Refugee Resettlement Via AI
As the world faces its largest crisis of displaced people since World War II, a new algorithm developed by Stanford researchers could help countries resettle refugees in a way that boosts their employment success and overall integration.
The group, headed by Stanford’s Immigration Policy Lab, utilized a machine learning algorithm to analyze historical data on refugee resettlement in the United States and Switzerland. They found that the refugees’ eventual economic self-sufficiency depended on a combination of their individual characteristics, such as education level and knowledge of English, and where they were resettled within the country. It turned out that refugees with particular backgrounds or skills achieved better outcomes in some locations than others.
The algorithm assigned placements for refugees that they project would increase their chances of finding employment by roughly 40% to 70% compared with how the refugees actually fared, according to the new study, published Jan 18 in Science. “As one looks at the refugee crisis globally, it’s clear that it’s not going away any time soon and that we need research-based policies to navigate through it,” says Jeremy Weinstein, a professor of political science at Stanford and a co-author of the study. “Our hope is to generate a policy conversation about the processes governing the resettlement of refugees, not just on the national level in the United States but internationally as well.”
In recent years, a record number of people have been displaced as a result of war, persecution and other human rights violations, surpassing the numbers seen after World War II. Often, countries that resettle refugees in their communities do so either somewhat randomly or according to local capacity of hosting communities at the time of refugees’ arrival. In the United States, refugees who have family members at a particular location are directed to join them there. But refugees without preexisting ties are free to be sent to various locations, and current approaches do not match them to locations where the evidence suggests it would be easiest for them to integrate.
“Our motivation was to bring the best of cutting-edge social science to an area of high policy priority that needs innovation but, because of the limited resources and challenges of navigating large numbers, has not been able to innovate from within,” Weinstein says.
The group found that if the algorithm had selected locations for refugees’ resettlement, the average employment rate among those refugees would have been roughly 41% higher.
The team went through the same process with data from asylum seekers who had been resettled in Switzerland between 1999 and 2013. They predicted the employment rate would have been 73% higher among asylum seekers who arrived in 2013 if they had been assigned to the places the algorithm identified as optimal.
The group said they still need to confirm the algorithm’s predictions through prospective tests that implement this approach in real time. The research team is now developing a number of pilot programs in partnership with governments and resettlement agencies to test the algorithm’s power. To support the Immigration Policy Lab, check out their site.
