Source · Select Committees · Public Accounts Committee
Recommendation 31
31
Rejected
Algorithmic bias in DWP systems acknowledged, but evidence of unfair impacts remains inconclusive.
Conclusion
We challenged DWP to explain how it would address the risk that legitimate benefit claims are unfairly delayed or reduced as a result of an algorithms targeting innocent behaviour, such as frequent changes of circumstances. DWP acknowledged that some level of algorithmic bias is to be expected because of how benefit payments work, for example Universal Credit payments are higher for people aged over 25, so older claimants are more likely to be flagged because fraudsters will tend to claim to be older. It asserted that while there “clearly is a hypothetical risk” of unfair impacts on claimants, that there is no evidence of that risk manifesting now. It explained that it is performing analysis regularly to identify bias in the outputs of its algorithms.72 But the NAO has reported that so far this analysis has been largely inconclusive because of limitations in the available data about claimants.73 DWP told us it did not want to provide any further detail on how it will prevent unfair impacts to avoid tipping off potential fraudsters.74
Government Response Summary
The government rejects detailing specific metrics for publication on data analytics' impact, citing the need to avoid compromising fraud detection. However, it reaffirms its commitment to reporting annually on the impact of data analytics on protected groups and vulnerable claimants, with the first assessment in its 2023-24 Annual Report and Accounts.
Government Response
Rejected
HM Government
Rejected
6.1 The government disagrees with the Committee’s recommendation. 6.2 The department is committed to reporting annually to Parliament on its assessment of the impact of data analytics on protected groups and vulnerable claimants with the first assessment in the department’s 2023-24 Annual Report and Accounts. In future years the department will iterate the annual assessments to include impacts on customer service. 6.3 While the department is committed to providing information as set out, it must not compromise its ability to tackle fraud and error by revealing details about its models that could be exploited. On that basis, the department disagrees with the Committee’s recommendation detailing specific metrics for publication.