PAN Lab example
Serbia Social Card (Socijalna karta)
Cut off by a data match: a social-assistance registry
Serbia's Social Card registry matches records to flag suspected income or assets. Its flags could cut assistance, were rarely contested, and were hard to correct.
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The Social Card (Socijalna karta) is a registry run by Serbia's Ministry of Labour, Employment, Veterans and Social Affairs. It cross-links roughly 130 to 135 categories of data from other state registers to check eligibility for social assistance. Its rules are undisclosed, and the sources show no evidence that it uses machine learning.
How it is used
The Law on the Social Card, adopted in 2021, gave the registry a statutory basis. It has been in force since 1 March 2022.
The registry draws on Tax Administration records, the Ministry of Interior's vehicle records, and property, pension, and banking data. The data cover each person receiving social assistance, their relatives, and so-called connected persons. The sources do not define connected persons. The registry uses them to verify eligibility and to detect undeclared income or assets.
The system is semi-automated. It flags discrepancies and sends notifications to social workers at municipal Centers for Social Work. The workers are meant to investigate before benefits are reduced, suspended, or removed. Over roughly two years, the Ministry processed more than 100,000 notifications of suspected increases in income or assets.
The registry's setup was financed as part of an 82.6 million euro World Bank public-sector loan. The World Bank also provided technical guidance.
What went wrong in documented cases
The Lab's case file, its written account of the sources, finds that the match turns informal, cash-based lives into a few sparse data points. The documented errors show how.
A one-off funeral donation of about 170 euro was flagged as income that disqualified a person. Two cars sold for scrap years earlier were still registered as assets. A collector of recyclable materials was recorded with income that implied selling far more material each month than was plausible. A single mother's seasonal jobs and vocational grant were read as regular income.
Amnesty International documented automation bias: workers deferring to the system instead of using their own judgment. Workers told beneficiaries there was nothing they could do, because the new system had decided.
Correcting a wrong flag
A removed person has 15 days to appeal. They must wait three months to reapply, whatever has changed in their circumstances. Removal letters often refer only to unspecified data from the electronic database.
Over roughly two years, beneficiaries filed 361 appeals against the rulings of the Centers for Social Work. In the same period, the Ministry processed more than 100,000 notifications. The two figures count different groups of people. So the gap shows how rarely flags were contested, not a measured appeal rate.
A11, the Initiative for Economic and Social Rights, alleges that social workers cannot correct errors recorded in the system, because it is semi-automated.
How many people lost assistance
Named sources report the caseload of financial social assistance falling by tens of thousands after the law. The figures depend on method and move over time.
A11, reading government data, records 211,266 recipients in February 2022 and 182,773 at the end of 2022. It records 168,307 by 1 February 2024, with at least 44,000 people having lost assistance since the law took effect.
Government figures cited by Amnesty International show about 176,000 recipients in August 2023. That is roughly 35,000 fewer than in March 2022. Earlier reporting by the Thomson Reuters Foundation put removals at about 27,000, some 15 percent of recipients.
The UN Working Group on Business and Human Rights visited Serbia from 6 to 15 October 2025. It reported that implementation of the law had left over 60,000 citizens without social assistance. Its full report is due to the Human Rights Council in June 2026.
These are largely net falls in the caseload, not audited counts of removals the system caused. The Serbian government attributes part of the fall to a stronger economy, so the cause is contested.
Who is most affected
The case file reports the system hitting the poorest, and Roma especially. The reporting says the informal earnings many Roma households rely on are misclassified as income. In 2023, Amnesty International reported that the system was worsening poverty, especially for Roma and people with disabilities.
The registry records no ethnicity. So the Roma figures are inferred from how many Roma households rely on benefits, and from case documentation. A 2019 UNICEF estimate, cited in the reporting, put the share of households in Roma settlements receiving some benefit at 84 percent.
Where the challenges stand
The system is operating and heavily contested. As of the latest reporting, no court or panel has ordered changes.
A11 filed a petition with the Constitutional Court of Serbia in April 2022. In late November 2022, a coalition from ESCR-Net, an international network for economic, social, and cultural rights, submitted a joint legal opinion to the court. It argued the law violates the rights to social security, equality and non-discrimination, privacy, due process, and remedy. The case remained pending.
On 31 March 2024, A11 filed a Request for Inspection with the World Bank Inspection Panel for six requesters, including Roma community members. It alleges that roughly 44,000 people were negatively and disproportionately affected. The Panel registered the request on 24 August 2024. It sent its Report and Recommendation to the World Bank's Board on 12 November 2024. As of 30 June 2025, the Board had not decided whether to authorize an investigation.
A11 has called for the law to be repealed. Amnesty International asks that the system be suspended, and made to respect rights if its risks cannot be prevented.
Where to look
Watch where correction is supposed to happen. Social workers are still part of the process. But the case file finds correction broken by the design of the system, not by individual neglect. A wrong flag counts again in the next match, because nothing pulls it back out.
The case file asks a live question. On what published basis, open to checking, may a state cross-link everyone's records and cut off the poorest? And who can correct it when the match is wrong?
What the available tools can and cannot address
A failure pathway is a link between two parts of the network where a mistake made by one part can be passed on to the other. A tool closes a failure pathway when mistakes stop passing along it. The work along it goes on.
Explore (No Targets) sets no targets. Under Service Targets Only, the targets can be met within this case's budget of 12 units. The cheapest way costs 3 units and uses one tool, Vet connections. Other ways pair two tools for 4 units, such as Mark AI-written records with Escalate checks.
Under Service and Safety Targets and All Governance Targets, the targets include closing every failure pathway. They can be met, but only just. Every combination of tools was checked. The cheapest way costs 11 of the 12 units and uses five tools: Understand the system, Escalate checks, Peer sharing rules, Vet connections, and Store less data.
Understand the system costs 4 units at these two levels. While it is on, Escalate checks, Peer sharing rules, and Vet connections each cost 1 unit less.
Under Service and Safety Targets, one more way costs all 12 units without Understand the system. It uses Mark AI-written records, Escalate checks, Peer sharing rules, Vet connections, and Store less data. Under All Governance Targets that way falls short. At that level, Store less data, Vet connections, Peer sharing rules, and Check copied records work less well without Understand the system.
Check copied records adds the check of a flag against its source. It closes none of the failure pathways, and it is in none of these ways. Upgrade model, Require sign-off, and Review on schedule close none either.
Using every tool at its strongest setting, at any cost, closes every failure pathway. But the added checks cost too much useful work to meet the service targets at any level that has targets.
Open this example in PAN Lab v0.1 to apply pressures and levers and watch what the system does.
What this models
This example runs on the Social-Card-class cross-registry data-matching registry network: 5 components and 12 pathways between them. Every context in the Lab is a stylized model, never a reconstruction of any actual deployment, and each assumption behind it carries a provenance label.
Evidence base: 3 assumed · 3 published baseline. In the Lab, the shaded evidence band behind each headline readout draws its width from the least-established class below.
Show all 6 assumptions
- assumed
This example follows the pattern of data matching across registers in the Lab's case file on Serbia's Social Card. It is not a reconstruction of the actual registry, whose rules are undisclosed.
- baseline
This example shows the Social Card registry as rules-based data matching, not a predictive model. Its rules are undisclosed, and the sources show no evidence that it uses machine learning. So a better prediction model is not where change would come from here.
- assumed
This example assumes social workers pick up from one another how far to defer to the registry. It assumes one set of matching rules gives every household the same blind spot. It also includes, as a check that can be added, a second look by a fellow worker. The sources describe no such review. Amnesty International reported workers telling people there was nothing they could do, because the system had decided.
- baseline
This example includes the other state registers as the source of the data the registry matches: roughly 130 to 135 categories, often from out-of-date snapshots. The matching uses data other state bodies collected for other purposes, and covers relatives and so-called connected persons. The sources do not define connected persons. Who that exposes, and how, comes from the case file. Nothing in the network computes it.
- baseline
The feature that defines this example is an alleged lack of a way to correct errors inside the system. A11's request to the World Bank Inspection Panel alleges that social workers cannot correct errors recorded in the system, because it is semi-automated. So the example includes, as a check that can be added, a comparison of each flag with its real-world source. Apart from the social workers' investigation, the sources describe no such check. A removed person has 15 days to appeal and must wait three months to reapply. Over roughly two years, 361 appeals were filed while more than 100,000 notifications were processed. The two figures count different groups, but together they show a correction route people could rarely use.
- assumed
The poorest, Roma, and people with disabilities are reported among the most affected. The reporting says the informal earnings many Roma households rely on are misclassified as income. The registry records no ethnicity. So the Roma exposure is inferred from how many Roma households rely on benefits and from case documentation, not from official counts. This example shows how errors move among the registry, the social workers, and the records, not who the people are. It estimates no difference in harm between groups of people served. That exposure and the harms to individuals come from the case file and are measured outside the network.
What this example does not show
Show all 2 limitations
- This example does not show who was harmed. The poorest, Roma, and people with disabilities are reported among the most affected. The reporting says the informal earnings many Roma households rely on are misclassified as income. The registry records no ethnicity, so the Roma exposure is inferred from reliance on benefits and case documentation, not official counts. The example shows how errors move among the registry, the social workers, and the records. It estimates no difference in harm between groups of people served. That exposure and the harms to individuals come from the case file and are measured outside the network.
- This example does not use the caseload figures as rates. They are a moving range that depends on method. There were about 35,000 fewer recipients by August 2023. At least 44,000 people had lost assistance by early 2024. Over 60,000 were left without assistance by October 2025. They are largely net falls in the caseload, not audited counts of removals the system caused. The government attributes part of the fall to a stronger economy. The example uses the shape of the case, not measured rates.
Sources and evidence
What this example rests on, claim by claim. Every entry resolves to the same ledger the Evidence Registry publishes.
Serbia's Social Card (Socijalna karta) registry, given a statutory basis by the Law on the Social Card in force from 1 March 2022 and financed in part by an 82.6 million euro World Bank public-sector loan, cross-links roughly 130 to 135 categories of data from other state registers to verify social-assistance eligibility and flag suspected undeclared income or assets. After the law, named sources report the caseload falling by a range of tens of thousands: government figures cited by Amnesty International show about 35,000 fewer recipients by August 2023, A11 counts at least 44,000 people having lost assistance by early 2024, and the UN Working Group on Business and Human Rights reported over 60,000 without assistance by October 2025. These are largely net caseload declines rather than audited counts of system-caused removals, and the government attributes part of the fall to a stronger economy. Roma are reported among the most affected because informal earnings are misclassified as income, but the registry records no ethnicity, so this is inferred rather than officially disaggregated. As of the latest reporting, Constitutional Court, World Bank Inspection Panel, and UN scrutiny were pending or active, with no court or panel yet ordering changes.
empirical- Advocacy A11 - Initiative for Economic and Social Rights, Two Years of the Social Card Law: Fair Distribution of Financial Social Assistance Remains Out of Reach (2024) https://www.a11initiative.org/en/two-years-of-the-social-card-law-fair-distribution-of-financial-social-assistance-remains-out-of-reach-law-should-be-abolished/
- Advocacy Amnesty International, Trapped by Automation: Poverty and discrimination in Serbia's welfare state (2023) https://www.amnesty.org/en/latest/research/2023/12/trapped-by-automation-poverty-and-discrimination-in-serbias-welfare-state/
- Investigative Context / Thomson Reuters Foundation, As Serbia adopts digital welfare system, the poorest miss out (2023) https://www.context.news/digital-rights/as-serbia-adopts-digital-welfare-system-the-poorest-miss-out
- Government UN Working Group on Business and Human Rights, End of Mission Statement, Serbia visit 6-15 October 2025 (2025) https://www.ohchr.org/sites/default/files/documents/issues/business/workinggroupbusiness/2025-10-15-eom-wgbhr-serbia-en.pdf
- Government World Bank Inspection Panel, Panel Registers the Request for Inspection from Serbia Public Sector Efficiency and Green Recovery Program (2024) https://www.inspectionpanel.org/news/panel-registers-request-inspection-serbia-public-sector-efficiency-and-green-recovery-program
- Reference China-CEE Institute, Serbia political briefing: Two years of the implementation of the Law on social card (2024) https://china-cee.eu/2024/04/10/serbia-political-briefing-two-years-of-the-implementation-of-the-law-on-social-card/
Under Serbia's Social Card system, a removed beneficiary has 15 days to appeal and must wait three months to reapply regardless of changed circumstances, and removal letters frequently reference only unspecified data from the electronic database. A11's Request for Inspection to the World Bank Inspection Panel alleges that, because the system is semi-automated, social workers cannot correct errors recorded in it. Over roughly two years the Ministry processed more than 100,000 notifications of suspected income or asset increases, while beneficiaries filed only 361 appeals against Centers for Social Work rulings; because the two figures cover different populations, the gap illustrates how rarely flags were contested rather than a measured appeal rate. Documented misclassifications include a one-off funeral donation read as income and long-scrapped cars still counted as assets.
empirical- Advocacy Amnesty International, Trapped by Automation: Poverty and discrimination in Serbia's welfare state (2023) https://www.amnesty.org/en/latest/research/2023/12/trapped-by-automation-poverty-and-discrimination-in-serbias-welfare-state/
- Advocacy A11 - Initiative for Economic and Social Rights, Two Years of the Social Card Law: Fair Distribution of Financial Social Assistance Remains Out of Reach (2024) https://www.a11initiative.org/en/two-years-of-the-social-card-law-fair-distribution-of-financial-social-assistance-remains-out-of-reach-law-should-be-abolished/
- Government World Bank Inspection Panel, Panel Registers the Request for Inspection from Serbia Public Sector Efficiency and Green Recovery Program (2024) https://www.inspectionpanel.org/news/panel-registers-request-inspection-serbia-public-sector-efficiency-and-green-recovery-program
- Reference China-CEE Institute, Serbia political briefing: Two years of the implementation of the Law on social card (2024) https://china-cee.eu/2024/04/10/serbia-political-briefing-two-years-of-the-implementation-of-the-law-on-social-card/
- Investigative Context / Thomson Reuters Foundation, As Serbia adopts digital welfare system, the poorest miss out (2023) https://www.context.news/digital-rights/as-serbia-adopts-digital-welfare-system-the-poorest-miss-out
Where this connects
Institutional pressures in this domain
- Austerity & recovery incentives — Cost-cutting and overpayment-recovery targets tilt the system toward denial and enforcement errors.
- Vendor opacity — The deploying institution cannot inspect the model, data, or update pipeline it is accountable for.
- Compliance over substance — Paper controls (sign-offs, checklists) satisfy audits while the behavior they describe erodes.
- Reviewer bottleneck — One fixed-capacity checking stage sits between AI output and consequence; everything queues behind it.
All of them in context on the Public benefits & eligibility domain page.
Levers available here and the patterns behind them
- Store less data — Data minimization
- Check copied records — Reconcile copied records
- Vet connections — Connection authorization
- Understand the system — Understand the system
- Mark AI-written records — Provenance labeling
- Require sign-off — Conformity assessment gate
- Review on schedule — Oversight cadence & retrospectives
- Upgrade model — Improve the model
- Escalate checks — State-feedback vigilance
- Peer sharing rules — Peer-edge governance
Documented case histories
- Serbia Social Card (Socijalna karta)
- Michigan MiDAS
- Robodebt (Australia)
- Indiana / IBM eligibility modernization
- Rotterdam welfare-fraud risk model
- Arkansas ARChoices / ARIA
- Netherlands childcare-benefits scandal (Toeslagenaffaire)
- SyRI (Netherlands)
- CNAF benefit-fraud risk score (France)
- Forsakringskassan VAB fraud-selection profile (Sweden)
- Udbetaling Danmark data-driven control (Denmark)
- BOSCO (Spain)
- UK DWP Universal Credit Advances fraud model
- ID.me identity verification as an unemployment eligibility gate
- Medicaid unwinding: automated ex parte renewal at population scale
- INSS auto-analysis: when the productivity metric makes denial the fastest way out
- Samagra Vedika
- Workforce Australia Targeted Compliance Framework: automated payment sanctioning after Robodebt
- NYC MyCity business chatbot
- Nevada DETR generative-AI unemployment appeals
- Tennessee TennCare TEDS