Child welfare (CW) agencies use risk assessment tools as a means to achieve evidence-based, consistent, and unbiased decision-making. These risk assessments act as data collection mechanisms and have further evolved into algorithmic systems in recent years. Moreover, several of these algorithms have reinforced biased theoretical constructs and predictors because of the easy availability of structured assessment data. In this study, we critically examine the Washington Assessment of Risk Model (WARM), a prominent risk assessment tool that has been adopted by over 30 states in the United States and has been repurposed into more complex algorithms. We compared WARM against the narrative coding of casenotes written by caseworkers who used WARM. We found significant discrepancies between the casenotes and WARM data where WARM scores did not not mirror caseworkers' notes about family risk. We provide the SIGCHI community with some initial findings from the quantitative de-construction of a child-welfare algorithm.

翻译：儿童福利机构利用风险评估工具作为实现循证、一致和不偏不倚决策的手段,这些风险评估作为数据收集机制,近年来进一步演变为算法系统,此外,由于容易获得结构化评估数据,其中若干算法加强了偏颇的理论结构和预测数据;在这项研究中,我们认真审查了华盛顿风险评估模型(WARM),这是美国30多个州采用的一个突出的风险评估工具,并被重新定位为更复杂的算法。我们比较了WARM与使用WARM案务人员所写案例说明的叙述性编码。我们发现,在WARM案注和WARM数据之间有重大差异,因为WARM得分没有反映个案工作者关于家庭风险的说明。我们向SIGCHI社区提供了从定量拆译儿童福利算法中得出的一些初步结果。