Group fairness in dynamic refugee assignment

Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geographic location to which the refugee or asylum seeker is assigned. In the past few years, innovations in analytics have given rise to machine learning (ML) models that predict integration outcomes using personal characteristics. With these ML models, recent research has proposed and implemented algorithms that assign refugees and asylum seekers to geographic locations in a manner that maximizes the average employment. While these algorithms can have substantial overall positive impact (up to 50% increases in average employment rate compared with current practice), using data from two industry collaborators we show that the impact of these algorithms can vary widely across key subgroups based on country of origin, age, or educational background.