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算法、人类与儿童保护系统中的种族差异:来自阿勒格尼家庭筛查工具的证据

Algorithms, Humans, and Racial Disparities in Child Protection Systems: Evidence From the Allegheny Family Screening Tool
Journal of Policy Analysis and Management · 2026 · Katherine Rittenhouse、Emily Putnam‐Hornstein、Rhema Vaithianathan

中文摘要

公平性担忧是算法在各类场景中获得采用的主要制约因素。我们探讨在儿童保护系统的背景下,向决策者提供预测性风险模型会如何影响种族差异。我们利用阿勒格尼家庭筛查工具(AFST)的实施,该工具旨在帮助儿童保护工作者决定将哪些虐待指控纳入调查。我们发现,AFST降低了涉及黑人儿童与白人儿童的转介案件在筛查决定和带离家庭率方面的差异。对假阳性率与假阴性率的分析表明,这种差异的缩小是在改善黑人儿童和白人儿童福祉的同时实现的。

Abstract

ABSTRACT Equity concerns are a primary constraint in the adoption of algorithms across various settings. We ask how providing decision‐makers with a predictive risk model affects racial disparities in the context of the child protection system. We exploit the implementation of the Allegheny Family Screening Tool (AFST), a tool that aims to help child protection workers decide which allegations of maltreatment to screen in for investigation. We find that the AFST reduced disparities in screening decisions and home removal rates for referrals involving Black versus White children. An analysis of false positive and false negative rates suggests that this reduction in disparities was achieved while improving welfare among both Black and White children.
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