Source · Select Committees · Public Accounts Committee
Recommendation 30
30
Rejected
Concerns persist regarding machine learning's potential unfairness and bias for vulnerable claimants.
Conclusion
We received written evidence from the Child Poverty Action Group and from the Public Law Project expressing concern about the potential unfairness of machine learning, particularly with regard to vulnerable claimants and people with protected characteristics.70 We asked DWP whether it understood the concerns of people who have warned of unintentional bias in its use of machine learning. DWP assured us it shared these concerns and that is why a human always makes the final decision on whether to make a benefit payment.71
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.