OECD Report on Harnessing Artificial Intelligence in Social Security: Global Governance and Strategic Workforce Readiness
SDG 9: Industry, Innovation and Infrastructure | SDG 16: Peace, Justice and Strong Institutions
Ministry of Electronics and Information Technology (MeitY) | NITI Aayog | Department of Social Justice and Empowerment
The OECD report, Harnessing Artificial Intelligence in Social Security, comprehensively examines how AI can address persistent challenges in social protection, such as reducing administrative burdens, streamlining complex eligibility processes, and improving outreach to reduce non-take-up of benefits. The report organizes its analysis around three pillars for trustworthy AI adoption in the public sector: Enablers, Guardrails, and Engagement.
What is non-take-up of social benefits? Non-take-up refers to the phenomenon where individuals who are eligible for social protection benefits, such as pensions, family allowances, or poverty support, do not receive them. This exclusion is often caused by administrative complexity, lack of awareness, stigma, or fragmented data systems, which AI applications aim to mitigate by proactively identifying eligible individuals and simplifying application processes.
Successfully deploying trustworthy AI requires strengthening core foundations:
Enablers: Governments need to invest in strategic AI governance, robust, scalable infrastructure (cloud or on-premises), and data governance frameworks to ensure access to high-quality, representative datasets for training AI models.
Guardrails: Essential for managing risks, these include ethical oversight, legal compliance with frameworks like the EU AI Act (which classifies social security eligibility evaluation as high-risk) , transparency measures (public registries, documentation), and strong accountability frameworks.
Engagement and Workforce Readiness: AI adoption requires aligning workforce development—through strategic recruitment, outsourcing, and training—to build in-house AI capability and ensure staff have “sufficient level of AI literacy” as mandated by the EU AI Act. It also requires the involvement of staff, users, and stakeholders in designing and deploying AI systems to build trust and ensure inclusivity
AI systems are currently applied across several functions:
Claims Administration: Automating back-office tasks like digitizing paper records and classifying unstructured data, which enhances efficiency and accuracy. For example, Germany’s Federal Employment Agency uses ADEST (Automated Data Extraction and Structuring Tool) to categorize over 100,000 annual job postings, significantly reducing manual effort. Finland’s Kela automates the processing of over 16 million user-submitted documents, saving an estimated 38 person-years of staff time.
Service Delivery: Communicating with beneficiaries via chatbots and supporting decision-making by summarizing case notes and identifying risks.
Policy Design & Monitoring: Future uses include predictive analytics to forecast demands, identify at-risk individuals, and detect fraud or anomalies.
The report notes recurring challenges globally, including a lack of algorithmic transparency, the risk of reinforcing bias from historical data, and skills gaps.
India-Specific Context
While the OECD report focuses on EU and OECD member countries, parallel policy developments and specific use cases in India provide essential context for the “Harnessing AI in Social Security” framework:
National AI Vision and Governance Framework:
India’s #AIforAll approach—led by MeitY and NITI Aayog—supports AI deployment in public services through the IndiaAI Mission (₹10,300+ crore). The accompanying India AI Governance Guidelines (November 2025) adopt risk-based oversight for AI in essential services, aligning with the OECD’s framing of social protection as a high-risk AI domain.Emerging Welfare Use Cases and Safeguards:
AI use in welfare delivery is expanding toward automated monitoring and verification to reduce leakages and speed up processing. State governments have indicated plans to use AI to track pension and scholarship schemes to reduce leakages and speed up verification. At the same time, past attempts at algorithmic eligibility and entity-resolution systems in welfare delivery have highlighted risks of data errors, opacity, and beneficiary exclusion, reinforcing the OECD’s emphasis on transparency, human oversight, and accountability as automation deepens.
Follow the full report here: Harnessing Artificial Intelligence in Social Security