REFUGEESUsing Machine Learning to Help Refugees Succeed

By Dylan Walsh

Published 28 November 2023

A new set of machine learning tools is helping countries place refugees where they’re most likely to find employment.

Dominik Rothenhaeusler grew up in Oberzell, Germany, a quaint town of roughly 2,500 people along the Schussen River. Like many towns and cities across Germany, Oberzell has witnessed a surge of asylum seekers and refugees in recent years — at first, mostly men from Gambia, Senegal, Cameroon, and Afghanistan; more recently, men, women and children from Ukraine have entered Oberzell in need of asylum.

The community in Oberzell has, for the most part, united in its support for the refugees. Rothenhaeusler’s old soccer coach emerged from retirement to host weekly practices and scrimmages. Other residents stepped in to show the ropes of riding public transportation or navigating municipal bureaucracy. Volunteers taught basic German.

Rothenhaeusler, now an assistant professor of statistics in the School of Humanities and Sciences at Stanford University, largely watched this effort from afar, first while completing his PhD in Zurich and then his postdoc at Berkeley. “I was somewhat separated from all of it, but I wanted to do my part,” he says. It was convenient, then, when the Stanford Immigration Policy Lab (IPL) reached out asking for help on a refugee-placement project called GeoMatch. “With academic research, there are usually a few steps between the work and its impact. With this project, there was a clear pathway to immediately effecting positive change in the world.”

Better Living Through Machine Learning
GeoMatch, partially funded by a Stanford HAI Hoffman-Yee Grant, is a machine learning tool designed to help placement officers match refugees with the communities where they’re most likely to thrive. The idea was first born when a team of researchers that included Jens Hainmueller, co-director of the Stanford Immigration Policy Lab and the Kimberly Glenn Professor and professor of political science in the School of Humanities & Sciences, met with U.S. government and nonprofit agencies that assist with refugee placement and integration. Conversation veered toward the challenge faced by placement officers. Though the resettlement process is rich with empirical questions — When are cities better for refugees and when are rural areas? Are homogeneous or diverse communities preferable? What local resources contribute to job placement? — none of these questions had been formally investigated. Placement officers leaned on experience and intuition more than anything else when finding new homes for refugees.