Description: A Department for Work and Pensions (DWP) algorithm wrongly flagged over 200,000 UK housing benefit claims as high risk, resulting in unnecessary investigations. Two-thirds of these flagged claims were legitimate, causing wasted public funds and stress for claimants. Despite initial success in a pilot, the algorithm's real-world performance fell short. This incident highlights the risks of overreliance on automated systems in welfare administration.
Entities
View all entitiesAlleged: Department for Work and Pensions (DWP) developed and deployed an AI system, which harmed UK general public and UK housing benefit claimants.
Incident Stats
Incident ID
738
Report Count
1
Incident Date
2024-06-23
Editors
Daniel Atherton
Applied Taxonomies
Incident Reports
Reports Timeline
![DWP algorithm wrongly flags 200,000 people for possible fraud and error](https://res.cloudinary.com/pai/image/upload/f_auto/q_auto/c_fill,h_480/v1/reports/i.guim.co.uk/img/media/15bb39e83ef7a186b48f9bae59b2c2bef674aa4d/0_131_5460_3276/master/5460.jpg?width=1200&height=630&quality=85&auto=format&fit=crop&overlay-align=bottom%2Cleft&overlay-width=100p&overlay-base64=L2ltZy9zdGF0aWMvb3ZlcmxheXMvdGctZGVmYXVsdC5wbmc&enable=upscale&s=c37343c2b4c73b0238894236093f8375)
theguardian.com · 2024
- View the original report at its source
- View the report at the Internet Archive
More than 200,000 people have wrongly faced investigation for housing benefit fraud and error after the performance of a government algorithm fell far short of expectations, the Guardian can reveal.
Two-thirds of claims flagged as potential…
Variants
A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.