美国儿童福利系统中使用的以人为本的算法审查
A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System
摘要：
美国儿童福利系统（CWS）负责改善寄养青年的成果;然而，他们负担过重，资金不足。为了克服这一限制，一些州已经转向算法决策系统，以降低成本，并确定更好的流程来改善CWS的结果。使用以人为中心的算法设计方法，我们综合了50篇关于CWS中使用的计算系统的同行评审出版物，以评估它们是如何开发的，所使用的预测因子的共同特征以及目标结果。我们发现大多数文献都集中在风险评估模型上，但没有考虑理论方法（例如，儿童-养父母匹配）也不是个案工作者的观点（例如，案例注释）。因此，未来的算法应该努力是上下文感知和理论上的强大，结合过去的研究确定的显着因素。我们为HCI社区提供研究途径，以开发以人为本的算法，将注意力转向CWS更公平的结果。
英文摘要：
The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
