However, written evidence we received from the Public Law Project raised a series of risks around DWP’s use of machine learning to tackle fraud. In particular, it noted the risk of machine learning taking on human biases when it was trained on historical data and the potential for widescale detrimental impacts on claimants if there was a system error. It said that DWP needed to be more transparent to address the issue of public confidence in its machine learning techniques.73 67 Q 46 68 Qq 46...

However, written evidence we received from the Public Law Project raised a series of risks around DWP’s use of machine learning to tackle fraud. In particular, it noted the risk of machine learning taking on human biases when it was trained on historical data and the potential for widescale detrimental impacts on claimants if there was a system error. It said that DWP needed to be more transparent to address the issue of public confidence in its machine learning techniques.73 67 Q 46 68 Qq 46, 57 69 C&AG’s Report (on accounts), paras 32, 34 70 Committee of Public Accounts, The Department for Work & Pensions Annual Report and Accounts 2022–23, Type: conclusion | Number: 37 | Response status: accepted Government response: 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 f