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Updated Ai Government Guide Data Literate Leaders
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pauldykes authored and Siteleaf committed Oct 23, 2023
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## AI use in Government

The UK Government has already begun to make careful use of these technologies over the last few years. When I worked at Government Digital Service (GDS) there was a [great example](https://www.gov.uk/government/case-studies/how-gds-used-machine-learning-to-make-govuk-more-accessible) on [GOV.UK](https://www.gov.uk/) which supported one of the core goals at the time – making content more accessible to users. At the time there were thousands of untagged pages of content that we estimated would take years for civil servants across government to review and tag. The project team built a supervised machine learning model that could recognise patterns in the pages, read the content, and suggest suitable tags. The final model was able to tag 96% of the content in about six months, making huge amounts of content more accessible to citizens. Another machine learning example from local government is that of [Swindon Council](https://digileaders.substack.com/p/how-one-council-used-machine-learning) which, with a tiny team of three people and seed funding of just £100K, successfully introduced AI to a number of internal business challenges – one notable example being the use of machine learning to cut their document translation costs by 99.6% and reduce the turnaround time from weeks to minutes.
The UK Government has already begun to make careful use of these technologies over the last few years. When I worked at Government Digital Service (GDS) there was a [great example](https://www.gov.uk/government/case-studies/how-gds-used-machine-learning-to-make-govuk-more-accessible) on [GOV.UK](https://www.gov.uk/) which supported one of the core goals at the time – making content more accessible to users. At the time there were thousands of untagged pages of content that we estimated would take years for civil servants across government to review and tag. The project team built a supervised machine learning model that could recognise patterns in the pages, read the content, and suggest suitable tags. The final model was able to tag 96% of the content in about six months, making huge amounts of content more accessible to citizens.

Another machine learning example from local government is that of [Swindon Council](https://digileaders.substack.com/p/how-one-council-used-machine-learning) which, with a tiny team of three people and seed funding of just £100K, successfully introduced AI to a number of internal business challenges – one notable example being the use of machine learning to cut their document translation costs by 99.6% and reduce the turnaround time from weeks to minutes.

Since 2015, the US Citizenship and Immigration Services (USCIS) has been using [Emma](https://www.uscis.gov/tools/meet-emma-our-virtual-assistant), their virtual assistant, to reduce the burden on call centres which receive a large number of calls for general information on the immigration process. In 2020, Emma responded successfully to [35 million enquiries from more than 11 million users](https://www.acus.gov/sites/default/files/documents/Automated%20Legal%20Guidance%20%285.26.22%29%20-%20final-1_0.pdf). By 2021, Emma’s success rate was 93% in English and 90% in Spanish. As another example of supervised machine learning, Emma trains with adjudicators and case managers, as well as through interactions with the public. Since 2014, Singapore has had a similar concept called ‘[Ask Jamie](https://www.tech.gov.sg/products-and-services/ask-jamie/)’ which they have implemented across 70 government agency websites. It supports the Singapore government’s no-wrong-door approach by surfacing answers from across those 70 websites, regardless of the site on which the user has posed their question.

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### Fix the plumbing

So far I’ve focused a lot on the skills and qualities you can bring to bear in getting ready to harness AI. But if you’re looking for a hands-on, practical next step so that you can get started and be ready, beginning with your data is a rock-solid idea. This is a hyped technology that makes an incredibly strong case for improving your data architecture, quality and stewardship. To quote my colleague, Pete Chamberlin again, “[The more I use it and work on strategies for deploying it, the more I think: this requires data-plumbing-fixing activities!](https://www.linkedin.com/posts/peterchamberlin_the-trouble-with-innovation-activity-7112164418049687554-CWbz?utm_source=share&utm_medium=member_desktop)” I totally agree, Pete.
So far I’ve focused a lot on the skills and qualities you can bring to bear in getting ready to harness AI. But if you’re looking for a hands-on, practical next step so that you can get started and be ready, beginning with your data is a rock-solid idea. This is a hyped technology that makes an incredibly strong case for improving your data architecture, quality and stewardship. To quote Pete Chamberlin again, “[The more I use it and work on strategies for deploying it, the more I think: this requires data-plumbing-fixing activities!](https://www.linkedin.com/posts/peterchamberlin_the-trouble-with-innovation-activity-7112164418049687554-CWbz?utm_source=share&utm_medium=member_desktop)” I totally agree, Pete.

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