Recommendation 6

DWP has not yet done enough to understand the impact of machine learning on customers and provide them with confidence that it will not result in unfair treatment. DWP is expanding its use of advanced data analytics to tackle fraud. This includes machine learning algorithms to flag potentially fraudulent benefit claims, so the system learns and adapts without following explicit instructions. DWP says it is in an early stage of implementing these tools, but has already piloted them to tackle fraud in Universal Credit advances. There are legitimate concerns about the level of transparency around DWP’s use of these tools and the potential impact on claimants who are vulnerable or from protected groups. DWP has not made it clear to the public how many of the millions of Universal Credit advances claims have been subject to review by an algorithm. Nor has it yet made any assessment of the impact of data analytics on protected groups and vulnerable claimants; though we acknowledge it has recently committed to provide such an assessment in next year’s annual report. Although DWP has internal governance arrangements over its use of machine learning and performs some ongoing analysis of bias, the results so far have been largely inconclusive. 8 The Department for Work & Pensions Annual Report and Accounts 2022–23 Recommendation 6: DWP should, as part of the assessment in its annual report, consider explicitly the impact of data analytics and machine learning on legitimate claims being delayed or reduced, the number of people affected, and whether this is affecting specific groups of people. The Department for Work & Pensions Annual Report and Accounts 2022–23 9 1 The scale of fraud and error in the benefit system