. All organisations in the UK have an obligation to protect data in line with GDPR but, for most organisations, data protection is just one information assurance function that sits alongside others such as information security and cyber security. AI is data driven, and so AI assurance has a tight relationship with these other information assurance functions.

Whilst AI assurance, data protection, information security, and cyber security are complementary and inter-related, the level of collaboration between specialists in each of these assurance functions is often limited. For example, it isn’t often that we see data scientists, data protection specialists and information security specialists sitting down together to co-review a Data Protection Impact Assessment for a new AI based service, or to brainstorm the organisational and technical measures that will help to make an AI solution safe, secure, transparent, and fair by design.  This sort of siloed working isn’t uncommon, but it is a missed opportunity for collaboration that risks creating poorer outcomes for AI assurance.

AI assurance, data protection, information security, and cyber security may be different and very specialised disciplines, but they all share a common outcome – to create trust and confidence by assuring that information is being managed responsibly, ethically and legally. Given that shared outcome, organisations should reflect on their operating model for information assurance and, if they need to, make changes that require close collaboration between the different functions. Close collaboration leads to a more complete and cohesive understanding of risks and opportunities that is greater than the sum of its parts. A more complete and cohesive understanding of risks and opportunities leads to more effective actions. More effective actions will lead to more assured AI and greater levels of trust and confidence.

Improving collaboration between assurance functions might sound easier said than done, but we have seen it done simply and well. The best examples are where assurance functions have adopted ways of working that you would typically find in an agile product team. The Plan-Do-Check-Act lifecycle that is a staple of many ISO standards maps closely to the Scrum sprint planning, delivery and review/retrospective framework, and we have seen assurance functions use Scrum very successfully as a methodology for running multi-disciplined teams who work collaboratively to shape and agree shared assurance goals and deliver a Backlog of work that achieves these.

Embed AI assurance techniques into delivery methods

Security by design, privacy by design and data protection by design and default are concepts that we’re all familiar with and subscribe to. These concepts say that security, privacy and data protection considerations should be ‘baked in’ to everyday working practices so that they are assured as a matter of course throughout the delivery lifecycle, rather than every so often. Applying the same principle to AI assurance will help to ensure that AI is safe and ethical by design and has people’s best interests in mind.

The majority of digital transformation programmes involve the delivery of new products, services and capabilities using agile methodologies based on frameworks like Scrum, Nexus and SAFe. These methodologies involve muti-disciplined teams of User Researchers, Service Designers, Architects, Data Scientists and Developers delivering products and services in a user-centred and iterative way. Teams frequently inspect and adapt what they are delivering to assure that user needs are being met, quality is high, and risks are being mitigated. This ‘baked in’ focus on user needs, quality and risk means that agile delivery methodologies can be adapted to embed AI assurance techniques with relatively little effort.

Here is one simple example of how we have embedded AI assurance techniques into a two-week Discovery Sprint where the goal is to understand user needs for a new digital service that incorporates AI:

• During Sprint Planning the whole team brainstorms and agrees the research objectives that they want to achieve during the coming Sprint. This includes identifying the users that we want to conduct research with, and the research techniques we plan to use (Focus Groups, interviews and surveys etc). The research objectives are formulated as hypotheses using a Hypothesis-Driven Development user story structure and are informed by what we’ve learned during the previous Sprint.
• Early on during Sprint delivery we run a Consequence Scanning ceremony based on the excellent Kit available at doteveryone.org.uk. This is a whole-team ceremony that brings together team members from user research, service design, data science, and technical architecture to consider the consequences of the AI based service from different perspectives. We also involve assurance specialists from our clients AI assurance, data protection, information security, and cyber security assurance functions so that delivery teams and assurance functions are collaborating shoulder-to-shoulder. During the ceremony, we take the hypotheses that were formed during Sprint Planning and consider what the intended and unintended consequences of these might be. We often use the ‘Potential Harms from Automated Decision-Making’ framework developed by the Future of Privacy Forum as a prompt for making sure we think broadly about the different categories of consequences that could lead to individual or collective benefits or harms. Once we have a sense of what the consequences could be, we use these to refine our hypotheses and inform the discussion guides or surveys that steer our research.
• We run our research and elicit user feedback on the hypotheses and consequences. We synthesise the feedback to draw out findings and insights that we use to refine our understanding of our user personas and needs. As well as capturing user needs in our personas, we also capture the users’ views on the consequences we’ve identified and articulate these as potential risks, harms, and opportunities. This helps to keep these topics at the forefront of the team’s mind.
• During Sprint Review, the whole team inspects the findings from our user research and reflects on what we’ve learned and whether our hypotheses turned out to be true or not. We take what we’ve learned and use it to inform and adapt the research objectives for our next Sprint. The cycle then starts again.

These are all simple things but making them an embedded part of your delivery method has significant benefits. The overall approach allows organisations to balance agility and innovation with control, which is in-keeping with the spirit the pro-innovation approach to AI regulation and assurance set out in the recent UK Government white paper. The frequency of inspection and adaptation reduces the likelihood of more insidious risks, such as bias in data and models, creeping in unnoticed. There are regular forums for involving assurance specialists in delivery and for different assurance functions to work shoulder-to-shoulder. It is more straightforward to quickly reconcile different viewpoints that team members might have, such as how to balance user needs identified through research with compliance obligations identified by assurance specialists. It is more straightforward to adapt AI assurance techniques (such as those set out in the CDEI portfolio of AI assurance techniques) as new needs, standards and guidance emerge.

AI assurance is closely inter-twined with other information assurance functions and should be approached with a ‘by design’ mindset. AI, data protection, information security, and cyber security assurance functions should collaborate closely, and AI assurance techniques should be baked in your delivery approach. Agile delivery frameworks like Scrum can be readily adapted to allow this and, by doing so, AI assurance becomes an everyday team sport. Ultimately, that can only lead to higher levels of trust and confidence that AI is being developed and used with people’s best interests in mind.

Originally posted here

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We can’t snooze on the job. Around 2015 I heard DeepMind founder Demis Hassabis interviewed about AlphaGo. When asked about the dangers of unethical development, he suggested that it wasn’t an immediate problem as very few people could build an AI. In 2017 I was already writing about the risks of compromise in AI ethics. Ethics go hand in hand with development, and it’s incredible how fast the world has changed.

The fabulous Timnit Gebru, former co-lead of Google’s AI ethics team, spoke at Inventures Canada in June. She asks five simple questions.

1. Do you build it?

This needs to be asked whenever a new technology rears its tempting head. It’s all the more important when dealing with AI thanks to a noticeable over-estimation of its current capabilities. What are you planning to do? Are you likely to crash into issues around data use consent, cultural sensitivities, or personal privacy? And could a different or simpler solution do the job better?

2. How do you build it?

AI depends entirely on carefully tagged data, and lots of it. Providing this involves, according to Gebru, “millions of people scraping data, building neural networks, and labeling data.” There are a multitude of different skills under the hood, too. Before we bundled everything under the AI umbrella, we talked about NLP (Natural Language Processing), imaging, algorithms. Shortcuts are not an option.

3. How do you test it?

If you test your new AI on a data set adjacent to your training data, it’s going to perform beautifully. You’ve already given it all the answers. It’s time to play hard-ball. Disaggregating data enables intersectionality in processing. This delivers powerful insights, and can reveal  hidden problems within the algorithm and underlying data such as unintended bias. The term ‘intersectionality’ comes from research into a 1976 case against General Motors concerning discrimination against black women when hiring. “Black jobs were available to Black men, and female jobs were available to white women. However, Black women were not employed in a similar manner.” The intersectionality of race and gender reveals discrimination, where the original case simply concluded that there was sufficient diversity of hiring.

4. How do you deploy it?

We have an inbuilt trust of the machine. Computer says yes! Great, but why? We use critical thinking when talking to humans, but we have a tendency to believe the machine. There are stories of drivers blindly following the SatNav down pedestrian streets, across dangerous bridges, and even to the wrong country in Europe, arriving in Rome, Germany, instead of Rome, Italy. In June 2023,  a legal firm was sanctioned for submitting a ChatGPT-generated legal brief that included six fictitious case citations. Transparency of algorithms and decision making, and education on the limitations of AI, should be front and centre of deployment.

5. What are the unintended harms?

Who is being damaged by the incautious deployment of AI? The scraping and tagging of data, and the moderation of data sources to remove the extremes of abuse and toxic opinion, are human tasks. They exact a mental toll on low paid moderators who are exposed to the worst of the internet. And what about the data? We have a wealth of data in the world, the volume doubling every two years, and it reflects all of our changing attitudes over time. Not only can the decision making data reinforce old stereotypes, but as Caroline Criado Perez highlights in her book Invisible Women, unless data is  disaggregated, it discriminates.

We are entering a new age of hype over AI. The pace of change is accelerating, and the capabilities of AI are only going to expand. It’s up to us to apply strong ethics to development, avoid shortcuts, and harness this tool for the good of all. What do you think is the most important of these guidelines for today’s developers?

Originally published by Kate Baucherel www.galiadigital.com. Kate is a speaker, author and consultant specialising in Web3 technologies including blockchain, cryptocurrency and AI.

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