Source · Select Committees · Public Accounts Committee
Recommendation 38
38
Accepted
DWP commits to further assurance on fairness of machine learning techniques for benefits.
Recommendation
We stressed that we would like to be satisfied that DWP’s use of machine learning is fair and consistent. DWP highlighted its main safeguard, which is that it would never stop benefit payments to a customer due to an AI tool. It said that what AI helped it to do was to direct the work of its staff to investigate more effectively. DWP also committed to work with us and the National Audit Office to find a way to provide the necessary further assurance as to the fairness of its machine learning techniques. It reiterated this commitment in subsequent correspondence.74 74 Qq 52-54; Letter from DWP to the Public Accounts Committee dated 23 December 2024 18
Government Response Summary
The government accepts the recommendation, reiterating its commitment to provide assurance on the fairness of machine learning by developing a new publishable fairness analysis assessment by Summer 2025, with improved governance and independent oversight.
Government Response
Accepted
HM Government
Accepted
7.1 The government agrees with the Committee’s recommendation. Target implementation date: Summer 2025 7.2 The department will support an in-confidence session with the Committee and departmental officials to set out the 2024 fairness analysis assessment. 7.3 Moving forward, the department has made a commitment at the Work and Pensions Select Committee on 29 January 2025 (Q18) to develop a new publishable form of fairness analysis assessment. 7.4 Across the public sector, this department is at the forefront of producing fairness analysis such as these. There is no set government standard for fairness analysis, nor any best practice examples that the department could identify, therefore it has had to adopt a test and learn approach to fairness analysis. The fairness analysis method has been endorsed by statistical experts. 7.5 At every stage of machine learning development, the department ensures checks and balances are in place and have safeguards to minimise the risk of unfair treatment or detrimental impact on legitimate claimants. 7.6 The department has reflected on how it can assure Parliament and the public of its processes and have committed to a new approach to fairness analysis of machine learning models designed to tackle fraud. To introduce additional independence and scrutiny into the process, the department will: • Improve upon existing governance around the assessment of the fairness analysis to determine whether each model is effective and remains reasonable and proportionate. • Assure both the statistical analysis and the assessment will be overseen by a team independent of those running the machine learning models, with reference back to the appropriate internal governance board when issues are discovered that require action. • Draft fairness analysis assessments in such a way that they can be published unredacted, setting out the rationale for why the department assesses the models to be reasonable and proportionate but without divulging the detail of its fraud and error controls that would put the security and integrity of the social security system at risk from fraud. 7.7 The aim of this new approach is to provide the Committee, Work and Pensions Select Committee and the wider public with assurance on the department’s fairness analysis.