Recommendations & Conclusions
5 items
1
Conclusion
Twenty-Sixth Report - The Department fo…
Acknowledged
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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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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Government response AI summary
The government agrees and says it has committed to the target implementation date and will keep the Committee up to date on the progress of this via the existing TM25 recommendation.
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HM Treasury
21
Conclusion
Twenty-Sixth Report - The Department fo…
Acknowledged
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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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 notes that this estimate is built up from a wide range of management information and modelling assumptions. The NAO reported that this savings estimate is experimental and requires further development before it can provide an appropriate framework for reporting the amount saved for the taxpayer and the cost- effectiveness of the Department’s activities. The Department told us that it will continue to develop “better, stronger metrics” to demonstrate the effectiveness of its counter-fraud activities.40 Transparency around the future use of data analytics in fraud prevention
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Government response AI summary
The government published an estimate of £2 billion in savings from counter fraud efforts in the 2021-22 ARA and is committed to working with the NAO to ensure agreement on the framework for the 2022-23 ARA.
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HM Treasury
22
Recommendation
Twenty-Sixth Report - The Department fo…
Acknowledged
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 …
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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
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Government response AI summary
The government is committed to ensuring assurances and governance for data and analytics functions and is considering the best method on reporting this information to Parliament annually.
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HM Treasury
23
Recommendation
Twenty-Sixth Report - The Department fo…
Acknowledged
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 …
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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 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
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Government response AI summary
The government is committed to ensuring assurances and governance for data and analytics functions and is considering the best method on reporting this information to Parliament annually.
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HM Treasury
29
Conclusion
Twenty-Sixth Report - The Department fo…
Acknowledged
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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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 (HRP). For people reaching State Pension age before 6 April 2010, HRP reduced the number of qualifying years of National Insurance contributions needed for a basic State Pension where someone stayed at home to care for children for whom they received Child Benefit or a person who was sick or disabled. The Department identified that HRP was missing from the National Insurance records of claimants who should have been entitled to it. National Insurance records are held and maintained by HMRC, which was unaware of the issue until the Department flagged it.61 We asked the Department what was its best estimate of the scale of the underpayments as a result of HRP. The Department explained that it did not yet know the extent of the underpayment in relation to HRP because it was dependent on HMRC to help identify affected cases before it could estimate the total value of any underpayment and reimburse pensioners.62
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Government response AI summary
The government agrees with the recommendation to work with HMRC to evaluate the extent of the HRP underpayment and provide a timetable for completion. Investigation is underway, but estimates of affected people and costs are not yet available.
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HM Treasury