Placement Algorithm

Airbel is piloting and scaling an innovative machine learning algorithm developed by the Stanford University Immigration Policy Lab that matches refugees in areas where they are likely to thrive when resettled.

Moving somewhere new is challenging under the best circumstances. It’s especially difficult for refugees, who are driven from their homes by conflict or major threats. Refugee resettlement offers one of the most transformative opportunities to those affected by conflict. When refugees are resettled in the United States, resettlement agencies like the International Rescue Committee determine where to send refugees and caseworkers have played a critical role in helping refugees adapt to new places.

The community where refugees are placed is generally dictated by need and availability. But data shows what common sense states: different people are likely to succeed in different circumstances. Enter the placement algorithm - a collaboration between Stanford and the Airbel Center that analyzes historical data on refugee demographics, local market conditions, individual preferences and outcomes to generate predictions that suggest an ideal location for resettled refugees. This actionable information can then be used to inform decisions about where to place refugees in the U.S. We ultimately aim to scale this approach to help identify where we can place refugees globally.