Predictive Risk Scores in the Public Sector: Experimental Evidence from Child-Protection Investigations
NBER Working Papers · 2026 · E. Jason Baron、Arkadev Ghosh、Richard Lombardo
中文摘要
公共部门的许多决策都需要在不确定性下分配稀缺的注意力资源。我们考察算法风险评估能否改善儿童保护决策；在此类决策中，主管人员需要决定哪些案件需要更严密的审查。在北安普顿县开展的一项随机评估中，研究覆盖了14个月内的4,752起儿童转介案件，主管人员在标准案件记录之外还获得了算法风险评分。获取该评分后，预测风险最高的儿童被安置于寄养家庭和获得服务的情况有所增加，而风险较低案件的变化很小；后续虐待转介也随之减少。我们未发现该评分扩大了决策或结果中种族差距的证据，这表明算法可以在保留人工裁量权的同时改善资源投放的针对性。
Abstract
Many public-sector decisions require allocating scarce attention under uncertainty. We examine whether algorithmic risk assessments improve child-protection decisions, where supervisors decide which cases need closer scrutiny. In a randomized evaluation of 4,752 child referrals over 14 months in Northampton County, supervisors received an algorithmic risk score alongside standard case records. Access to the score increased foster-care placements and services for children at highest predicted risk, with little change for lower-risk cases, and it reduced subsequent maltreatment referrals. We find no evidence that the score widened racial disparities in decisions or outcomes, suggesting algorithms can improve targeting while preserving human discretion.