论文标题
纽约州寄养系统的数据驱动模拟
A Data-Driven Simulation of the New York State Foster Care System
论文作者
论文摘要
我们引入了一条分析管道，以通过纽约州寄养系统模拟和模拟青年轨迹。我们这样做的目标是预测拟议的干预措施如何影响寄养系统实现其指定目标的能力\ emph {在实际实施这些干预措施之前，并影响了数千名青年的生活}。在这里，我们重点介绍了该系统的两个具体陈述的目标：种族平等，以及最近由2018年《家庭First Faceenty Services Act》（FFPSA）编纂的，重点是使所有年轻人都远离寄养。我们还专注于一种特定的潜在干预措施 - 一种预测模型，在先前的工作中提出，并在美国其他地方实施，该模型旨在确定青年是否需要护理。我们使用我们的方法来探索这种预测模型在纽约的实施将如何影响种族平等和护理青年的数量。尽管我们的发现，例如在任何模拟模型中，最终都依赖建模假设，但我们发现证据表明该模型不一定会实现这两个目标。因此，我们主要旨在进一步促进使用数据驱动的模拟，以帮助了解公共系统中算法干预的后果。
We introduce an analytic pipeline to model and simulate youth trajectories through the New York state foster care system. Our goal in doing so is to forecast how proposed interventions may impact the foster care system's ability to achieve it's stated goals \emph{before these interventions are actually implemented and impact the lives of thousands of youth}. Here, we focus on two specific stated goals of the system: racial equity, and, as codified most recently by the 2018 Family First Prevention Services Act (FFPSA), a focus on keeping all youth out of foster care. We also focus on one specific potential intervention -- a predictive model, proposed in prior work and implemented elsewhere in the U.S., which aims to determine whether or not a youth is in need of care. We use our method to explore how the implementation of this predictive model in New York would impact racial equity and the number of youth in care. While our findings, as in any simulation model, ultimately rely on modeling assumptions, we find evidence that the model would not necessarily achieve either goal. Primarily, then, we aim to further promote the use of data-driven simulation to help understand the ramifications of algorithmic interventions in public systems.
