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Millions of people across the UK are being profiled by biased algorithms every year by the Department for Work and Pensions (DWP).
Our new report ‘Suspicion By Design’ is a deep dive into the DWP's growing use of AI, automation and algorithms to profile and flag people receiving benefits.
It examines the bias found in the DWP's machine learning models and the privacy risks posed by the government's suite of 'high-tech' anti-fraud tools. It also lays out the key questions around data protection and potential algorithmic discrimination that the government refuses to answer.
Dive into our new report's key findings to learn more about these shadowy profiling algorithms. Sunlight, not secrecy, is needed to see into the digital welfare system and expose the biases within.
Our new report ‘Suspicion by Design: What we know about the DWP's algorithmic black box and what it tries to hide’ details the massive expansion of AI, algorithm supported decision-making and biases in the benefits system. Read the report.
Around one million people were profiled by the Universal Credit Advances machine learning model last year, which is riddled with algorithmic bias.
The DWP went to court to try to keep details on the model’s data risks secret.
New machine learning models in development by the DWP contain significant potential for discrimination.
The DWP refuses to meet its obligations to publish details about its algorithms.
As Universal Credit moves the centre of the welfare state towards Whitehall, the report builds on our 9-month-long investigation “Poverty Panopticon: The Hidden Algorithms Shaping Britain’s Welfare State”.
This report revealed how councils across the UK are conducting mass profiling of welfare and social care recipients and “citizen scoring” to predict fraud, rent non-payments and major life events.
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Have you been affected by DWP algorithms? Email us at info@bigbrotherwatch.org.uk and let us know.
Find out if your local council uses people's personal data to decide if they are a ‘fraud risk’ using our interactive map.
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