论文标题
算法辅助决策的异质性：虐待儿童的案例研究热线筛查
Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening
论文作者
论文摘要
算法风险评估工具现在在刑事司法和人类服务等公共部门领域中很普遍。这些工具旨在使用行政系统中捕获的丰富而复杂的数据系统地帮助决策者。在这项研究中，我们研究了在现实世界虐待儿童筛查用例筛查的情况下，工人决策与算法风险评分之间的一致性的异质性来源。具体来说，我们专注于与工人经验相关的异质性。我们发现，即使我们控制观察到的算法风险评分和其他案例特征，高级工人也更有可能在转诊中进行调查。我们还观察到，与算法的风险评分相比，经验较低的工人的决定与在引入工具之前具有决策经验的高级工人的决定更加紧密地保持一致。在筛查决策随着儿童种族而异，我们找不到在工人经验和筛查决策之间关系中种族差异的证据。我们的发现表明，对于机构和系统设计师来说，在将算法引入诸如儿童福利呼叫筛查之类的较高员工营业额设置时，考虑保留机构知识的方法很重要。
Algorithmic risk assessment tools are now commonplace in public sector domains such as criminal justice and human services. These tools are intended to aid decision makers in systematically using rich and complex data captured in administrative systems. In this study we investigate sources of heterogeneity in the alignment between worker decisions and algorithmic risk scores in the context of a real world child abuse hotline screening use case. Specifically, we focus on heterogeneity related to worker experience. We find that senior workers are far more likely to screen in referrals for investigation, even after we control for the observed algorithmic risk score and other case characteristics. We also observe that the decisions of less-experienced workers are more closely aligned with algorithmic risk scores than those of senior workers who had decision-making experience prior to the tool being introduced. While screening decisions vary across child race, we do not find evidence of racial differences in the relationship between worker experience and screening decisions. Our findings indicate that it is important for agencies and system designers to consider ways of preserving institutional knowledge when introducing algorithms into high employee turnover settings such as child welfare call screening.
