Source · Select Committees · Public Accounts Committee
Recommendation 36
36
Accepted
DWP uses machine learning for fraud, but with limited transparency on fairness assessment.
Recommendation
DWP is implementing machine learning techniques to help it identify fraud in benefit expenditure. It has one machine learning model in operation, for new UC advance claims, alongside several others in development.69 The previous Public Accounts Committee raised concerns about the level of transparency in DWP’s use of these tools and the potential impact on claimants who are vulnerable or from protected groups.70 In December 2023, the previous Committee recommended that DWP should consider explicitly the impact of using machine learning to identify fraud on legitimate claims being delayed or reduced, the number of people affected, and whether this was affecting specific groups of people.71 DWP has since undertaken a fairness impact assessment, but has not published the results so as to conceal from fraudsters how its model operates. It says that the results did not give any cause for concern when considered alongside the safeguards in place to protect claimants.72
Government Response Summary
The government accepts the recommendation, committing to developing a new publishable form of fairness analysis assessment for machine learning 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.