AI for Refugee Operations

Last updated: 2026-08-15

AI in refugee operations covers registration, appointment scheduling, feedback and accountability, and the data integrity work underneath all three. Shahzad Asghar built UNHCR\u2019s first global interactive voice response appointment system, used by more than 700,000 refugees, and DigitalAAP, a voice-based feedback platform selected for the UN Global Pulse Accelerator programme.

What artificial intelligence has actually delivered in refugee operations, drawn from systems built and run inside UNHCR rather than from pilots. Each example states the problem, the approach, the measured outcome, and the constraint that decided the design.

The appointment system: reaching people who have a phone, not an app

Problem. Refugees needing a registration appointment had to queue in person, often travelling long distances and losing a day of work for a slot that might not exist. Any digital alternative had to reach people with a basic handset, no data plan, and limited literacy, in more than one language.

Approach. A voice service rather than an application, because a phone call reaches a person an app never will. The system became UNHCR’s first global interactive voice response appointment platform.

Outcome. More than 700,000 refugees used it. Registration service time fell by 83 percent. The design was replicated across five country operations: Egypt, Iraq, Syria, Iran, and Ethiopia.

DigitalAAP: accountability to affected populations

Problem. Feedback from displaced people arrives as speech, in many languages and dialects, through channels that do not produce structured records. Complaints that are never classified are never routed, and a complaint that is never routed is not accountability.

Approach. DigitalAAP converts refugee voice notes into actionable insight using speech-to-text and classification models, so that what people say reaches the team able to act on it. Human review stays on the path for anything sensitive or ambiguous.

Outcome. DigitalAAP was selected for the UN Global Pulse Accelerator programme in its third cohort and is listed in the UN Innovation Network library as refugee feedback through AI.

Data integrity: the work nobody sees

Problem. Large refugee registration databases accumulate duplicates, gaps, and errors. Every service built on that data inherits the defects, and staff stop trusting the numbers they are asked to act on.

Approach and outcome. Data integrity analysis recovered 63,000 missing contacts, restoring the ability to reach people who had effectively become unreachable in the system. Contact data is the precondition for every other channel: without it, an appointment system has nobody to call.

What these systems have in common

None of them put a model in front of a refugee making an unreviewable decision. Each sits behind an operational process with a named owner. Each was designed for the channel people actually hold rather than the channel that is convenient to build. And each was constrained first by protection considerations: the people described in this data may be at risk from the authorities they fled, so minimisation and access control are protection measures, not paperwork.

The delivery discipline behind them is set out as the Last-Mile AI Framework. The wider sector view is on the AI for humanitarian organisations pillar, and the governance that has to accompany deployment is in the AI governance playbook. The full portfolio is in AI projects.