Updated Ai Government Balancing Productivity Accountability

_drafts/ai-government-balancing-productivity-accountability.markdown

@@ -44,7 +44,7 @@ As I said earlier, it’s not all about speed. Colin says in his blog, “These
 
 Take the policy-making process. As a roundtable participant explained, this process has remained largely unchanged over the decades, but AI could play a transformative role. With its ability to digest and analyse vast amounts of data, AI could radically improve the process of gathering and identifying gaps in the evidence on which policy is based. In addition, AI’s ability to extrapolate future trends from historical data could transform impact analysis, helping predict the potential outcomes of new policies – and this could be a dynamic process. As one of the roundtable’s participants suggested, it would be possible to run rapid experiments in the early stages of policy-making to demonstrate the causality between interventions and their impacts. In this way, you could provide confidence in the metrics with which the policy’s effectiveness would be measured.
 
-The extraordinary power of AI opens up new use cases that would be inconceivable for humans in terms of the time and resources required. For example, medical prescription errors result in a significant number of deaths a year. The government’s Incubator for AI sought expressions of interest for a pilot project to trial using pharmacy data to flag suboptimal prescription profiles and concerning cases. In the wider health sector, there are already well-known use cases that exploit AI’s superhuman capabilities – for example, the use of image analysis and pattern recognition in early diagnosis of conditions such as cancer and heart disease.
+The extraordinary power of AI opens up new use cases that would be inconceivable for humans in terms of the time and resources required. For example, medical prescription errors result in a significant number of deaths a year. The government’s Incubator for AI sought [expressions of interest for a pilot project](https://ai.gov.uk/files/Incubator%20for%20AI%20-%20Expressions%20of%20Interest%20for%20Pharmacy%20Data%20project.pdf) to trial using pharmacy data to flag suboptimal prescription profiles and concerning cases. In the wider health sector, there are already well-known use cases that exploit AI’s superhuman capabilities – for example, the use of image analysis and pattern recognition in early diagnosis of conditions such as cancer and heart disease.
 
 ## The importance of explainability
 
@@ -56,7 +56,7 @@ However, it’s a complex area and research is still underway into explainabilit
 
 As important as explainability will be in the harnessing of AI by the public sector, it isn’t a silver bullet. Particularly while techniques are still maturing, explainability will depend on people who are trained in interpreting the explanations of a model’s outputs, based on sufficient knowledge of the context and how the model works.
 
-Beyond the hype, human involvement remains vital
+## Beyond the hype, human involvement remains vital
 
 The discussions at the IfG roundtables returned time and again to the conclusion that human involvement in most AI-assisted processes will remain vital for the foreseeable future. Transparency and accountability are intrinsic to the running of government services and, as things stand, AI can only go so far in supporting these. Even where a use case may be technically feasible and apparently straightforward – e.g., automating replies to correspondence – there’s a larger context that the government must consider; as a roundtable participant stated, automating anything changes its meaning. A recipient of a fully AI-generated piece of correspondence may feel a greater sense of disconnection from government as a result.