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Case studies

Investigations into government algorithms, and how they were done

  • NL · 2023

    Suspicion Machines: Rotterdam's welfare fraud risk score

    Rotterdam ranked every welfare recipient by fraud risk with a machine-learning model, and journalists did something that almost never happens: they got hold of the model itself and took it apart. They found it scored parents, young people, women and people with weaker Dutch higher, and that it was only about 50 percent more accurate than random selection. The city had already put it on hold, and later agreed the Dutch-language variables were an unacceptable proxy for migrant background.

  • SE · 2024

    Sweden's Suspicion Machine: the Social Insurance Agency's parental benefit risk model

    In Sweden the Social Insurance Agency refused almost everything for three years, and the investigation went ahead anyway by going through the agency's overseer. It showed that a risk model for parental benefit selected women more than 1.5 times as often as men, and people with a foreign background about 2.5 times as often. Amnesty International called for the system to be discontinued, and in 2025 the agency took it out of use.

  • NL · 2025

    The Limits of Ethical AI: Amsterdam's Smart Check welfare pilot

    Amsterdam did nearly everything the responsible-AI playbook asks: it opened its records, tested for bias, consulted citizens and built a model that could be explained. The journalists still found it unfair. In the live pilot the bias reversed, with Dutch applicants and women wrongly flagged more often, and the model did no better than caseworkers. The city stopped the pilot in November 2023.

  • FR · 2023

    France's Digital Inquisition: the family benefits fund's risk score

    In France, journalists used access-to-information law to get the scoring code of the national family benefits fund. The model gives every household on the benefit rolls a risk score, and the authors say it pushes scores up for single parents, low-income households and people with disabilities. The disclosure fed a court case that is still pending, and in January 2026 the fund published the source code of its current model.

  • NL · 2023

    The discriminatory fraud checks of DUO

    Reporters could not get into the Dutch student-finance agency's risk system, so they asked lawyers for their case counts instead. Of 376 objection and appeal cases the 32 lawyers had handled since 2012, 367 involved students with a migration or bicultural background. The education minister suspended algorithm-based selection in 2023, the data protection authority later found the checks unlawfully discriminatory, and the state accepted liability for the damage.

  • NL

    The Dutch childcare benefits scandal: the tax authority's risk classification model

    The Dutch tax authority scored childcare benefit applications with a self-learning risk model, and Dutch citizenship was one of its inputs. Tens of thousands of parents were wrongly treated as fraudsters, and migrant and low-income families were hit hardest. Years of reporting by Trouw and RTL Nieuws, pressure from MPs and a data protection investigation established the use of nationality in July 2020. The government resigned in January 2021, and the tax authority was fined €2.75 million. The model was one part of a wider failure.

  • NL

    SyRI: the Dutch fraud risk system struck down in court

    SyRI let Dutch government bodies combine personal data to predict who might commit benefits or tax fraud, and it was used mainly in poorer neighbourhoods. Almost everything about how it worked was secret. A coalition of civil-society groups and two writers, later joined by the FNV union, took the state to court. In February 2020 a court in The Hague ruled that the law behind it breached the right to private life, because its use could not be understood or checked. The government did not appeal.

  • DK · 2024

    Coded Injustice: Denmark's automated welfare fraud controls

    Denmark's benefits agency runs about 60 fraud models, and it refused Amnesty's requests for a joint audit and full access to code and data. With redacted documents on four models and dozens of interviews, Amnesty still raised serious discrimination concerns: one model scores 'foreign affiliation', ties to countries outside the EEA, to prioritise investigations. It is a good example of building a credible case when code and data are refused. As far as we could find, nothing has changed since.

  • UK · 2024

    The DWP model that flags Universal Credit advance requests for fraud

    A records release forced the UK's Department for Work and Pensions to admit statistically significant disparities in a fraud tool that scores Universal Credit advance requests. Its own later figures show non-UK nationals were referred about 2.27 times as often as UK nationals in 2024/25, and about 3.35 times in 2025/26. DWP says a caseworker decides every flagged case. It published its fairness assessment and committed to retrain the model, which stays in use until a retrained version is tested.

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