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

TL;DR

This paper presents a data-driven simulation model to forecast the impact of a predictive intervention on racial equity and youth in foster care in New York, highlighting potential limitations of such algorithms.

Contribution

It introduces a novel analytic pipeline for simulating youth trajectories in foster care to evaluate policy impacts before implementation.

Findings

The predictive model may not improve racial equity.

The model could increase the number of youth in foster care.

Simulation highlights potential unintended consequences of algorithmic interventions.

Abstract

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.