The government is blocking the full publication of an official report assessing the ways artificial intelligence could be deployed across Whitehall and the wider public sector, NS Tech can reveal.
Ministers commissioned a British consultancy firm to review potential applications of the technology in government earlier this year. The report was delivered to the Cabinet Office in April and is likely to play a key role in efforts to transform public sector processes in the coming years.
NS Tech requested a copy of the report, which was produced by Faculty, under transparency rules in August. The government complied with the request, but returned a version which is heavily redacted, on the grounds that ministers are yet to determine how it will inform policy.
The Government Digital Service and Office for AI published guidelines for using artificial intelligence in the public sector in June, before issuing further guidance on AI procurement in October. However, the government is yet to publish a comprehensive strategy for how it will invest in AI across central government, despite committing to the review in the 2018 autumn budget.
The chancellor, Sajid Javid, had been expected to outline plans for AI investment in this year’s spending review, but the only mention of the technology was in the context of a £250m investment in its adoption in healthcare.
During the review, Faculty, which was formerly known as ASI Data Science, surveyed departments and carried out workshops with the Home Office, Ministry of Justice, Ministry of Defence, HMRC, the Department for Work and Pensions and the Department for Transport. Its consultants also spoke to representatives from the Canadian, Swedish and German government, as well as academics at Stanford University.
But while the report Faculty produced reveals the processes behind its review, most of the suggested deployments have been redacted in the version shared with NS Tech by the Cabinet Office. The report does, however, disclose the most common existing applications of AI and machine learning, which include inspections of schools, farms and borders, fraud detection in benefits claims and analysing satellite data for international development work.
The report also reveals reasons why uptake of AI across government has been hindered so far. “The lack of a widely-available and consistently-used modern data architecture means that AI model build and testing is time-consuming and labour-intensive and largely unsupported for production, and data assets cannot be joined,” the report states.
The consultants describe cross-department data sharing practice as “antediluvian”, claiming they take “six months to negotiate but not providing any transparency or accountability in how they are used”. The report adds: “To address these data governance issues, a more consistent and rigorous set of operational responsibilities in relation to data could be applied to publicly funded IT systems within particular departments and discharged by the relevant departmental leader (e.g. the Chief Information Officer).” Low rates of employee retention are also cited as a barrier to adoption.
The report concludes: “Machine learning and artificial intelligence are in the process of disrupting/transforming almost every sector of the economy […] However, there are barriers to adoption – some general, some specific to the public sector.”
“The Cross Government AI Review has been an extremely useful exercise to understand the current state of departmental capability/readiness/appetite, and to identify the most promising use cases for transformative projects.”