From Automation to Accountability: The Promise and Perils of AI in Social Security Systems

The OECD report shows that artificial intelligence is already improving efficiency and access in social security systems across Europe, from automating eligibility checks to streamlining document processing, when applied to well-defined tasks with human oversight. It concludes that AI can strengthen social protection only if backed by strong governance, high-quality data, workforce readiness, and transparency to maintain public trust.

Produced by the OECD’s Public Governance Directorate and Employment, Labour and Social Affairs Directorate, in collaboration with the European Commission and social security institutions in France (CNAF, CNAM, MSA), Italy (INPS), Finland (Kela) and Germany’s Federal Employment Agency, a report examines how artificial intelligence is being introduced into one of the most sensitive areas of public administration. Funded by the European Union through the Technical Support Instrument, the study starts from a shared diagnosis: social security systems across Europe face high administrative burdens, fragmented data, staff shortages and widespread non-take-up of benefits. Millions of eligible citizens fail to receive support simply because systems are complex, slow, or poorly connected. Against this backdrop, AI is presented not as a futuristic experiment, but as a pragmatic tool that could help administrations deliver benefits more efficiently, proactively and fairly, if used responsibly.

What AI Is Already Doing on the Ground

Rather than speculating, the report focuses on real, operational use cases. In Catalonia, AI has been applied to address energy poverty, where legally entitled households often lost access to basic energy services due to administrative complexity. An AI-enabled platform developed by the Open Administration Consortium of Catalonia now automates much of the eligibility verification process by securely integrating data from multiple public bodies and generating standardised reports for municipalities. Built on deterministic, rule-based logic rather than opaque prediction, the system has reduced administrative workload, improved consistency across municipalities and significantly lowered non-take-up, even though legal consent requirements still prevent full automation.

In Germany, the Federal Employment Agency uses machine learning to process over 100,000 unstructured job advertisements each year. The AI tool, ADEST, extracts and categorises information from emails, PDFs and web links, presenting staff with suggested classifications and confidence scores. Human caseworkers retain full decision authority, ensuring accountability while cutting processing time by more than half. In Finland, Kela has taken a different route, applying AI to back-office efficiency rather than eligibility decisions. Its in-house platform automates document classification, text recognition and call transcription, processing more than 16 million attachments annually and saving the equivalent of dozens of staff-years. Together, these cases show that AI can deliver tangible gains when tightly scoped and carefully governed.

Governing AI Where Mistakes Have Real Consequences

A central message of the report is that social security is not a neutral testing ground for technology. Because AI systems can affect access to essential benefits, the sector is explicitly classified as high-risk under the EU AI Act when algorithms influence eligibility or entitlement. The report therefore places heavy emphasis on governance, structured around the OECD’s three-pillar framework of enablers, guardrails, and engagement. While most EU and OECD countries now have national AI strategies, the report finds that institutional strategies within social security agencies are often fragmented. Investment decisions lack clear criteria, data governance remains weak, and interoperability gaps continue to limit what AI can safely achieve.

Guardrails such as ethical frameworks, audits, transparency requirements and accountability mechanisms exist in many countries but are unevenly applied and often voluntary. The report points to past failures in Europe, where poorly governed algorithms led to discrimination or wrongful benefit withdrawal, as stark reminders of what is at stake. It argues that compliance with the AI Act must be complemented by practical oversight tools embedded in everyday administrative practice.

Trust, Transparency and the Missing Voices

One of the report’s most critical findings concerns engagement. While civil servants are increasingly involved in piloting and testing AI systems, service users and affected communities are rarely consulted in a systematic way. Public awareness of AI use in social security remains low, and mechanisms for feedback, challenge, or redress are limited. This lack of engagement risks undermining trust, particularly among vulnerable populations who already face barriers to access. The report stresses that transparency about when and how AI is used, combined with meaningful user involvement across the design and deployment process, is essential if AI is to strengthen rather than weaken confidence in social protection systems.

Preparing the Workforce for an AI-Enabled State

Finally, the report turns to the human dimension. AI adoption is reshaping work in social security institutions by automating repetitive tasks and augmenting cognitive work, not by replacing public servants outright. Evidence so far suggests productivity gains and improved job quality, but only where staff are equipped with the right skills. Recruiting advanced technical talent remains difficult for public institutions, making training and in-house capability development crucial. The report argues that AI literacy, ethical awareness, and data skills should be as central to workforce strategies as software procurement. Ultimately, it concludes that AI will only deliver lasting benefits in social security if governments invest as much in people, governance, and trust as they do in technology itself.