Tag Archives: transforming local services

How AI is transforming local government

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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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From data to intelligence and improvement – what cutting edge councils are doing in the UK

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By Steven McGinty

Data has the potential to revolutionise the delivery of local services. Just like the private sector – where organisations such as Amazon and Facebook have leveraged user data – local councils have the opportunity to reap significant benefits from analysing their vast silos of data. Improving efficiencies, increasing levels of transparency, and providing services which better meet people’s needs, are just some of the potential benefits.

Although many councils are still at the early stages of utilising their data, some are innovating and introducing successful data initiatives.

Wise Councils

In November 2016, the charity NESTA published a report highlighting the most ‘pioneering’ uses of data in local government. The report emphasised that most local services would benefit from data analysis and that a ‘problem-oriented’ approach is required to generate insights that have an impact on services. The case studies included:

Kent County Council

Kent County Council (KCC), alongside Kent’s seven Clinical Commissioning Groups (CCGs), have created the Kent Integrated Dataset (KID) – one of the largest health and care databases in the UK, covering the records of 1.5 million people. The core requirement of the dataset was to link data from multiple sources to a particular individual, i.e. that information held about a person in hospital, should also be linked to records held by other public bodies such as GPs or the police.

This integrated dataset has enabled the council to run sophisticated data analysis, helping them to evaluate the effectiveness of services and to inform decisions on where to locate services. For example, Kent’s Public Health team investigated the impact of home safety visits by Kent Fire and Rescue Service (KFRS) on attendances at accident and emergency services (A&E). The data suggested that home safety visits did not have a significant impact on an individual’s attendance at A&E.

Leeds City Council

Leeds City Council have focused their efforts on supporting open innovation – the concept that good ideas may come from outside an organisation. This involved the initiatives:

  • Data Mill North (DMN) – this collaborative project between the city council and private sector is the city’s open data portal (growing from 50 datasets in 2014 to over 300 data sets, in over 40 different organisations). To encourage a culture change, Leeds City Council introduced an ‘open by default’ policy in November 2015, requiring all employees to make data available to the public. A number of products have been developed from data published on DMN, including StreetWise.life, which provides local information online, such as hospital locations, road accidents, and incidents of crime.
  • Innovation Labs – the city has introduced a series of events that bring together local developers and ‘civic enthusiasts’ to tackle public policy problems. Leeds City Council has also provided funding, allowing some ideas to be developed into prototypes. For example, the waste innovation lab created the app, Leeds Bins, which informs residents which days their bins should be put out for collection.

Newcastle City Council

Newcastle City Council have taken a data-led approach to the redesign of their children’s services. The Family Insights Programme (FIP) used data analysis to better understand the demand and expenditure patterns in the children’s social care system. Its aim was to use this insight to support the redesign of services and to reduce the city’s high re-referrals and the number of children becoming looked-after.

The FIP uses data in three different ways:

  • Grouping families by need – The council have undertaken cluster analysis to identify common grouping of concerning behaviours, such as a child’s challenging behaviour or risk of physical abuse. When a child is referred to long term social work, senior social workers analyse the concerning behaviours of the case, and then make a referral to a specialist social work unit. Since introducing this data-led approach, social work units have been organised based on needs and concerning behaviours. This has resulted in social workers becoming specialists in supporting particular needs and behaviours, providing greater expertise in the management of cases.
  • Embedding data analysts – Each social work unit has an embedded data analyst, who works alongside social workers. Their role is to test what works, as well as providing insights into common patterns for families.
  • Enabling intelligent case management – Social workers have access to ChildSat, a tool which social workers use to help manage their cases. It also has the capability to monitor the performance of individual social work units.

Investing in data

Tom Symons, principal researcher in government innovation at Nesta, has suggested that councils need support from central government if they are to accelerate their use of data. He’s suggested that £4 million – just £1% of the Government Digital Service (GDS) budget – is spent on pilot schemes to embed data specialists into councils.

Mr Symons has also proposed that all combined authorities should develop Offices of Data Analytics, to support data analysis across counties. Over the past few months, Nesta has been working on this idea with the Greater London Authority, and a number of London boroughs, to tackle the problem of unlicensed HMOs (Houses in Multiple Occupation). Early insights highlight that data analytics could be used to show that new services would provide value for money.

Final thoughts  

After successive years of cuts, there has never been a greater need for adopting a data-led approach. Although there are undoubtedly challenges in using council data – including changing a culture where data sharing is not the norm, and data protection – the above examples highlight that overcoming these challenges is achievable, and that data analysis can be used to bring benefits to local councils.

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