Updated Ai Government Guide Data Literate Leaders

_drafts/ai-government-guide-data-literate-leaders.markdown

@@ -27,11 +27,11 @@ As I’ll go on to explain, while the technologies may be new, your best next st
 
 ## 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. 
+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.
 
 A 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 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 all of those websites, regardless of the site on which the user posed their question.
 
@@ -53,7 +53,7 @@ It’s important to gain a good understanding of the capabilities of different A
 
 Within the public sector at least, it’s likely that most use cases at this stage will begin as trials and require some experimentation to test your hypotheses. This experimentation comes with a degree of failure and learning baked into it. The examples above show the benefits of having some success with using AI to solve issues, but experimenting in this way takes time and also comes with a cost and a high likelihood of at least some failure and/or rework.
 
-We need to encourage informed experimentation and give people the skills to do this well. Experimentation is not always encouraged in the public sector. The systems that underpin government aren’t designed to handle rapid learning as it can look very much like failure. However, [studies](https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever) have shown that only organisations that encourage experimentation and innovation (through a balance of talent, technology, strategy and culture) achieve positive outcomes through digital transformation. A culture of curiosity (as my colleague Matt Phillips describes it [in his recent white paper](https://www.scottlogic.com/white-paper-value-generative-ai)) and experimentation is a big part of a data-literate culture.
+We need to encourage informed experimentation and give people the skills to do this well. Experimentation is not always encouraged in the public sector. The systems that underpin government aren’t designed to handle rapid learning as it can look very much like failure. However, [studies](https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever) have shown that only organisations that encourage experimentation and innovation (through a balance of talent, technology, strategy and culture) achieve positive outcomes through digital transformation. A culture of curiosity (as my colleague Matt Phillips describes it [in his recent white paper](https://www.scottlogic.com/white-paper-value-generative-ai)) and experimentation is a big part of fostering data literacy.
 
 ### Bring to bear your data literacy
 
@@ -79,6 +79,6 @@ Initiatives like [One Big Thing](https://moderncivilservice.blog.gov.uk/2023/07/
 
 ### 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 in order to 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's post that I linked to earlier, “The more I use it and work on strategies for deploying it, the more I think: this requires data-plumbing-fixing activities!” 
+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 in order to 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's post that I linked to earlier, “The more I use it and work on strategies for deploying it, the more I think: this requires data-plumbing-fixing activities!”
 
 I totally agree, Pete.