AI in social protection: harnessing potential, preventing harm

This blog has been published as part of the launch event for the AI Hub for Social Protection. Click here to register for the virtual launch event on December 2, 2025.

Around the world, social protection systems are under strain. From climate shocks to geopolitical and economic uncertainty, overlapping crises have exposed and exacerbated longstanding gaps in how cash transfers, food assistance, pensions, and other social benefits are delivered, who gets left out, and how quickly systems can respond. Governments are now expected to do more: deliver faster, to more people, but with tighter resources and rising public scrutiny.

Artificial Intelligence (AI) has emerged as a powerful solution to address these challenges. While foundational improvements such as interoperable registration systems or unified databases stem from broader digital transformation, AI plays a distinct role in enhancing how these systems function. For example, once basic records are digitized, AI models can analyze data patterns to flag inconsistencies, detect missing information, or pre-fill fields with user consent, improving both speed and accuracy. Eligibility checks that once took weeks can now be supported with machine-learning models that identify households likely to meet program criteria. In grievance handling, AI tools such as chatbots or conversational agents using natural language processing can triage complaints, detect recurring issues, and direct cases to the right channels for faster resolution. When thoughtfully implemented, AI-enabled systems can ease workloads for frontline staff and make grievance processes more timely, transparent, and responsive.

But this potential comes with risks, especially in a domain like social protection that serves the most vulnerable populations. AI systems are not neutral; their outcomes reflect the quality of the data, the soundness of the programming, the adequacy of human training, and the policy choices behind their design. In the Netherlands, for example, an algorithm used to flag irregularities in child benefit claims incorporated dual nationality as a risk factor. This outcome stemmed from poor-quality data and how they were used, rather than deliberate intent to target migrants.^[1] It reflected broader policy priorities around fraud prevention and resource management. While these aims were legitimate, the approach resulted in thousands of families being wrongly accused of fraud, with a disproportionately high number of families with migrant backgrounds targeted. This case illustrates how successful AI deployment depends on how the AI is framed, what objectives it serves, what data it uses, and the governance structures that shape it. In the absence of accountability, and transparent and user-centered design principles, these contextual factors can distort outcomes and entrench harm.

What AI in social protection looks like today

While AI is now present across many branches of social protection, most applications remain at an early or narrow stage of maturity. From South Asia to Sub-Saharan Africa, governments and their partners are experimenting with AI to improve delivery and oversight.^[2] Emerging applications support:^[3]

• Eligibility prediction: Using AI to pre-identify households likely to qualify for a benefit, enabling faster outreach or auto-enrolment.
• Anomaly and fraud detection: Analyzing historical data to flag outliers, duplications, or fraudulent claims.
• Automating case management: Using natural language processing or rule-based models to support social workers.
• Chatbots and virtual assistants: Offering real-time assistance on entitlements, status of applications, or grievance management.
• Predictive analytics for crisis response: Anticipating areas or populations likely to need support due to shocks (e.g., floods, drought, displacement).

Three main reasons underpin governments’ growing interest in using AI in social protection:^[4]

• Efficiency and quality of service: AI can automate laborious tasks, including eligibility checks, grievance management, and data cleaning, thereby reducing capacity constraints. AI can also help institutions tailor information, guidance, and services to individual needs, improving overall quality of service.
• Cost-effectiveness: Streamlining operations reduces leakages, duplicate payments, and administrative overheads.
• Perceived objectivity: AI is often seen as a way to improve the quality and consistency of decisions, reducing arbitrary variations between similar cases. In some contexts, this means automating routine processes, while in others, it means providing caseworkers with better quality data and analyses so discretion can be applied more fairly and transparently.

As we observe the evolving landscape, three notable trends emerge:

• Uneven readiness: Some countries are actively piloting AI tools, while others remain cautious observers, often hindered by a lack of enabling ecosystems. Even within institutions, teams often have widely varying levels of digital and analytical capacity, which shapes how effectively AI tools can be adopted, interpreted, and governed.
• AI applications in social protection remain concentrated in a few areas, even as interest accelerates: Current use cases cluster around a few application areas, such as fraud detection, process automation, and chatbots. On the other hand, potential uses, such as early warning for climate-linked shocks, detecting exclusion errors, improving case prioritization for social workers, or supporting data quality improvement, remain underexplored.
• Demand is outpacing safeguards: There are still no widely accepted gold standards for responsible and ethical AI adoption in social protection, and the lag in developing standards, ethical norms, and local capacities risks widening without coordinated action. In many settings, discussions also focus on where to use AI rather than asking whether it is the appropriate solution, or whether complementary reforms are needed to leverage AI responsibly.

As enthusiasm around AI in social protection grows, a small but instructive set of country experiences are beginning to shed light on what happens when intent outpaces careful design.

Globally, AI adoption in social services is outpacing the development of adequate safeguards, prompting growing calls for transparency and oversight.

AI applied in social protection: When good intentions meet real-world complexities

Some real-world scenarios provide valuable insights especially when systems interact with the complex realities of identity, vulnerability, and frontline service delivery:

1. India: When Data Mismatches Deny Benefits

In 2024, an AI-integrated welfare system in the state of Telangana was set to auto-reject applications where Aadhaar (national ID) numbers didn’t perfectly align across government databases. Thousands of households, including elderly citizens with worn fingerprints, migrants with outdated records, and climate-displaced families, lost access to essential food rations.^[5]

 2. The Netherlands: Invisible Logic, Visible Harm

The Netherlands used SyRI (System Risk Indication), an algorithmic risk-scoring tool. Despite being built within a robust legal framework, the system disproportionately targeted low-income and immigrant communities. Ultimately, courts ruled it discriminatory and in violation of basic rights, prompting its shutdown in 2020.^[6]

 3. Chile: When System Design Ignores the Frontline^[7]

Chile built a predictive model to identify children at risk of neglect or abuse. However, the model faltered at the point of use. Social workers who were expected to act on the data were not involved in the creation of the model, questioned the underlying assumptions and felt the approach undermined their judgment. Burdened with extra data entry and what they saw as little added value; many rejected the tool. The program was paused and had to be rebuilt with direct input from field staff.

These examples from countries diverse in geography, income level, and system maturity, highlight recurring challenges:

• Opacity and accountability gaps: When AI-enabled systems operate as “black boxes,” applicants and frontline workers may not understand how decisions are made or how to challenge them. Weak grievance redressal mechanisms make it harder to contest or correct errors, reducing trust and accountability. Social security institutions can mitigate this by publishing decision criteria, using explainability tools, and ensuring human review for high-stakes decisions.
• Embedded bias: Pre-existing social inequalities can get amplified if training data reflects systemic exclusions. That said, bias is not always undesirable; targeting itself is a form of intentional prioritization. What matters is that institutions assess data quality, document both intended and unintended biases, and test models for exclusion errors.
• Missing human safeguards: Automated processes without clear redressal mechanisms leave little room for course correction. AI adoption can proceed safely by ensuring fallback mechanisms, human override, and functioning grievance redressal.
• Lack of co-design: Tools that disregard frontline users and community voices can go underutilized or misapplied. Lightweight co-design (workshops with front-line staff, and usability tests) can drastically improve acceptance and accuracy at low cost.

These challenges are not uncommon. Similar tensions have emerged elsewhere, such as in the UK’s visa system, where automation flagged applicants based on opaque criteria that reinforced historical biases in the system, raising legal and ethical concerns.^[8] In the U.S., predictive tools used in child welfare and Medicaid eligibility have been challenged for embedding racial bias and undermining due process.^[9] Globally, AI adoption in social services is outpacing the development of adequate safeguards, prompting growing calls for transparency and oversight.

AI promises efficiencies and analytical depth, but many countries are building on weak institutional and data foundations. The above examples are symptoms of these deeper structural issues, potentially hindering any digital reform, not only AI adoption. But because AI magnifies both benefits and risks of digitalization, its success depends heavily on the strength of these foundations. These challenges are not only technical, but also deeply institutional and political:

• Fragmented data systems: Many departments still rely on patchy, non-interoperable databases with questionable accuracy and limited safeguards. This is a risky foundation for high-stakes automation.
• Vendor-driven implementation: Proprietary systems are often introduced through opaque processes, leading to vendor lock-ins, limited public oversight, and minimal capacity transfer to governments.
• Frontline overload: Workers are expected to use AI-enabled tools without adequate training or support. Often, they must act on opaque AI-made decisions rather than being empowered to use the tools themselves. Instead of easing their burden, new systems add to it.
• Asymmetries of power: Whether between citizens and the state, or between governments and tech vendors, these imbalances distort whose interests are prioritized in system design and deployment.

Taken together, these factors define the realities for countries striving to scale up social protection coverage, even as they race to improve efficiency. These systemic weaknesses don’t just make implementation difficult; they amplify the consequences of failure. Mistakes in delivery can deny food to hungry families, delay relief to disaster survivors, or flag a parent as “risky” based on flawed logic. The margin for error is thinner, and the implications can be significant.

When designed with intention, AI can expand the reach, responsiveness, and equity of social protection systems.

Why social protection needs a higher bar for AI

Three features make AI in social protection uniquely sensitive:

1. The power it holds: In some systems, AI is not just advising, it is deciding who gets benefits, how much, and when. Mistakes in delivery, such as exclusion, misclassification, and denial, can deeply affect livelihoods, dignity, and survival.
2. The people it serves: Often, the most marginalized, including informal workers, women and children, persons with disabilities, or people without digital access. These people are least visible in data and least able to contest opaque decisions.
3. The context it operates in: Delivering social protection at scale requires coordination across ministries, departments, and databases. Human coordination of this magnitude is a tall order. There are also technical complications where governments are expected to build intelligent, integrated systems while juggling messy data, overburdened staff, and siloed infrastructure. Furthermore, many countries and systems still lack foundational structures and capacities, such as sufficiently robust digital public infrastructure, clear accountability, strong data governance, or functioning grievance redressal. AI, in such contexts, can compound existing gaps rather than close them.

So far, responses to AI-related risks remain fragmented. Some governments have hit pause on AI deployments amid public backlashes, while others have accelerated digitalization without rethinking governance.

If AI in social protection must meet a higher bar, then no country can and should be asked to meet that bar alone – what’s needed is a trusted, multi-stakeholder space for shaping AI in social protection; an enduring mechanism for bringing together governments, technologists, researchers, and civil society to ask hard questions, share knowledge and learning, and co-create better AI-powered solutions.

Furthermore, meeting this higher bar should not mean delaying innovation. When designed with intention, AI can expand the reach, responsiveness, and equity of social protection systems.

The path forward is not just about what AI can do, it is about how we choose to use it.

AI Hub for social protection: A shared space for navigation

The AI Hub for Social Protection is designed to provide exactly what is needed: a trusted resource and collaborative platform supporting country-led, responsible, and context-aware adoption of AI in social protection.

Unlike many AI initiatives that focus on technical innovation, the AI Hub begins with a different question: “What is the problem you are trying to solve, and is AI the right solution?”

While there are other important initiatives, such as AI sandboxes for testing tools and global summits on AI ethics, these often address AI in general terms, without the depth, governance focus, and context-specific support required in social protection. The AI Hub is unique in its dedicated focus on social protection, its grounding in rights-based governance, and its emphasis on national sovereignty alongside innovation.

The Hub stands out for its focus on real-world constraints, human-centered design, and shared ownership. It works with governments, technologists, researchers, and civil society to navigate AI adoption in ways that are responsible (minimizing harm and bias), innovative (enabling safe experimentation), and sovereign (ensuring countries retain control over their systems and data).

The AI Hub does not set out to replace comprehensive digital transformation programs or long-term capacity building. Instead, it provides time-bound, strategic, and demand-driven support to help governments make informed, responsible decisions about AI adoption. In practice, this includes both advisory services at the policy and technical level. Topics include AI strategy and regulatory frameworks, feasibility and risks assessments, design, development and scaling of AI-driven solutions, and testing algorithms for fairness and robustness.

The path forward is not just about what AI can do, it is about how we choose to use it. The AI Hub for Social Protection aims to support that choice – deliberately, collaboratively, and with a clear sense of responsibility. The work starts not from shiny tools but from real-world problems, lived constraints, and respect for the people and systems AI aims to serve.

This is our shared task: to harness the promise of AI while avoiding the injustices of the past; to build not only intelligent systems, but just ones; to scale what works, to identify risks and provide guidance on potential harms; and to ensure that the future of social protection is responsive, smart, and fair.

AI’s influence on social protection is inevitable. The real question is: will governments shape this evolution on their own terms, with the right safeguards, capacities, and governance, or risk having the transformation shaped for them?

Let’s get it right. Together!

Authors: Ronda Zelezny-Green and Corinne Grainger