  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 within a host country to which the
refugee or asylum seeker is assigned. Recent research has proposed and
implemented algorithms that assign refugees and asylum seekers to geographic
locations in a manner that maximizes the average employment across all arriving
refugees. While these algorithms can have substantial overall positive impact,
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. Thus motivated, we develop a simple and
interpretable framework for incorporating group fairness into the dynamic
refugee assignment problem. In particular, the framework can flexibly
incorporate many existing and future definitions of group fairness from the
literature (e.g., maxmin, randomized, and proportionally-optimized
within-group). Equipped with our framework, we propose two bid-price algorithms
that maximize overall employment while simultaneously yielding provable group
fairness guarantees. Through extensive numerical experiments using various
definitions of group fairness and real-world data from the U.S. and the
Netherlands, we show that our algorithms can yield substantial improvements in
group fairness compared to an offline benchmark fairness constraints, with only
small relative decreases ($\approx$ 1%-5%) in global performance.
