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The Guardian – DWP algorithm mistakenly flags 200,000 people for possible fraud and error

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A Big Brother Watch investigation has found that hundreds of thousands of innocent people are being put through intrusive reviews of their Housing Benefit every year.

They were flagged by an underperforming DWP algorithm – and had done nothing wrong. It also means about £4.4m has been spent on pointless checks.

When launching the algorithm the DWP justified the mass-rollout of profiling for all Housing Benefit claimants by citing data from the initiative’s pilot that found that 2 out of every 3 “high risk” cases reviewed were receiving the wrong amount of housing benefit. After three years of real world use, data obtained from the DWP by Big Brother Watch has found that only 1 in 3 people on Housing Benefit subjected to review are being paid the wrong amount.

Susannah Copson, Legal & Policy Officer at Big Brother Watch, said: “This is yet another example of DWP focusing on the prospect of algorithm-led fraud detection that seriously underperforms in practice. In reality, DWP’s overreliance on new technologies puts the rights of people who are often already disadvantaged, marginalised, and vulnerable, in the backseat.

There is a real danger that DWP repeats this pattern of bold claims and poor performance with future data-grabbing tools. It was only recently that the Government tried – and failed – to push through intrusive measures to force banks to conduct mass algorithmic monitoring of all customer accounts under the premise of tackling social security fraud and error. Although the powers failed to make it through legislative wash-up, concerns
for DWP’s relentless pursuit of privacy-invading tech remain.”

The Guardian – DWP algorithm wrongly flags 200,000 people for possible fraud and error

The post The Guardian – DWP algorithm mistakenly flags 200,000 people for possible fraud and error appeared first on Big Brother Watch.


Source: https://bigbrotherwatch.org.uk/press-coverage/the-guardian-dwp-algorithm-mistakenly-flags-200000-people-for-possible-fraud-and-error/


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