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.

翻译：确保难民和寻求庇护者在接收国蓬勃发展（例如找到工作）是一项深远的人道主义目标，而就业的主要驱动力是难民或寻求庇护者被分配到的接收国地理区域。近年研究提出并实施了多种算法，通过优化所有抵达难民的平均就业率来分配其地理区域。尽管这些算法总体能产生显著的积极影响，但基于两家行业合作方的数据，我们发现其效果可能因原籍国、年龄或教育背景等关键子群体而存在巨大差异。为此，我们开发了一个简洁且可解释的框架，将群体公平性纳入动态难民分配问题中。该框架能灵活整合文献中现有及未来提出的多种群体公平定义（例如最大最小公平、随机公平和组内比例优化公平）。基于此框架，我们提出了两种出价算法，能在最大化整体就业率的同时提供可证明的群体公平保障。通过使用美国与荷兰的真实数据及多种群体公平定义进行大量数值实验，我们证明：与离线基准公平约束相比，本算法能显著提升群体公平性，而全局性能仅小幅下降（约1%-5%）。