Matching algorithms have been proven effective in a wide range of policy areas. Following the “refugee crisis”, initiatives based on a simple algorithm have been implemented to assign refugees to hosts (known as “Airbnb for refugees”) and local companies (such as LinkedIn’s Refugee Talent). More generally, matching-algorithms can serve the interests of migrants—following their ranked options—and allow states to oversee the type of people they admit according to state preferences. It can be part of a global responsibility-sharing mechanism.
The project explores whether matching theories can help preselect future citizens (through “citizenship-track entry” in immigration policy) and facilitate access to citizenship by encouraging social interaction between migrants and citizens? Technologically, there are questions of design—a market-design approach that matches states and newcomers, or a data-driven approach that employs machine learning data to optimize outcome? And how are “success” and “failure” measured? Normatively, questions include: what are the justifications for selecting citizens by matching algorithms, and what are the risks? What should the goals be of such a system? Which criteria should be used? And how does selecting citizens via matching algorithms differ from selecting labor migrants or refugees?