论文标题
如何培训（不良）算法的案例工作者：对儿童福利的风险评估的定量解构
How to Train a (Bad) Algorithmic Caseworker: A Quantitative Deconstruction of Risk Assessments in Child-Welfare
论文作者
论文摘要
儿童福利（CW）机构使用风险评估工具作为实现循证，一致和公正决策的手段。这些风险评估是数据收集机制，近年来进一步发展为算法系统。此外，由于结构化评估数据的可用性，这些算法中有几种增强了理论结构和预测因子的偏见。在这项研究中，我们批判性地研究了华盛顿风险模型评估（温暖），这是一种突出的风险评估工具，已被美国30多个州采用，并已将其重新用于更复杂的算法。我们将温暖与使用温暖的案例工作者撰写的卡塞诺特人的叙事编码进行了比较。我们发现Casenotes和温暖的数据之间存在很大的差异，而温暖的分数并没有反映出案例工作者对家庭风险的注意事项。我们为Sigchi社区提供了来自儿童福利算法的定量解构建的一些初步发现。
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.
