This study examines 51 Freedom of Information requests to reveal how the UK’s Department for Work and Pensions uses opaque data-driven fraud detection systems in welfare, highlighting the limited transparency, the role of journalists and charities in uncovering details, and inconsistencies in the department’s disclosures.
For the past several years, the UK’s Department of Work and Pensions’ (DWP) has quietly adopted automated data analytics and machine learning to detect fraud and error in social security applications. The department has disclosed very little about these initiatives to the public beyond cursory mentions in year-end accounting reports and answers to parliamentary questions, despite growing interest from politicians, grassroots groups and citizens in how they affect welfare applicants. One way to gain a larger picture of the DWP’s practices is to look at the results of Freedom of Information (FOI) Requests people have submitted over the years to ask about DWP’s data-intensive systems for fraud and error detection. This study analyses 51 such requests from 2018 up to the present; these present a chronology of some of the tools in place, the internal processes around them, how they are evaluated and how they relate to staff roles. The exercise yielded a collection of 44 internal documents that DWP released to requesters. We also gain insight into how charities and journalists deploy FOIA as a democratic tool – how they react to the publication of government reports, use FOIA procedures to obtain more details, and release these findings to the public in articles and reports in some cases, which can spur more requests by others. Finally, the FOI request dataset reveals inconsistencies across the DWP’s responses to requests: those who accept the DWP’s default refusals do not see the same information as those willing to pursue the matter through complaints to the Information Commissioner's Office.
Alli Spring, Morgan Currie
L'Università degli Studi di Urbino Carlo Bo annuncia il lancio di GoUniurb, il nuovo progetto strategico di accoglienza e orientamento dedicato alle studentesse e agli studenti internazionali e con bisogni educativi speciali.
Course designed and taught by Maria Milosh at Bard College and Kronika in January - May 2026.
We bring together the university’s world-class, interdisciplinary, socio-technical expertise in web science, data science and artificial intelligence to leverage the unique role of online technologies in tackling global challenges.
The NYC Open Data Lab is a public-facing initiative that equips students to access, analyze, and publish civic data through reproducible workflows and open-source tools.
