Universal Credit claimants could miss out on payments under a new fraud detection process being trialled by the government, warned campaigners.
The Department of Work and Pensions (DWP) is testing the use of a machine learning algorithm to detect cases of benefits fraud. The algorithm analyses historical data to predict which cases are likely to be fraudulent in the future, without being explicitly programmed by a human.
But while campaigners support the crackdown on fraud, there are concerns that using a machine-learning algorithm to flag potentially fraudulent benefits claims could mean some people unfairly miss their payments.
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Ariane Adam, Legal Director of the Public Law Project said: “Despite many requests under the Freedom of Information Act, the DWP has previously refused to provide details about its use of automation to assess Universal Credit applications. This lack of transparency is very problematic.
“Without transparency there can be no evaluation, and without evaluation it is not possible to tell if a system works reliably, lawfully or fairly. Discriminatory impact is a massive risk. Using algorithms fed by historic big data to make decisions on welfare benefit claims carries a danger of unfairly penalising and discriminating against marginalised or vulnerable groups.
"This could be, for example, because the historic data may be inaccurate or because it may be tainted by human bias that will be exacerbated by the machine. In the midst cost-of-living crisis, people could have benefits stopped before they are even paid out because a computer algorithm said ‘no’.”
The DWP said it "is aware of the potential for such a model to generate biased outcomes that could have an adverse impact on certain claimants". The Government department also said it "intends to continuously monitor the model’s outcomes for inaccuracies, unintended bias and unfairness.".
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