使用机器学习支持儿童福利决策的概念框架
A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions
摘要：
人类服务系统做出影响社会中个人的关键决策。美国儿童福利系统做出这样的决定，从筛选热线报告的涉嫌虐待或忽视儿童保护调查，将儿童寄养，以返回儿童到永久的家庭环境。这些对儿童生活有影响的复杂决定取决于儿童福利决策者的判断。儿童福利机构一直在探索如何通过经验性的、基于数据的方法来支持这些决策，其中包括机器学习（ML）。本文描述了ML支持儿童福利决策的概念框架。ML框架指导儿童福利机构如何将ML可以解决的目标问题概念化;审查用于构建ML的可用管理数据;制定和开发ML规范，以反映相关人群和机构正在进行的干预措施;随着儿童福利背景，政策和实践的变化，部署，评估和监控ML。道德考量、利益攸关方参与以及避免常见陷阱是该框架影响力和成功的基础。从抽象到具体，我们描述了这个框架的一个应用程序，以支持儿童福利的决定。这个ML框架虽然以儿童福利为中心，但可以推广到解决其他公共政策问题。
英文摘要：
Human services systems make key decisions that impact individuals in the society. The U.S. child welfare system makes such decisions, from screening-in hotline reports of suspected abuse or neglect for child protective investigations, placing children in foster care, to returning children to permanent home settings. These complex and impactful decisions on children's lives rely on the judgment of child welfare decisionmakers. Child welfare agencies have been exploring ways to support these decisions with empirical, data-informed methods that include machine learning (ML). This paper describes a conceptual framework for ML to support child welfare decisions. The ML framework guides how child welfare agencies might conceptualize a target problem that ML can solve; vet available administrative data for building ML; formulate and develop ML specifications that mirror relevant populations and interventions the agencies are undertaking; deploy, evaluate, and monitor ML as child welfare context, policy, and practice change over time. Ethical considerations, stakeholder engagement, and avoidance of common pitfalls underpin the framework's impact and success. From abstract to concrete, we describe one application of this framework to support a child welfare decision. This ML framework, though child welfare-focused, is generalizable to solving other public policy problems.
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。
