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The UK government has developed a manual to help public sector decision-makers understand and deploy artificial intelligence (AI) technologies.
The guide, developed by the Office for Artificial Intelligence (OAI) and the Government Digital Service (GDS), touches on a variety of topics related to AI, from the evaluation stage through to post-implementation.
“A number of public sector organisations are already successfully using AI for tasks ranging from fraud detection to answering customer queries,” the report said.
“The potential uses for AI in the public sector are significant, but have to be balanced with ethical, fairness and safety considerations,” it added.
There is a chapter aimed at organisation and delivery leads around basic aspects of AI. It contains various case studies, such as the story of how the Driver and Vehicle Standards is using AI to improve MOT testing, and how National Grid is using AI to improve the maintenance of the national energy infrastructure.
The report also suggests ways in which decision makers can assess whether AI is the right option. It also aims to get decision makers to consider whether the technology they are looking to adopt is truly relevant and if it can be adapted at a later stage.
Aspects related to planning and preparing for systems implementation are also included in the report, in a chapter aimed at technology buyers. It includes considerations around how to handle alpha and beta stages of AI projects, as well as discovery, preparation of data and security.
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Elsewhere in the guidance, topics related to management of AI systems roll-outs is also covered; decisions around how projects run, team building and the actual implementation, as well as risk, governance, testing and quality assurance.
The manual also provides advice on ethics and safety and contains a summary of the Alan Turing Institute’s detailed guidance. This contains considerations around areas such as how to establish a culture of responsible innovation and the need to consider fairness, accountability, sustainability and transparency in the design and use of AI systems.