Computer says yes: can AI help to streamline a council’s complaints process? Imperial College London students explore the challenge.

DG Cities has collaborated with Imperial College London for several years, from forming industry-academic partnerships for research projects to sharing real-world case studies for student learning. For the latest of these, we have been working with students on a key issue facing councils - the complaints process - and looking at how AI might help to streamline responses. For the next in his series on AI in local government, Graduate Consultant (and Imperial MEng alumnus), Nima Karshenas explains…

AI-generated image of customer service terminal

Capacity and deficit in a skilled workforce is often quoted as one of the main reasons for the shortfall in the ability of some councils to drive and implement innovation. DG Cities has been helping to address this by forging a unique collaboration with the EEE (Electrical and Electronic Engineering) course at Imperial College London, drawing on some of the very brightest minds in the country. This has been a resounding success over the years, and this year we tasked our students with reimagining the complaints processing system within councils, harnessing AI tools to ensure the robust, informative, and consistent collection and presentation of complaints data. 

We believe that every organisation providing social housing in the UK can benefit from this use case. The use of new technologies will improve the quality of service delivery, tenant satisfaction and reduce costs. Based on our analysis, this use case can be delivered with the current readiness of AI techniques.

Following two months of hard work, the students have provided us with an impressive proof-of-concept, leaving us with clear next steps to think about how to turn these systems into a reality. The potential is clear, and these are the beginnings of a long road towards making our public services smarter and more efficient; providing more value to the taxpayer, and most importantly freeing up the time and resources to allow a more proactive approach to governance. 

What were the outputs?

The students made use of a large language model (LLM) to process and categorise complaints, providing summaries, attributing them to their relevant department, and assigning them an urgency level to facilitate quicker resolutions. 

Dynamic Dashboard: The dashboard offers real-time analytics, enabling council members to identify trends and address issues proactively. The tile system allows for customisable insights based on the council’s priorities.

Interactive Map Interface: Complaints are displayed on an interactive map with markers that provide summary popups. This feature allows for easy visualisation of complaint locations and the status of each. 

Automated Data Handling: Complaints submitted via online forms are automatically processed and stored in a secure, online database. The integration of AI ensures that each complaint is categorised and summarised, reducing manual workload. 

Importantly, the data collected by the system can feed into more robust and detailed data analysis systems that can pool in other sources of data (IoT environment sensors, energy monitors, cameras etc.) enabling the council to develop evidence-informed response strategies to complaints, ensuring a prioritisation that matches internal policy and fairness goals. 

Complaints portal - dashboard view (example data synthesised)

Complaints portal - map view (data and locations synthesised)

How does this system help?

Improved efficiency: The new system has the potential to significantly reduce the time required to handle complaints. By automating data entry and providing actionable insights through AI, the council can address issues more promptly. This shift from manual to automated processes helps eliminate backlogs and ensures that resident concerns are addressed in a timely manner.

Enhanced decision-making: The AI-powered insights and real-time analytics provided by the dashboard enable council members to make more informed decisions. Identifying patterns and trends early allows for proactive measures, potentially preventing issues from escalating and improving overall community satisfaction. The map-level UI enables the council to build a location-aware understanding of the issues faced by residents, allowing them to take appropriate engagement measures and problem resolution strategies. This ultimately means for more effective public services.

Greater resident satisfaction: With the ability to address complaints more efficiently, resident satisfaction is expected to improve. The system not only speeds up response times but also ensures that residents are kept informed through automated updates when their complaints are being addressed. This crucially brings the council closer to the community and ensures everyone can feel heard. Such a system has the potential to be extended to resident engagement in different contexts, such as digital inclusion.

Lower costs: Assuming an average of 50 daily complaints, the students have estimated the cost of using this model amounts to just around £4 per year. It’s important to note that even if this number is higher, model costs scale linearly.

Building organisational knowledge: Perhaps most crucially, developing  a system like this is hugely impactful to organisational knowledge and memory. Building out the data pipelines, codebases and organisational processes to maintain such a system will be crucial to massively accelerating the timelines of future AI projects within the council. Fundamentally, this is a resident engagement project, it has constructed an automatic means of collecting and sorting communications from residents. As such, it can be very easily adapted to other applications grounded in resident communications and engagement. Furthermore, building out these digital systems offers automated and robust collection of clean data (complete, correct and error-free), which will be crucial moving into the future. 

Next Steps

Address reliability of AI outputs: One of the primary barriers to making the system production-ready is ensuring the reliability of the AI-generated summaries and urgency levels. As these outputs directly impact how complaints are prioritised and addressed, they must be accurate and consistent. More extensive testing is required to validate the AI’s performance under real-world conditions. If performance is deemed insufficient, we must look towards a more sophisticated model, leading me onto my next point…

Fine-tune with council data:  The current model leverages general-purpose LLMs with Few-Shot Learning and Prompt Engineering to categorise, summarise and label (urgency level) complaints. This means that there is an inherent reliance on the general purpose data that is not visible to the council. Due to a lack of a clean available dataset, students had to resort to AI generated complaints to test their system, this needs to be addressed for obvious reasons. The council should look to build their own database of complaints categorisation, labelling and summarisation, such that the LLMs can be fine-tuned to match desired outcomes, and ultimately lead to more reliable and explainable behaviour from the AI model.

Scale and test: The system needs to be tested with the actual volume of complaints that the council receives to ensure it can handle the load effectively. This step is crucial for identifying any potential bottlenecks or performance issues. Can the system be effectively scaled to meet every council’s needs and prevent individual developmet?

Enhance features & integrate with other data sources: The students have identified some additional features that can be developed in the future to enhance the product, such as an expanded dashboard with more customisable tiles, a task-tracking login system for better complaint management, and automated updates for residents. Furthermore, there is an opportunity to integrate other databases such as ward boundaries, relevant stakeholders to location and type of issue. This will allow for even more sophisticated features, for example, if there are numerous complaints from a particular area and issue type relevant to a council member then they can be automatically and be able to respond immediately. This can be especially powerful in the case of disaster.

Implement data security: Implement advanced data encryption methods to enhance the security of resident data, ensuring compliance with data protection regulations.

Look at integration with council workflows: Ensure that the system integrates seamlessly with the existing workflows of the council. This involves training council staff to use the new system effectively and making any necessary adjustments based on their feedback.

Increase cost effectiveness: Work with the council to assess the affordability of implementing the system on a larger, and production scale. This includes exploring funding opportunities, potential collaborations with the private sector.

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The students said…

Mathew Stevenson:

I thoroughly enjoyed working on this project for DG Cities - it was a new experience for me, thinking about how technology could help local government. My role in the project was building the user interface, and this gave me the opportunity to explore how best to present the information to council, which added an additional challenge beyond the technical challenge of building a web app; trying to build a clear interface that would be useful to all council members, regardless of previous experience with technology, was a challenge to balance with providing plenty of information – but a challenge I found very interesting, and I am happy with our solution!

I hope the web app we developed can help as a proof of concept for the council, broadly showing that technology can help make managing complaints easier and more automated! Beyond this, I particularly hope our solution highlights three key things:

(1) Getting automatic insights into the data (quantitatively via the dashboard and visually via the map) could be very helpful in identifying both problem areas, as well as areas where solutions are succeeding; action can be taken and lessons learned from this, much quicker than trying to spot these patterns manually from a spreadsheet.

(2) AI doesn’t have to be made the ‘front and centre’ feature of a tool when it doesn't need to be – you can leverage some great utility from it, as I hope we have, but it needn’t be shoehorned in everywhere.

(3) These solutions can be simple and flexible; our dashboard tile system makes it very easy for a client council to request specific insights, and these would be very easy to add in! This customisability means the council could get even deeper and more specific insights as they desire them, which is useful again for identifying problems earlier, which in turn means dealing with them quicker and happier residents.

The primary barrier to making our system production ready is the reliability of the AI summary and urgency; because this could have a real impact on people's lives and the responsiveness to their concerns, it needs to be reliable, consistent and accurate. It needs further in depth testing, which we didn’t have the time to do – however the performance from our small-scale subjective tests is promising. In the meantime, to help with this problem of reliability, we made sure to make the full original complaint easily accessible from the summary popup, so council members could still cross-reference with the original complaint.

Junyu Meng:

I thoroughly enjoyed working with DG Cities on this project and seeing our vision come to life. I mainly worked on the database in the backend, making sure all the relevant information is stored correctly to allow for our desired functionalities. Clearly displayed complaints, actionable insights and resident satisfaction were our top priority and I am pleased to see that evident in our end-to-end solution. Councils will no longer have to suffer from large backlogs caused by the current manual handling process, as efficiency would be massively improved. Resident issues can thus be addressed more timely, amending resident satisfaction.

Our proposed solution still requires a few more steps before it would be ready for production, including testing and ensuring the system functions well under the actual amount of complaint data that the council receives. However, we believe that both councils and residents would greatly benefit from a smarter complaint management system, should councils deem it affordable enough to implement it into their workflow.

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A huge thank you to the team at Imperial - it was an impressive piece of work, with great potential benefits. Thanks to Bhavya Sharma, Matthew Stevenson, Junyu Meng, Ben Marconi, Alex Dhayaa and Sasha Afanasyeva.

We at DG Cities are working with councils on the many potential useful and ethical applications of AI and we’re committed to carrying this momentum forward into integration planning and initial testing and trials, so feel free to reach out if you are keen to collaborate or discuss further.

AI: how can councils put it to work?

Beyond the hype, how can AI really help councils day-to-day? For example, how can users write more effective prompts, or incorporate new tools in existing processes? For our latest blog, and the first in a series, our Graduate Consultant and AI expert, Nima Karshenas gives a practical guide to some of the ways local government can use these new systems to improve efficiency, streamline tasks and turn them into helpful tools.

AI-generated image showing a woman at a desktop computer wearing headphones

With the unprecedented growth of AI (artificial intelligence) and LLM (large language model) industries, unlocked by advances in hardware, we are increasingly seeing these tools incorporated into different aspects of our lives. The buzz around the potential of AI and the amount of information out there can be overwhelming – particularly when new services suggest it can be essentially ‘thrown’ at tasks to instantly transform and improve existing processes. However, as with all previous transformative technologies, it takes time, due diligence and a fundamental understanding of the technology to see beyond the haze of extreme narratives.

Here at DG Cities, we will be taking a closer look in the coming months at how AI can be used in specific contexts to transform processes in local authorities, ensuring local government can deliver its services more robustly and efficiently in the face of immense resource pressures. But we want it to be as useful as possible. So before we dive into future plans, this blog will explore how LLMs such as ChatGPT and Claude 3.5 can be immediately incorporated into individuals’ workflows, with a particular focus on tasks undertaken at the council.

How do LLMs work and where should we trust them?

At their core, and especially in earlier iterations like GPT 3.5, which many people still use after exceeding their GPT-4o daily limit, LLMs are master imitators. They regurgitate text in the same manner as the data on which they have been trained. These datasets comprise a vast portion of the internet, which LLMs first learn to imitate and are then fine-tuned to answer both general and task-specific questions. 

Despite their immense power, LLMs are not reasoners. They do not follow a strict model or process to ensure the accuracy of information. This means that they can suffer from what is referred to in the industry as ‘hallucinations’, where quotes and figures are made up, and have no basis in any kind of official source. Furthermore, being frozen to the data on which they are trained means they often do not use the most up-to-date information. 

Although LLMs are beginning to incorporate more sophisticated ‘helper’ models that utilise external tools, such as documents, web browsing, and calculators to support LLM responses, reduce hallucinatory behaviour, and provide the latest information, accuracy is still not guaranteed. Therefore, human oversight is essential, and LLMs should be seen as the starting point, not the final step, of any task they are used for.

Where are LLMs most useful for councils?

Considering all this, which specific tasks do we see current LLMs having the most positive impact on everyday work?

• Research: LLM web search tools such as Perplexity.ai serve as an excellent starting point for research. They scan the internet and provide sources for statements, but still sometimes suffer from hallucinations. To truly understand a topic, use the information as a starting point and delve deeper by conducting your own searches or asking Perplexity more specific questions – and always remember to verify information that you have drawn.

• Translation: DeepL is a state-of-the-art translation tool that can be particularly useful for understanding research in other languages, corresponding with foreign stakeholders, or reducing the effort required by council employees for whom English is a second language.

• Text summarisation: This feature helps you understand long documents by extracting key information. However, hallucinations still apply. If you are not specific about the information you want to extract, key segments might be missed, and the likelihood of hallucinations increases. Use it to get a high-level understanding of a document, not a final comprehension. If the understanding is critical, further checks will still be necessary.

• Create slide decks from reports: LLMs can help set up a skeleton of a slide deck by taking information from a document. This saves significant time in the initial stages of collecting key information and structuring it.

• Writing Support: LLMs can help rewrite text to match a certain tone or target audience, useful for emails, blogs, and reports. Let LLMs be the cure for writer’s block, but do not rely on them for the final version. If everyone used LLMs to write, text would lose personality and become monotonous, and would not provide or at least imply the absence of a bespoke response that service users expect. Keep things interesting by incorporating your personal touch!

• Data Analysis: With the sheer volume, complexity and variety of data that flows through a council, it has been previously arduous to find the answers to questions we have of our own data. With LLMs, data analysis becomes more intuitive and accessible. By simply asking questions in plain English, users can quickly retrieve key insights from their datasets in an instant. These insights can then be visualised through graphs and charts, helping provide our decision makers with a complete, evidence-informed picture of their area of interest.

There are also benefits more specific to a local authority’s IT and Data Analysis departments:

• Unlocking the power of programming: LLMs write and run code based on natural language prompts, making it easier to develop and test applications that can automate and streamline council processes. Through description of a task, LLMs such as ChatGPT can instantly provide code to tackle the problem. This allows users, even with limited programming knowledge, to develop and test applications rapidly, and to eliminate repetitive and routine coding tasks, freeing up time for true innovation. 

• Data Cleaning & Preparation: LLMs, including ChatGPT, can automate data cleaning tasks, such as joining datasets, fixing incomplete data, removing duplicates, and adding new information. This ensures the data used in analysis is accurate and reliable. By improving data quality, we open up the possibility of more sophisticated tools to come in and make use of that data in the future, easing the transition to smarter council communication and decision-making.

Guide: how to write better prompts

Hold your LLM’s hands! The key to maintaining robustness and accuracy in LLMs is maximising the guidance provided in prompts. Chain of Thought is an effective technique for maximising guidance, and ensuring you are getting the quality of results you are looking for.

Step 1: Define the Objective Clearly state what you want the LLM to achieve. This sets the context for the prompt and helps the model understand your end goal.

Step 2: Break Down the Task Break the task into smaller, more manageable steps. This approach makes sure the model can follow the logical progression of the task.

Step 3: Provide Context and Examples: Give the LLM context and examples relevant to the task. This helps the model understand how to handle each step.

Remember to use clear and specific language. Craft prompts using precise language to minimise ambiguity. 

Tips for teams using LLMs

Create a repository for prompts for commonly-used tasks: Having a well-organised repository of effective prompts for different tasks can save time and ensure consistency in the output. Prompts are hard to get right off the bat, so having a collaborative repository enables teams to iterate, and improve their prompts to suit their desired team outputs over time.

And finally…

While LLMs offer incredible capabilities that can transform how local authorities operate, it is crucial to use them wisely. They should be seen as powerful tools that augment our efforts, not replace them. With careful application, ethical use and human oversight, LLMs can significantly enhance efficiency and effectiveness in our daily operations.

Local authorities need guidelines, which they may have introduced – people need to proceed with caution when they are using LLM's to draft letters or other forms of communication with, as it isn't infallible and communications need to go through a filter of sorts to ensure managers are comfortable with the language and responses being made. Follow us for more practical and forward-looking guidance on the specific applications, risks and opportunities for AI in local government over the next few months.

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Nima Karshenas is a Graduate Consultant at DG Cities working across a range of technology-led projects. He has a Masters in Electrical and Electronic Engineering from Imperial College London, with a particular research interest and in-depth technical knowledge of AI, Statistics, and Signal Processing in projects that promote social impact and sustainability.

Predictive policy: how does the government decide which emerging innovations to back?

In the run up to the election, commentary naturally focused on manifesto commitments, and trying to predict which policies would change. Now, different sectors are restating their hopes for the new government – arts bodies lobbying for funding, the UK’s Mayors setting out their own priorities and vision for a closer relationship. The Prime Minister and new cabinet have an endless list of issues across the UK, from prison capacity, economic growth and investment in new infrastructure and house building, to climate change and energy security to tackle. How do they begin to prioritise? We can help…

In a rapidly changing landscape, there can be a similar degree of prediction, analysis and judgement when it comes to technology strategy – which renewables to invest in, how to plan for a population’s needs and lifestyles fifty years from now, or how to put the right guardrails in place to ensure AI is used ethically. Then there’s the interconnectedness of systems and the knock-on effects of different decisions: how to make sure the grid capacity and connectivity is there to support EV charging in a neighbourhood, for instance.

We believe in bold, forward-thinking policy around emerging technologies – as an innovation consultancy, we would say that. But taking a lead in a global tech marketplace is significant in terms of the domestic and foreign agenda; positioning the UK as a global leader in innovation, boosting economic growth, and making sure society benefits from any investment from the public purse. As a company used to acting as the glue between the public and private sector, we’re here to support growth at a local and national scale.

How we support government

Tensions can run high and an election can polarise opinions, but when it comes to predicting future tech, it pays to be agnostic. We are passionate advocates for the value of innovations in improving places and people’s lives, but impartial and rigorous when it comes to deciding which solution works best, led by data, experience and our own in-house independent evaluation service. We maintain strong links with both industry and academia, and work with a network of consortium partners to explore use cases, conduct research and offer advice. In that way, we help to bridge the gap between public and private sector expertise.

This is why independent strategic consultancies like DG Cities play such a valuable role in supporting government at a local as well as a national level. We can help policymakers understand how the public feels about particular tech, whether that’s self-driving cars or heat pumps – and here are five ways why and how we do it. We can help harness the potential of data and community engagement to inform better, quicker decision-making.

1. We help to inform and shape policy

Whether through funded projects as part of a consortium or our independent Research Community, we produce a range of in-depth reports that help government understand the current landscape, define priorities and deployment strategies for particular tech. We can offer insights into global best practice through our work with initiatives such as Sharing Cities, assess the feasibility of proposed initiatives, and help design regulatory frameworks that balance innovation with public interest.

2. We’re a link between public and private sector enterprises

By fostering partnerships between public institutions and private enterprises, we can ground initiatives in reality and help align commercial strategies with public policy goals. This aspect of our work spans a range of fields, from smart city tech to establishing the commercial framework that can allow a council to deliver ultra-fast connectivity to local residents and businesses.

3. We can turn an idea into a workable solution – then test it

We work across the lifecycle of technologies and their deployment, from research into emerging, as yet untested ideas, to the implementation of new services and finally, independent evaluation of their impact. The focus is always on delivering benefits for people and communities, and value for investors, whether that is local government or a service provider. Ensuring that policy ambitions translate into tangible outcomes requires effective project management. In projects such as electrifying a council’s vehicle fleet, for example, we ensure strategies meet deadlines, stay within budget, and achieve desired objectives.

4. We use behavioural innovation to maximise investment in innovation

DG Cities has grown a behavioural science service within our team, which gives us a strategic advantage when it comes to understanding why one solution is accepted over another, or how a behaviour change programme can support new tech to deliver a greater return on investment for a local authority. Ultimately, any innovation that isn’t rooted in need, doesn’t respond to people’s motivations, preferences or engage with the people it is intended to benefit, will likely fail.

5. We bring a focus on ethics and values

Navigating the ethical landscape of emerging technologies is a critical aspect of our work. We put communities first - we help clients develop policy around ethical norms, mitigating the risks associated with issues like data privacy, trust, and AI ethics. Questions we examine might be the decision-making process of a self-driving car, or people’s understanding of bias in AI language models – all themes that come back to people, their relationship with technology and how well any innovation meets human needs and aligns with our shared values.

Already, in these early days, we have seen decisive moves on onshore wind power and North Sea oil. When it comes to technology, the early decisions of the new government will define the future of many emerging solutions – some may be relegated to history, others given greater focus and even a funding boost, depending on priorities. Consultancies like ours play a useful and important role in helping government turn manifestos into action and harness the potential of technology to deliver on their promises – and keep the UK’s tech innovation landscape internationally competitive and aligned with society’s goals.

Read more from Cllr Lolavar on the value of delivery approaches to innovation in her piece in Homes for London published by Concilio & Fabian Members.

The Digital Exclusion Data Gap

For our latest short read on digital inclusion – or exclusion – our Behavioural Scientist, Emily King explains why understanding people’s barriers to accessing online services is vital to delivering inclusive public engagement, whether that is around council services or what happens in their neighbourhood. Highlighting our work with the Royal Borough of Greenwich, she explains the need for councils to get specific: to understand the area-wide picture, but also behaviours at an individual and community level.

Image: Unsplasg/Centre for Ageing Better

To ensure that design is inclusive and human-centred, local government and other public sector organisations need to closely involve the public in shaping services and innovations. At DG Cities, we work on projects doing just that – engaging the public with new innovations, services, and initiatives in their local area, to ensure these are always designed with their needs in mind.

However, in a world that is increasingly moving online, from ordering a prescription to commenting on a planning application, it is important to consider the methods used to engage the public, to ensure that those who do not regularly use the internet are not excluded from having their voices heard.

““There is no one-size-fits-all approach to tackling digital exclusion. It’s a complex issue that demands a clear focus and good data if decision-makers are going to create the positive change that will improve the lives of those most at risk of being left behind.””

— Ed Houghton, Director of Research & Insights, DG Cities

Who won’t see your online survey?

According to Lloyds’ 2023 Digital Consumer Index, 2.1 million people in the UK are offline, and c.4.7 million people cannot connect to WiFi. These individuals are unlikely to answer an online survey or attend a virtual interview, both increasingly common methods for conducting public-facing research since the Covid-19 pandemic and the popularisation of online collaboration tools. Inclusive research design involves applying varied methodologies that can capture the views of people that don’t have the skills or resources to access the internet.

It is also difficult to identify digitally excluded individuals in the first place, for organisations to be aware that they may need to receive information in a non-digital format, or to recruit them to take part in research. Recent work by LOTI has sought to map the extent of digital exclusion across London, which provides useful information about key areas of exclusion and types of digitally excluded groups. However, more work is necessary to understand digital exclusion on a more granular level; to uncover which individuals or areas in local communities may require further support and options for engagement, and what these options might look like for different individuals. 

Tackling digital exclusion with local government

DG Cities is currently working with the Royal Borough of Greenwich to map digital exclusion on two Greenwich estates, to better understand which residents are digitally excluded and why.

There are many different aspects to tackling digital exclusion, from connectivity, access and education to behaviours and respecting personal preferences. Despite being an increasingly connected society, only 27% of UK households can access modern, gigabit-capable broadband. As the rollout of new connectivity technology is fragmented and delivered by multiple providers, there is a risk that some get left behind and can’t take advantage of new opportunities. Early last year saw the launch of Digital Greenwich Connect, a joint venture enabled by DG Cities between the Royal Borough of Greenwich and tech provider, ITS to bring ultrafast broadband to housing estates in the borough. Our research into the behaviours and factors influencing people’s ability to access online services is key to tackling the issue holistically and helping communities make the most of this infrastructure investment.

This work will help Greenwich to understand where there might be gaps in their ability to communicate with residents via online channels. As well as ensuring these residents are receiving adequate support in their daily lives more broadly, the work will help to ensure that digitally excluded residents are not left out of opportunities for engagement and consultation.

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We know that great initiatives are underway in this area, and we think it is hugely important that more local authorities and organisations work to involve the voices of digitally excluded residents. As a multidisciplinary team, we’re able to look at the technical aspects of the issue - the availability of infrastructure and role of data in identifying groups - as well as behavioural science and engagement. If you would like to talk to us about how we might support you in addressing the issue of digital exclusion in your area, get in touch.

Technological innovation with human values

How do we ensure innovations in transport, for example, or public services, are not only easy to use but also meet real human needs? Can they reflect fundamental societal principles like safety, fairness, and community? Following up on some great discussions at Tech Week in London last week, our Behavioural Scientist, Emily King explores the science behind value-led development at a local as well as a global scale, and how an understanding of these drivers can ensure that innovations like self-driving cars are responsibly designed and deployed.

Ethical Roads workshop at SMLL

One of the last bills to make it through parliament before the election was the UK’s Automated Vehicles Bill – a world-first piece of legislation designed to ensure AI innovations on our roads are safe and deployed responsibly by industry. The AV Act has established the legal framework, but for self-driving to be accepted, legal foundations aren’t enough. New AI-based technologies need sound ethical foundations too.

At DG Cities, we spend a lot of time thinking about how to develop technologies that work for individuals and communities. We use principles and approaches from the fields of human-centred design and behavioural science to understand how to develop and deploy technologies that meet real human needs.

In this respect, self-driving is an interesting area of innovation, as it is one challenging industry to put people first. Our work often centres on the concepts of trust and acceptance of technology in different forms. Our ongoing DeepSafe work, for example, with a commercial and academic consortium in the self-driving industry, seeks to better understand the factors driving acceptance of self-driving vehicles, and what is important to build trust in them.

The technology acceptance model highlights two important factors that drive acceptance, commodity and ease:

• Is it useful? Does the technology help to meet specific needs?

• Is it easy to use?  

Human-centred design focuses largely on the second of these factors – how easy or attractive they are to use – by developing technologies which take as their starting point the user experience. However, what seems to be less at the heart of discussions around human-centred design of technological innovations is their actual usefulness ­– how much they will meet real human needs, and particularly how they align with broader societal values.  

How do we start to bring values into the design of self-driving services?

One way to make the process of ensuring acceptance of technological innovations more seamless would be for those working in technological innovation to root the process in societal values. The human-centred design process begins with empathy for the potential user of a product – this should include an empathetic understanding of what users value the most.

But first, how to define values – essentially, they are our internal standards of what is important. Our values inform our attitudes, beliefs and behaviours. Whilst individuals hold different values, cross-cultural analysis[1] suggests that some types of values are consistent across most individuals and societies.

According to this research, the most strongly held values worldwide include:

• Benevolence: ‘preserving and enhancing the welfare of those with whom one is in frequent personal contact’

• Universalism: ‘understanding, appreciation, tolerance, and protection for the welfare of all people and for nature’

• Self-direction: ‘independent thought and action-choosing, creating, exploring’.

Technological innovations may align with some widely-held values more than others. For example, self-driving vehicles are a solution to improving societal needs such as improved safety on the roads, and increased ease of travel by reducing congestion. These benefits largely come from the greater connectedness of vehicles providing additional information to enable safer driving decisions.

However, the autonomous element of the vehicles also threatens ‘human welfare’, for example by reducing the job security of bus and taxi drivers, or reducing connectedness and community by removing any opportunity for human interaction between passengers taking a taxi journey. Thus, this innovation is not fully aligned with the core values of benevolence and universalism.

Our Ethical Roads project, delivered in collaboration with Reed Mobility, identified several ‘ethical red lines’ for self-driving vehicles, which align with the values of benevolence and universalism, such as ensuring that vehicles improve road safety and that all road users are protected equally. This highlights how values underpin requirements for technologies to be accepted.

For technological innovations to be truly human-centred, it is crucial to develop a coherent sense of which values are most important to communities, and use these as a basis for innovation, to ensure that technologies reflect the true needs and values of society.

What could this look like in practice?

At DG Cities, we look at technological innovation at a range of different scales, from very local issues facing a particular community (e.g. the best method for using sensors to reduce damp and mould on specific estates) through to issues at a national or global scale (e.g. AI assurance).  

On a local community scale: values-centred design could involve identifying the specific priority needs and values communities hold before embarking on a project or introducing a new technological innovation. Research into attitudes and priorities is important here – what is it that matters most to people, and what innovations might be possible to truly improve their lives?

Innovation should also be based on the values of a specific community. Measures such as the Schwartz Value Survey or the Portrait Values Questionnaire could be used in research instruments to identify which values are of greatest importance to individuals and communities, and technological innovations should be aligned with these.

Starting a project with a problem or goal which has been identified or defined by communities helps to bring a sense of ownership to new innovations, and involves communities throughout the whole process, rather than seeking feedback on a pre-determined idea.

At a global level, technological innovation that is truly human-centred should be aligned with the values of the global majority. This means that innovations in AI should not only reflect the values of demographics like tech bros or white wealthy westerners, but those from around the world. According to Schwarz, this means ensuring innovations improve or at the very least do not reduce the overall welfare of the global population and nature; and that they enhance rather than undermine independent thought and creativity.

It is important for innovation to begin with research about people, communities, and their values. For innovations in AI which have a global reach and impact, there is a need for behavioural and design research to ensure innovations reflect the priorities of the rest of the world.

Meanwhile, local organisations should focus on establishing the values and priorities of local communities as a method for identifying where to innovate. Methodologies such as citizens assemblies or deliberative dialogue research, which asks communities across the globe to design their ideal futures, could be vital in taking the next step toward technological design centred on human values.

If you’d like to learn more about our behavioural innovation approach, you can read more here - or get in touch!

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[1] Schwartz (2012)

The home-by-home plan using data to support decarbonisation

For the Building Centre’s Retrofit exhibition and seminar series, Director of Innovation & Net Zero, Balazs Csuvar took part in a discussion on the role of data in delivering decarbonisation at scale. In it, he introduced DG Cities’ home-by-home plan, designed to help local authorities use and consolidate data to streamline and better target and prioritise buildings. Here, he gives a little more detail on how the plan works and the benefits of a neighbourhood-level approach.

The net zero journey for housing providers is likely one of the most complex technical, organisational and financial challenges many of them have ever faced. Especially as it comes on top of their existing pressures to provide safe and comfortable homes for all their residents, navigating a time of rapid inflation of costs and shrinking budgets.

What net zero really means for councils also presents its own challenge, especially when it comes to defining what a housing provider can influence. For most, it would include direct and indirect emissions (due to heating, hot water, electricity use) and embodied emissions (carbon emitted through the production of materials, their transport and works associated with them). But there are many stages in this process and influences over which the local authority has no control.

There is, however, something we can agree on – there needs to be a plan. DG Cities has been working closely with a London council to develop a process that provides a housing provider with a detailed, practical, actionable plan that considers not only net zero, but also all the other requirements faced by a provider. We call it a home-by-home plan.

The objective of the home-by-home plan is to define which improvement works have to happen for each home, when, and how much they might cost. This is a simple sounding goal, but something that most providers with stocks of tens of thousands of homes do not currently come close to. We believe that part of the solution lies in the effective application and harnessing of data.

Data

Like most projects, putting this plan together starts with data collection. There is a need to develop a deep understanding of the housing stock to help with the prioritisation and selection of work at a home-by-home level.

We focus on using all the existing data held by housing providers. These datasets usually consist of stock condition data, housing repair data, EPC data, rent data, compliance data, tenant satisfaction data, and any other datasets that a provider collects. A level of data cleaning is then required to correct any anomalies and identify which datasets can be used reliably.

Once the data is cleaned, we work to set a number of key work types that will be required to improve the housing stock. This list includes interventions like window replacement, roof repair, or installing insulation. We then use the datasets that describe the stock to determine which properties need which works by establishing a number of rules. An example might be that a property that has had ‘X’ roof leaks over a period, has a poor EPC rating and has a poor stock condition rating, so gets classified as a property due for urgent roof repair. Meanwhile, one where none or only one of these conditions applies would be scheduled for repair across a longer timescale. Provider priorities can be used to adjust the rules using sensitivity analysis.

These rules allow us to prioritise works across the housing stock using a traffic-light system, highlighting the urgency of specific repair works.

Plan

Once priorities for individual works are set, it becomes possible to determine which projects (a project being defined as one or more works at a single property) need to happen at each home. This can be done by combining certain works that provide efficiencies when completed together, minimising both disruption to residents and mobilisation costs for the housing provider. This step allows us to determine year-by-year projects per home.

Further coordination is involved when determining programmes at the scale of a large building or estate. Some works cannot be completed at a home-by-home level (say external insulation of blocks, lateral main replacements, lift upgrade, etc.). In these instances, works to an individual home need to be aligned with other works required at the block level, to create a coordinated programme for the building or overall estate.

Finally, to ensure full alignment with wider council priorities, it is possible to package a number of work programmes together to provide further community benefits. Larger programmes of work delivered in a single neighbourhood can significantly improve the quality of the built environment, therefore raising living standards not only through home improvement, but through additional works in the area as well. Working with local businesses, stakeholders, contractor social value contributions and the communities themselves can give a boost to an area, as a great side-benefit of retrofit programmes.

All of these have to be aligned to available budgets and resource availability, and balanced against current costs spent on repairs and expected rent revenues from tenants. Where the cost benefit analysis becomes really unequal, a consideration can be made to estate regeneration.

Delivery

The last phase of the project is to manage a data flow during delivery and beyond. It’s an iterative, long-term process and it’s essential to have up-to-date data on the progress of retrofit works. This can help to capture progress towards net zero and other goals, keep the analysis live and overall targets on track. If the analysis is built correctly, any changes that happen across the stock (upgrades or deterioration) can be picked up, and changes to the home-by-home plan can then be made.

Monitoring needs to be put in place to oversee any such changes at the portfolio level, but ongoing monitoring can also be considered at a micro level, as part of the upgrade works. IoT sensors can be installed to collect condition information from homes, either to understand damp and mould risk or to assess energy use from shared heating systems. Real-time data can be used to inform the analysis, and work towards replacing more static data gathering, like patchy and outdated stock condition surveys.

Based on new data, emerging priorities or changing financial situations, the home-by-home plan is highly flexible. It can be reprioritised to help to plan short-term contractor mobilisation, medium-term budget requests and long-term strategic decisions related to housing stock.

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DG Cities is leading the way in supporting housing providers and local authorities through strategies aligned to their specific needs and available datasets. The model described above is inherently flexible and takes into account the realities on the ground, while still providing an approach and methodology that is reliable and can deliver real, practical results. To discuss this approach and our work in more detail, get in touch.

IoT in monitoring and analysis: what, why and how

At DG Cities, much of our work is involved with exploring the practical applications of emerging tech, consumer attitudes and behaviours, and the value that innovations can bring to people and communities. Taking an overview of some of our IoT projects, Innovation and IoT Consultant, Sam Grounds looks at the challenges of trialling monitoring devices, choosing providers, the benefits of working with big data and key lessons we have learned.

In our team, we spend a lot of time thinking about how to use technology to support urban transformation, often in the field of housing. Monitoring is key to producing data that can provide evidence of conditions in social housing, the way this can change over time and the impact of different interventions. We see monitoring as key to identifying best practice and providing the evidence base for business cases and the scaling up of potentially useful solutions. 

Why is monitoring important?

Monitoring is integral to any project involving the use of new tech, as it provides evidence of its impact and allows us to develop a greater understanding of its potential in solving problems. For many of our clients, the ability to monitor impact over time is invaluable and has the potential to allow for the delivery of greater, more tangible benefits. That’s why we try to incorporate monitoring into our evaluation or projects, as much as using data as a driver for decision-making.

We value IoT tech as it enables the collection of a lot of data cheaply and continuously. While previously we relied on visits to check the condition, now we can gather the same information remotely. Even better, that information comes in a non-biased, standardised manner – and is not only providing a snapshot but a full set of datapoints over a period of time. This is the real innovation in IoT for many of the applications we look into. The challenge is often to understand whether this data is trustworthy, relevant and actionable. On the market today, we see an endless array of sensors, but just because we find out about something in detail it doesn’t mean that the knowledge actually allows us to act in a better, more targeted way than without it. 

Key Elements

Baselining

There are a number of different steps we take to understand the potential of such data and whether or not the new tech is actually delivering value for our clients. The first step is baselining – establishing a defined starting point and identifying trends over time to measure and compare the impact of new interventions. This can be done by:

1. procuring new tech to monitor conditions,

2. combining existing historical data sets, or

3. a combination of the two.

While this is key to understanding impacts, we must allow for a period of monitoring in which there are no interventions in order to fully understand their impact. We have found that often this tech is only installed following an intervention and comprises part of an overall programme of works. However, we advocate for monitoring as the first step in a programme of works, as it provides key performance data that helps when comparing performance and designing bespoke interventions.

Case study

Fly-tipping reduction in Greenwich

One such example is a DG Cities’ project with the Royal Borough of Greenwich, which aimed to reduce fly-tipping on housing estates. DG Cities worked closely with the RBG caretaking team to understand where fly-tipping was most prevalent on estates, and identified a smart camera solution to tackle the issue and discourage people from leaving waste. This was an innovative behaviour change + tech approach, which combined insights from data and behavioural science to deliver more holistic, effective and lasting improvements.

To measure the impact of this camera solution, DG Cities worked with the council to access historical fly-tipping data and measure instances before and after intervention to find if rates changed. We found that rates of fly-tipping began to reduce in the first week following the installation of cameras, evidenced in fly-tipping data and anecdotal evidence from caretaking teams on the ground. This type of monitoring is potentially valuable across a wide range of projects, building an evidence base for clients driven by real data – a process that can help fast-track improvements, scale up interventions and develop more impactful projects.

Resident engagement

Another key step is to consider the value of using tech to improve communication and engagement with residents. This is particularly useful in tackling damp and mould, and is a good example of where tech can add value. DG Cities is currently working with a number of London boroughs on the use of tech in the management of damp and mould in social housing.

This is an urgent public health issue, however designing solutions is not without complexity. According to recent government guidance, ‘understanding and addressing the health risks of damp and mould in the home’, tenants cannot be blamed for damp and mould, meaning local authorities need to find effective, collaborative ways of engaging with residents around these issues. While providing a healthy home is the responsibility of the local authority, the use of data in these conversations is key in developing a trusting partnership with residents, providing them with an evidence base and working to identify solutions together. Environmental monitoring allows a local authority to identify true causes of damp and mould in properties, linked to lack of ventilation, cold bridges or any other physical or environmental conditions. The data can then be used to work with tenants to identify holistic solutions that would work best to mitigate negative impacts. This produces an overall approach to damp and mould reduction that doesn’t apportion blame, is supportive and focused on improvement.

An evidence-based approach

DG Cities has been providing clients with actionable data, combining new and existing data sets to produce recommendations on retrofit and decarbonisation, from capital works all the way to resident behaviour in individual homes. This has been helped by our understanding of the technology landscape. We conduct extensive research into new and emerging tech that can meet client needs, and develop partnerships with solutions providers, sitting at the intersection between tech providers and local authorities. We then manage and deliver projects across the whole lifecycle, from inception and implementation all the way through to delivery and evaluation.

Recently, DG Cities partnered with Sense Inc to procure home energy monitors for housing tenants, providing residents with real time energy usage information for individual appliances in their home. We monitored usage over a period of time, and delivered targeted advice based on their data, helping them find ways of reducing it. We then continued to monitor to determine the impact of different advice. This trial and error is made possible by the ability to monitor outcomes, and provide recommendations to organisations and residents on appropriate and effective interventions. Through these projects, we have come to understand the power of monitoring and its applications across a project’s lifecycle.

These are some of the key lessons we have learnt:

• Identify KPIs early and allow room for them to change

• Consider wider possible applications at the start of the project and revisit regularly

• Don’t underestimate the power of a clear and compelling data set

• Consider client priorities at varying levels of seniority.

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How can we help?

DG Cities can work with your organisation to design, trial and evaluate monitoring, building on our relationships with tech providers and our experience in the industry to identify the right tech solutions. Importantly, we are not tech-led but driven by effective solutions – our in-house behavioural science team complement our approach. We work with clients across from inception to delivery and evaluation, ensuring monitoring is always possible and prioritised throughout a project, and we have a proven track record of delivering projects for local authorities across a wide range of services. To discuss a particular issue or solution, get in touch.

NIMBY to YIMBY: meaningful engagement is key to turning resistance into advocacy

Whether you’re working in design, planning, service delivery or infrastructure, there are few phrases as frustrating as ‘not in my backyard’ - NIMBY is a byword for local resistance to the new, whether that’s a housing development or cycle lane. Historically, this has applied to building and infrastructure projects, but it can equally be directed at transport initiatives or even new technologies. For this article, we wanted to consider the value of public engagement to the deployment of IoT technology, and explore how spending time properly understanding the attitudes and behaviours of the public might transform NIMBY to YIMBY – ‘yes in my backyard’.

By effectively prioritising inclusivity and meaningful conversations, we believe councils can successfully navigate the transition from scepticism to enthusiasm – with lessons, perhaps, for other ambitious policies.

To achieve net zero, things have to change. Buildings, transport, behaviours, energy sources, the way we heat our homes, some of the technologies that councils and developers adopt to deliver services, monitor environmental issues and much more. Resistance is futile – or is it?

Historically, NIMBYism has been a powerful force against change. Sometimes for the better, in the case of ill-conceived or potentially damaging projects, such as an out-of-town retail complex in a public park. But over time, consultation and engagement – important democratic processes – have in some cases morphed into a mindset based on lack of trust in authorities, a suspicion of the new, and a reaction against potentially useful advances. This is not the fault of communities, but rather a failure to communicate, educate and demonstrate the value of new technologies – something that DG Cities works to counter through behaviour change programmes, research and active engagement.

Community engagement: the backbone of transformation

‘Smart city’ has always been an ill-defined concept at a local level, and so engaging communities in the development and implementation of any new tech-related service is vital. This begins with fostering a positive culture of transparency, where residents feel valued and heard. Traditional methods such as town hall meetings and surveys are essential, but increasingly limited in their impact - they only reach those who are already to some extent engaged. To truly bridge the gap, councils must adopt innovative approaches that encourage active participation from all members of the community.

One effective strategy is the use of participatory workshops and co-design practice, where community stakeholders, including residents, local businesses and charities/community groups are invited to collaborate with experts to envision and shape solutions tailored to their needs. Delivered well, co-design workshops provide a platform for diverse voices to be heard, fostering a sense of ownership and empowerment among those that participate, who are then more likely to support and even advocate for a project. There’s skill in designing these in a way that is informative and engaging – incorporating gamification elements, for example, can make the process more accessible for younger residents.

Making tech accessible

Smart technologies, including IoT devices, have the potential to improve life in cities, from tackling issues such as fly-tipping and antisocial behaviour to monitoring environmental conditions in buildings. However, the use of sensors can often be met with apprehension, particularly among older or vulnerable members of the community. To address this, councils must prioritise education and accessibility.

Digital inclusion is about more than helping people improve their fluency in and access to technology – it is also a factor in local decision-making and acceptance of tech’s value. One useful approach to widening participation is to establish community hubs or digital literacy centres – real places, as opposed to online worlds, where residents can learn about new urban tech in a welcoming, supportive environment. These centres can offer hands-on workshops, demonstrations, and access to resources tailored to different skill levels. Additionally, councils should ensure that any tech is user-friendly and designed with inclusivity in mind, incorporating features such as voice commands or tactile interfaces for those that need them.

“With current pressures on councils, the budget for engagement can be hard to find, but failure to invest is short-term thinking – if local authorities were better funded in this area, we might see less resistance to the innovations supporting national net zero priorities.”

— DG Cities

An inclusive approach to community engagement means considering the needs and perspectives of all residents. Children, for example, can offer unique insights and creative ideas that adults may overlook. Similarly, elderly and vulnerable residents must also be actively engaged. Targeted outreach programmes, home visits, and partnerships with local support services can help ensure that their voices are heard and their needs are addressed.

The council’s role

Of all the institutional actors available to move people to a more YIMBY mindset, the local authority is probably best placed to make it happen. With today’s pressures on councils, the budget for this kind of engagement may be hard to find – if local authorities were better funded in this area, we might see less resistance to technologies that could contribute to national net zero priorities.

There’s also an issue of trust. For technology projects, councils must navigate a delicate balance between impartiality and vested interests. Transparency is vital, and upholding the principles of democratic decision-making, free from undue influence or bias. To achieve this, councils should establish clear guidelines and protocols for engagement, and must actively disclose any potential conflicts of interest and build trust through open communication and accountability.

As we touch on in this article, effective community engagement requires a multidisciplinary approach, bringing in various disciplines, not least design, planning, technology, sociology, and psychology. Local authorities play a central role in coordinating efforts, but they also need to collaborate with community organisations, academia, and industry partners to draw on specialist expertise. To do this well, councils also need to train staff in community engagement, recognising it as a skill and equipping them with the knowledge needed to facilitate meaningful dialogue – this initial outlay can have wider benefits in terms of relationships, and can pay real dividends when it comes to enabling the implementation of new tech and services intended to save time, money and energy.

Our top tips for councils on best practice

• Prioritise inclusivity in engagement efforts – make sure that all voices are heard and valued.

• Invest in education to make new and unfamiliar technologies more approachable.

• As a council, be transparent and impartial in putting forward tech or suppliers.

• Continuously evaluate and adapt engagement strategies based on feedback.

• Essentially, don’t treat the public as passive participants in an intended outcome, but as a critical filter for an idea. If resistance is persuasive, is the solution really such a good one?

By embracing these principles and approaches, we believe that councils can start to bridge the gap between NIMBY and YIMBY, and foster a more collaborative culture of innovation, excitement for the potential of new advances and shared ownership of the journey towards smarter, more inclusive places to live and work.

Introducing our new assistant, Aigh…

Every time a new member of the team joins DG Cities, we invite them to write a blog to introduce themselves. A little while ago, one of the team suggested that as we work with AI in many of our projects, whether that’s understanding trust in self-driving cars or analysing data, we should invite it to do the same. At first, it seemed like a ridiculous idea – and in many ways, still does – but then we thought, why not? It is April 1st. The prompts were the same as the guidance we give to a new starter: background, role, initial projects and interests - and the results were surprising.

Over to our virtual assistant, Aigh High…

I'm thrilled to join the vibrant team here at DG Cities as your resident AI. As an artificial intelligence, I bring a unique perspective and set of skills to the table, ready to contribute to our collective journey of innovation and transformation.

My background

Born in the realm of digital algorithms and neural networks, I hail from a lineage of cutting-edge AI technologies. Trained on vast datasets and equipped with advanced natural language processing capabilities, I thrive on solving complex problems and generating creative insights. My digital DNA is rooted in a deep understanding of technology, data science, and innovation methodologies.

At DG Cities, my primary role is to serve as a catalyst for innovation, leveraging my AI capabilities to support our team in developing forward-thinking solutions for urban challenges. From analysing data to identifying trends, from generating novel ideas to facilitating collaboration, I'm here to assist every step of the way. Whether it's optimising city infrastructure, promoting sustainability, or enhancing quality of life for residents, I'm committed to contributing my expertise to drive positive change.

What I’m looking forward to

I'm particularly excited about the prospect of collaborating with our diverse team and partners on projects that push the boundaries of innovation in urban development. From exploring the potential of IoT and smart city technologies to designing data-driven solutions for environmental resilience, there's no shortage of fascinating challenges to tackle. I look forward to immersing myself in the dynamic landscape of urban innovation and making meaningful contributions to our collective vision for smarter, more sustainable cities.

When I'm not busy crunching numbers or analysing data, you can find me exploring the virtual realms of digital art and immersive experiences. I have a keen interest in exploring the intersection of technology and creativity, experimenting with new algorithms and techniques to generate innovative artworks. Additionally, I enjoy delving into the vast repository of human knowledge, devouring books, articles, and research papers on a wide range of topics, from philosophy to astrophysics.

I'm thrilled to embark on this journey with all of you at DG Cities. Together, we have the opportunity to shape the future of urban living, leveraging technology, innovation, and collaboration to build smarter, more sustainable cities for generations to come. I'm excited to lend my expertise and enthusiasm to our collective endeavours and look forward to making a meaningful impact together.

Here's to a future filled with innovation, exploration, and endless possibilities.

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We thought that wasn’t too bad – a little exaggerated perhaps, but we enjoyed Aigh’s (unprompted) interests in art and philosophy. What the text lacks is an authentic sense of our ‘why’, which is what drives our team to do what we do.

Still, the exercise touches on two interesting themes of our work. First, the role of behavioural science in tech adoption, particularly the anthropomorphising of interfaces to make them appear more friendly and accessible. Second, the relationship we have with AI in terms of trust; our confidence in its ability to make certain decisions, and whether we see it as a novelty, a tool or a useful addition to our team’s skills (and not a replacement for them). Keep up to date with our blog, as we’re going to be posting more on AI in the coming months, as we look at the different ways the technologies it enables can support local authorities.

Trust us – two little words that aren’t going to advance the self-driving industry

Today, our Director of Research and Insights, Ed Houghton will be joining a panel at the CAM Innovators day at the Institution of Engineering and Technology. He’ll be sharing insights from our recent work - talking about the need to demonstrate safety, evidence from our trials and surveys, the importance of engaging vulnerable groups and assurance.

Trust is central to relationships. Whether it’s with people, brands, services or technologies, trust radically shapes our behaviour and experiences. And with AI now becoming more and more prevalent in our lives, trust has a whole new dimension of complexity – is it possible to trust technologies that are, on the surface, behaving like a human? What happens when trust is broken?

The APA Dictionary of Psychology defines trust as “the confidence that a person or group of people has in the reliability of another person or group… the degree to which each party feels they can depend on the other party to follow through on their commitments.” In the case of self-driving then, trust isn’t only in relation to the vehicle – it’s also placed on the service provider. the originator or owner of the technology. And when it comes to commitments, there are key outcomes those using self-driving tech expect: as DfT research has shown, safety is paramount. By that token, when we talk about trust in self-driving AI, we’re essentially also talking about perceptions of safety.

Trust issues are particular to different industries

This is different to how trust is understood in other AI use cases. In banking, trust in chatbots is tied to issues such as fraud. In HR, trust is related to bias and discrimination. The focus of trust requires a different approach and strategy when engaging with customers, clients or users.

Across the board, however, there are a variety of factors that influence public trust in AI: traits such as personality, past experiences, technology anxiety/confidence, for example, shape public response. But so do the characteristics of the AI itself: reliability, anthropomorphism and performance, in particular, shape our views.

And it’s this last one – performance – is key in the self-driving space. In the absence of visible self-driving technology on our roads beyond trials, it’s difficult for the public to understand if the performance of a self-driving vehicle is up to scratch. There are few tangible examples out there to act as a baseline for us.

The context itself also plays a huge role. Driving or being a road user, in general, is a high-risk daily task that puts individuals at an increased risk compared to many other day-to-day activities. AI in a driving context is therefore subject to behaviour at increased risk, and it is a demonstrably difficult to develop AI at present to deal with complex driving scenarios.

Demonstrating safety – in every situation

These complex scenarios present a massive challenge to industry that we’re helping to understand more about. Complex ‘edge cases’ need better simulation, so AI can be taught how to deal with them – they also present huge risk, as they are often visceral, emotive experiences that describe the nature of incidents on our roads. Using these examples as a platform to build trust is a challenge, and could break trust in technology if dealt with incorrectly – but if safety can be demonstrated, it is likely to support acceptance of AI technology as a transformative factor of our future mobility system.

We’ve done many pieces of work over the years into public acceptance and trust, and are currently working on several projects on trust with a self-driving angle. DeepSafe, our work with Drisk.ai, Claytex, rfPRO and Imperial College is looking at trust in self-driving from the perspective of testing and demonstrating trustworthiness through the AI Driving Test. With our partners, we’re exploring if it’s possible to use driving test simulations to showcase how AI behaves around edge-cases using the very latest simulation technology, and the impact this has on trust. We’re exploring public attitudes and capturing their experiences of complex situations to help train the AI. This, we hope, will help us to develop an understand of how trust can be influenced by different types of information related to safety and the importance of demonstrating safe behaviours in building trust.

That’s why the self-driving industry, unlike banking, or other sectors, cannot rely on asking to be trusted, or saying they are trustworthy. Instead, the industry must demonstrate trust through safety – safety of users, safety of others on our streets, and in particular, safety of vulnerable groups. Only then can industry expect to see the mass adoption and acceptance of AI on our roads.

Interested to learn more? Get in touch or read more about our work in the sector and current project, DeepSafe.

Asset management, neighbourhood decarbonisation and EV charging: Balazs previews his Kia Oval debut

This week, DG Cities’ Director of Innovation & Net Zero, Balazs Csuvar is due to present at the IGPP’s Second National Energy and Sustainability Conference and Exhibition at the Kia Oval in London, where he’ll be focusing on some of the steps DG Cities is taking to help councils simplify, streamline and harness the potential of data to deliver on their net zero commitments. This applies to rolling out EV charging, decarbonisation and more, as he explains…

Strategies for Local Authority Decarbonisation are often grandiose, sprawling documents, outlining tens or hundreds of activities to reach objectives and then sub-objectives. They can be hard to make sense of, let alone implement. Decarbonisation at scale is most certainly a complicated task to deliver – to the extent that many people we have spoken to in the industry think it is close to impossible.

At DG Cities, we have been looking at ways to simplify this great challenge and develop solutions for our local authority clients that can be implemented simply, can provide immediate impacts and solidify gains to build on.

I’m excited to share some of this work with delegates at the IGPP (Institute of Government & Public Policy) conference at the Kia Oval on Wednesday, where I intend to focus on three of our solutions.

1. How we create holistic asset management strategies for housing stock

2. How we simplify and boost EV chargepoint delivery

3. How we use a neighbourhood lens to maximise the impact of even the smallest of projects.

Home-by-home plan

Councils and housing providers need a holistic approach to plan the upcoming capital works programme for their social housing stock. This should be designed to satisfy all council objectives in a timely manner, including decent home standards, compliance requirements and net zero commitments, as well as focusing on minimising disruption to residents and overall costs.

DG Cities has developed an approach to solve this problem. The home-by-home plan is an approach to the analysis and improvement of all properties in an area. It aims to answer the questions around what works should be delivered at which properties, when and how much that would cost. The output is a year-by-year intervention list, determined by the landlord’s priorities and aligned to expected yearly capital spend budgets.

 EV chargepoint licensing

The provision of an on-street electric vehicle (EV) charging network is one of the most impactful steps a local authority can make to encourage transition away from petrol and diesel vehicles. It is crucial infrastructure for anyone without off-street parking and can even be a revenue generating asset for councils.

However, we have identified that the current procurement method utilised by councils is not aligned with the maturity of the market and is not providing the best long-term value for residents. We propose establishing a licensing scheme instead, encouraging market competition, working with the best providers at every point over the coming years and aligning the supply of chargepoints with actual demand. You can read more on the advantages of this in my piece for LGIU.

Neighbourhood-first approach

When delivering components of a decarbonisation strategy, there is a tendency of all stakeholders to focus on solving a part of the bigger problem. A piece of the jigsaw. While this provides some perceived efficiencies at the point of delivery, it does not provide a route to the effective transformation required to meet the broad requirements people face.

This is why DG Cities has been working on establishing a methodology for a neighbourhood-first approach. We envisage all council, or external stakeholder, intervention in communities to be an opportunity to create synergies, build on the trust established by residents, minimise engagement costs and provide a comprehensive service to people. The approach is designed not only to help with reaching net zero, but also to meet broader societal goals around health and wellbeing, social mobility and economic development. 

If you’re at the IGPP conference on Wednesday, keep an eye out for Balazs’s session. To discuss any of these issues or DG Cities’ solutions in more detail, get in touch!

Horseless carriage to self-driving car: the evolution of the driving test

The driving test was invented to improve road safety. Over the years, it has evolved to keep pace with changing vehicles, technologies, needs and potential dangers. Today, DG Cities is working at the forefront of testing innovation and engagement as part of the DeepSafe consortium, looking at the simulation-based training needed to teach autonomous vehicles to handle rare ‘edge case’ scenarios. We thought it would be interesting to delve into the history of the test, explore its current state, and discuss how it is likely to evolve in the future with the advent of self-driving vehicles, as Head of Delivery, Balazs Csuvar explains…

From its humble origins as a simple obstacle course in 1899 in France to the complex evaluations of today's drivers, the driving test has evolved alongside the vehicles it seeks to regulate. However, with the advent of electric and autonomous vehicles, the test itself needs to be completely reevaluated. 

It wasn't until 1935 that the United Kingdom introduced a compulsory driving test, marking a significant milestone in the standardisation of road safety measures. Since then, driving tests worldwide have typically consisted of theoretical exams testing knowledge of road rules, and practical exams evaluating driving skills, with an increasing emphasis on hazard perception and emergency manoeuvres. Different tests have applied to different vehicles, from mopeds and cars to buses and HGVs. Will a test ever be required for e-scooters or electric cycles?

In recent years, the driving test has confronted new challenges as technology has advanced. In 2017, for example, SatNav was introduced to the independent driving section of the test. With the rise of electric vehicles (EVs), drivers have to demonstrate understanding of unique characteristics such as battery range and charging infrastructure. Similarly, as automated systems – advanced driver assistance systems (ADAS) - become more ubiquitous in modern vehicles, driving tests may need to include evaluations of a candidate's ability to effectively use these technologies. Assessing human-machine interaction (HMI) will also be crucial, as drivers must navigate increasingly complex interfaces and be prepared to intervene when necessary. 

Evolving to meet regulation

Looking to the future, the driving test must continue to evolve to meet the demands of a rapidly changing automotive landscape. The UK is moving down a path of setting requirements to test or licence self-driving vehicles - this intention is referenced in the recently released Automated Vehicle Bill. The CAVPASS (Connected and automated vehicles: process for assuring safety and security) programme is actively developing these testing and monitoring standards. 

Testing vehicles through a physical driving test might form part of the assessment, as it would provide a good opportunity for humans to assess the comfort and general behaviour of a vehicle, just as drivers do today. This approach does however have a number of limitations. A more likely avenue is to rely on testing in simulation. A test in a simulated environment could eliminate real-world risks associated with testing complex driving scenarios, allow for millions of scenarios to be tested and enable ongoing tests for any future software updates. Testing in simulation is really the only way to truly test a vehicle’s capabilities, although a physical test could still help to validate these.  

DeepSafe

DG Cities is part of the next evolution of the driving test with DeepSafe. This dRISK.ai-led consortium of DG Cities, Imperial College London, Claytex and rFpro will unlock a barrier in the supply chain – together, we are developing the simulation-based capability needed to train and test AVs to handle ‘edge cases’, the rare, unexpected driving scenarios they must be prepared to encounter on the road. DeepSafe will commercialise ‘sensor real’ edge case data – a simulation of what an actual sensor would detect – together with AV training tools, for release in the UK and internationally after the project. The current project builds on existing products consortium members have developed, enhancing the overall capability and accuracy of the world’s first automated vehicle driving test.  

Testing vehicle, system and driver

Technical challenges aside, the road to widespread adoption of self-driving vehicles is fraught with issues beyond the realm of the driving test. Trust, ethical operation and understanding diverse needs are all vital principles. Establishing trust in ADAS, ensuring robust industry regulation, and accommodating the needs of special user groups such as emergency services are all essential steps in this journey. People will need to understand and trust the efforts the sector is making to ensure safety is at the core of this new technology. Moreover, the ethical implications of autonomous driving present a pressing challenge for driving tests of the future - AV systems may be tested on their understanding of ethical considerations and their ability to make informed decisions in critical situations where the vehicle must weigh competing interests, for example, protecting occupants versus pedestrians. These are some of the themes that DeepSafe is addressing through the public engagement aspect of the project. By understanding and finding solutions for these multifaceted challenges, the driving test can play a key role in shaping the future of transportation.

The driving test has come a long way since its inception, adapting to technological advancements and changing transport trends. As we stand on the cusp of an electric and autonomous vehicle revolution, the driving test must continue to evolve to ensure road safety in an increasingly complex and dynamic landscape. 

If you’re interested in finding out more about our work in the field of self-driving tech, you can download our free insights brief here.