Giving Compass' Take:

• Jennifer Fei shares lessons from Immigration Policy Lab's matching algorithm tool to improve the way refugees are assigned to resettlement locations.

• How can the learnings from this project be broadly applied? What risks come from using AI to place refugees?  

• Learn more about responding to refugee crises. 

Around the world, wherever governments have received rising numbers of refugees and asylum seekers in recent years, policymakers have wrestled with how to successfully integrate newcomers into host societies. There’s an emerging consensus that place matters: whether or not refugees thrive may hinge on where they are resettled within the host country. A good match between person and place can be the difference between success and failure. But how do you make a good match? The exact formula might be impossible to pin down: the ways refugees’ personal characteristics interact with local conditions are intricate and nearly endless.

This is where artificial intelligence comes in. With an algorithm to find patterns in vast troves of data, governments and resettlement agencies can make the best matches possible for all resettled refugees.

Researchers at the Immigration Policy Lab (IPL) created this matching algorithm tool to improve the way refugees are assigned to resettlement locations. Using data on past refugees and their outcomes, the tool identifies the location where incoming refugees are most likely to thrive, based on their individual characteristics. Thanks to the generous support of The Rockefeller Foundation, IPL launched the first pilot test of the tool in Switzerland last year, and a U.S. pilot program is in development. We have also been working with additional partners in Europe and Latin America to explore implementing the matching tool in their own countries.

The breakthrough idea was only the first step. It’s a much bigger project to translate this innovation into everyday practice so that it actually improves the lives of refugees. Here are some of the key lessons we’ve learned so far.

  • Emphasize Human-Centered Design
  • Adapt to Different Countries
  • Align Shared Goals

Read the full article about lessons in scaling data science by Jennifer Fei at The Rockefeller Foundation.