How AI is transforming local government

Robot

By Steven McGinty

Last year, Scottish Local Government Chief Digital Officer Martyn Wallace spoke to the CIO UK podcast and highlighted that in 2019 local government must take advantage of artificial intelligence (AI) to deliver better outcomes for citizens. He explained:

“I think in the public sector we have to see AI as a way to deliver better outcomes and what I mean by that is giving the bots the grunt work – as one coworker called it, ‘shuffling spreadsheets’ – and then we can release staff to do the more complex, human-touch things.”

To date, very few councils have felt brave enough to invest in AI. However, the mood is slowly starting to change and there are several examples in the UK and abroad that show artificial intelligence is not just a buzzword, but a genuine enabler of change.

In December, Local Government Minister Rishi Sunak announced the first round of winners from a £7.5million digital innovation fund. The 16 winning projects, from 57 councils working in collaborative teams, were awarded grants of up to £100,000 to explore the use of a variety of digital technologies, from Amazon Alexa style virtual assistants to support people living in care, to the use of data analytics to improve education plans for children with special needs.

These projects are still in their infancy, but there are councils who are further along with artificial intelligence, and have already learned lessons and had measurable successes. For instance, Milton Keynes Council have developed a virtual assistant (or chatbot) to help respond to planning-related queries. Although still at the ‘beta’ stage, trials have shown that the virtual assistant is better able to validate major applications, as these are often based on industry standards, rather than household applications, which tend to be more wide-ranging.

Chief planner, Brett Leahy, suggests that introducing AI will help planners focus more on substantive planning issues, such as community engagement, and let AI “take care of the constant flow of queries and questions”.

In Hackney, the local council has been using AI to identify families that might benefit from additional support. The ‘Early Help Predictive System’ analyses data related to (among others) debt, domestic violence, anti-social behaviour, and school attendance, to build a profile of need for families. By taking this approach, the council believes they can intervene early and prevent the need for high cost support services. Steve Liddicott, head of service for children and young people at Hackney council, reports that the new system is identifying 10 or 20 families a month that might be of future concern. As a result, early intervention measures have already been introduced.

In the US, the University of Chicago’s initiative ‘Data Science for Social Good’ has been using machine learning (a form of AI) to help a variety of social-purpose organisations. This has included helping the City of Rotterdam to understand their rooftop usage – a key step in their goal to address challenges with water storage, green spaces and energy generation. In addition, they’ve also helped the City of Memphis to map properties in need of repair, enabling the city to create more effective economic development initiatives.

Yet, like most new technologies, there has been some resistance to AI. In December 2017, plans by Ofsted to use machine learning tools to identify poorly performing schools were heavily criticised by the National Association of Head Teachers. In their view, Ofsted should move away from a data-led approach to inspection and argued that it was important that the “whole process is transparent and that schools can understand and learn from any assessment.”

Further, hyperbole-filled media reports have led to a general unease that introducing AI could lead to a reduction in the workforce. For example, PwC’s 2018 ‘UK Economic Outlook’ suggests that 18% of public administration jobs could be lost over the next two decades. Although its likely many jobs will be automated, no one really knows how the job market will respond to greater AI, and whether the creation of new jobs will outnumber those lost.

Should local government investment in AI?

In the next few years, it’s important that local government not only considers the clear benefits of AI, but also addresses the public concerns. Many citizens will be in favour of seeing their taxes go further and improvements in local services – but not if this infringes on their privacy or reduces transparency. Pilot projects, therefore, which provide the opportunity to test the latest technologies, work through common concerns, and raise awareness among the public, are the best starting point for local councils looking to move forward with this potentially transformative technology.


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New Idox research – Data Mining to inform public policy

By Susan Lomax, Data Scientist, Knowledge Transfer Partnership placement

The latest “new” thing in the world of data mining is using “Big Data” to inform public policy. Using data mining methods, we can aid evidence-based decision making by learning what the data can tell us and using this to write or implement policy. Idox are now exploring these methods to look at opportunities for our public policy and research members.

Investigation indicates that using data in this way is in its infancy, where data mining methods are in the process of being used, but so far, very little is completed. Published examples include, London Borough of Newham’s property data, which has been combined with numerous other datasets and mined to examine change in property tenure in order to support, amongst other things, their housing management services. The University College London mined Oyster Card data in order to minimize cost for travellers using public transport and to encourage public transport use. The first stage of the research will be exploring what can be done and what would be useful to members.

As a new member of the Idox staff, I am on a scheme known as Knowledge Transfer Partnership (KTP), which helps companies engage in this type of research and development. The scheme is celebrating its 40th Anniversary this year, having first been formed in 1975 as the Teaching Company Scheme. The KTP program is funded by 17 public sector organisations and led by Innovate UK, formally the Technology Strategy Board. The aim is to support UK businesses wanting to improve their competitiveness, productivity and performance by accessing the knowledge and expertise available within UK Universities and Colleges.

Traditionally taking place in engineering and manufacturing industries, they have now branched out into ICT, looking at data analysis, and creative industries such as design, fashion, music and video games businesses. There are currently 800 partnerships across the UK.

Our research partnership includes an academic institution and The University of Salford, is on hand to provide support and guidance. It has an outstanding record with regard to innovation, enterprise and skills. The Informatics Research Centre builds on history, success and achievements of research in Computer Science and Information Systems over the last 30 years.

Data mining is a process to discover patterns in large datasets. Its roots are in disciplines such as artificial intelligence, machine learning, statistics and database systems. Its overall goal is to extract information from data and make this understandable, so that it can be used to make decisions. A popular book “Data mining: Practical machine learning tools and techniques with Java” has information about the most common data mining methods.

The three main data mining methods we will be trying are association rules, classification and clustering and we will be exploring these in the research.

  • Association rule learning searches for relationships between variables (or attributes) in the dataset. A most popular example is a supermarket finding out which products their customers buy together and use this information for marketing purposes. This is also known as market basket analysis.
  • Classification is when a dataset has examples grouped into known classes; the task is to assign a new example to one of these known classes. A well-known algorithm performing this task is the Decision Tree algorithm C4.5.
  • Clustering performs a similar task to classification but with clustering we don’t have an assigned ‘class’. A technique known as k-nearest Neighbour is a popular method. Other main tasks are regression, summarization and anomaly detection.

Although the research is explorative at the moment, I hope to keep you updated with our progress throughout the project. If you have any thoughts or want to find out more, please get in touch.


The Idox Information Service can give you access to a wealth of further information on data and knowledge management. To find out more on how to become a member, contact us.

Further recent reading*

Classification

Association rule

Measuring transit use variability with smart-card data

Digital councils

*Some resources may only be available to members of the Idox Information Service