A Data-Driven Simulation of the New York State Foster Care System

A Data-Driven Simulation of the New York State Foster Care System
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纽约州寄养系统的数据驱动模拟

DOI:
10.1145/3531146.3533165
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发表时间:
2022
期刊:
and Transparency
影响因子:
--
通讯作者:
Joseph, Kenneth
Joseph, Kenneth
中科院分区:
--
文献类型:
--
作者:
Du, Yuhao;Ionescu, Stefania;Sage, Melanie;Joseph, Kenneth

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我们引入了一个分析管道,通过纽约州寄养系统建模和模拟青年轨迹。我们这样做的目标是预测拟议的干预措施可能会如何影响寄养系统实现其既定目标的能力,这些干预措施实际上是实施和影响成千上万的青年的生活之前。在这里,我们专注于该系统的两个具体目标:种族平等,以及最近由2018年家庭第一预防服务法(FFPSA)编纂的重点是让所有青少年远离寄养。我们还专注于一个特定的潜在干预-预测模型,在以前的工作中提出并在美国其他地方实施,旨在确定一名青少年是否需要照顾。我们用我们的方法来探讨如何在纽约实施这种预测模型将影响种族平等和照顾青年的数量。虽然我们的研究结果,在任何模拟模型,最终依赖于建模假设,我们发现的证据表明,该模型不一定会实现这两个目标。首先,我们的目标是进一步促进数据驱动模拟的使用,以帮助理解算法干预在公共系统中的后果。
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 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.
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