Twenty-Sixth Report - The Department for Work and Pensions’ Accounts 2021–22 – Fraud and error in the benefits system

As part of our previous examination of the Department’s 2019–20 Accounts, we recommended that the Department should monitor and report any discrimination or bias caused by using artificial intelligence and machine learning on different claimant groups.41 In its response to … Read more As part of our previous examination of the Department’s 2019–20 Accounts, we recommended that the Department should monitor and report any discrimination or bias caused by using artificial intelligence and machine learning on different claimant groups.41 In its response to our report, the Department agreed with our recommendation and told us that it had a Data Science Ethics Framework for machine learning that ensures it considers bias and discrimination in the design of predictive models. It also told us that it was working with legal experts to ensure that the ethical position of its products have been properly considered ahead of any wider automation, and committed to providing an update on how it is using data to tackle loss as part of its Annual Report and Accounts.42 The Department told us that wider use of data analytics methods such as machine learning will be a key part of its future efforts to prevent fraud from entering the benefit system. During 2021–22 the Department trialled a machine learning model that uses information from past fraud cases to build a prediction of which claims are likely to be fraudulent in the future.43 Show less

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We asked the Department about the degree of transparency that the public can expect to have about how its data analytics and machine learning tools will work. The Department told us that this was a “challenging balance”. It cautioned that … Read more We asked the Department about the degree of transparency that the public can expect to have about how its data analytics and machine learning tools will work. The Department told us that this was a “challenging balance”. It cautioned that it did not want to make public details about its techniques or what characteristics or behaviours it was looking for that could tip off fraudsters about how the Department identifies fraud. But it agreed that it should look at publishing what it can to give the public confidence in the fairness of these methods in future.44 The Department has previously acknowledged the 39 Qq 41–43 40 Q 41; DWP ARA 2021–22, pages 70, 230 41 Committee of Public Accounts, Department for Work and Pensions Accounts 2019–20, Twenty-Sixth Report of Session 2019–21, HC 681, 18 November 2020 42 HM Treasury, Treasury Minutes – Government responses to the Committee of Public Accounts on the Twenty- Fifth to the Twenty-Ninth reports from Session 2019–21, CP 376, February 2021 43 Q 68; DWP ARA 2021–22, page 229 44 Q 60 The Department for Work and Pensions’ Accounts 2021–22 – Fraud and error in the benefits system 17 potential for data analytics and machine learning to generate biased outcomes that could have an unfair impact on certain groups of claimants. For instance, if a machine learning model were to disproportionately identify a group with a protected characteristic as more likely to commit fraud, the model could inadvertently obstruct fair access to benefits.45 Show less

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On the basis of a Report by the Comptroller and Auditor General (C&AG), we took evidence from the Department for Work & Pensions (the Department) on its 2021–22 Annual Report and Accounts and the level of fraud and error in the benefits it administers.1

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The Department told us it had included numbers in its Annual Report and Accounts for the savings from its efforts to reduce fraud and error. The Department’s 2021–22 Annual Report included an estimate for the impact of its activities to reduce fraud and error of £2 billion for 2021–22, but …

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In our January 2022 report, we warned that, given the nature of underpayments identified, there was a risk that similar, unidentified errors existed in the State Pension caseload.60 In 2021–22 the Department identified several new groups of pensioners potentially affected by underpayment, the most significant relating to Home Responsibilities Protection …

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