论文标题
在基于机器学习的匹配中实现难民安置的权衡取舍
Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement
论文作者
论文摘要
瑞士国家移民秘书处最近宣布了一个试点项目，用于基于机器的基于机器学习的任务，以进行难民安置。这种方法有可能大大提高瑞士难民的总体就业率。但是，当前提出的方法忽略了家庭的偏好。在本文中，我们以这项先前的工作为基础，并提出了两种匹配机制，这些机制还考虑了家庭对位置的偏好。第一种机制是防策略的，而第二个机制不是策略性的，而是实现了更高的家庭福利。重要的是，我们将这两种机制参数化，从而为安置官员提供了精确控制如何与家庭福利进行交易，以与整体就业成功。关于合成数据的初步模拟表明，即使对难民的总体就业率只有很小的损失，这两种机制也可以显着增加家庭福利。
The Swiss State Secretariat for Migration recently announced a pilot project for a machine learning-based assignment process for refugee resettlement. This approach has the potential to substantially increase the overall employment rate of refugees in Switzerland. However, the currently proposed method ignores families' preferences. In this paper, we build on this prior work and propose two matching mechanisms that additionally take families' preferences over locations into account. The first mechanism is strategyproof while the second is not but achieves higher family welfare. Importantly, we parameterize both mechanisms, giving placement officers precise control how to trade off family welfare against overall employment success. Preliminary simulations on synthetic data show that both mechanisms can significantly increase family welfare even with only a small loss on the overall employment rate of refugees.
