A “rushed implementation” of artificial intelligence in Department for Work and Pensions ( DWP ) sickness and disability benefits could “harm” people, experts have said. Experts are concerned that the “historical data used to train AI may perpetuate bias and discrimination” within the benefits system.
An investigation by the Guardian found that the machine-learning programme used by the DWP to detect universal credit fraud incorrectly selected people from some groups more than others. It showed that there was bias according to people’s age, disability, marital status and nationality.
Shelley Hopkinson, head of policy and influencing at Turn2us, said: “AI has the potential to improve speed and consistency in the social security system, but errors from rushed implementation could cause serious harm to people relying on support. We are concerned that the historical data used to train AI may perpetuate bias and discrimination against marginalised groups.”
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Ms Hopkinson added: “AI integration should be guided by consultation and transparency so that the system meets the needs of everyone and builds trust that decisions are fair and accurate. This must come with clear accountability and safeguards to allow for challenge .
“AI must work for people, not against them, prioritising people’s lives and wellbeing in decision-making.” It comes as the UK government has unveiled a wide-ranging action plan to boost the country’s role in developing and deploying artificial intelligence.
The 50 recommendations from the report, written by the tech investor Matt Clifford, have been endorsed by Keir Starmer, who says the technology will drive “incredible change in our country”. The plan calls on the government to “rapidly pilot” AI-powered services.
Starmer says this will drive better experiences and outcomes for citizens, as well as boosting productivity – a measure of economic performance. Examples of public sector use of AI flagged by the government include helping teachers prepare for lessons and using AI-enhanced road cameras to spot potholes.