算法已经渗透到整个民间政府和社会中，它们被用来对人类生活做出高风险的决定。在本文中，我们首先开发了一个适用于公共部门的算法决策框架（ADMAPS），通过综合人机交互（HCI）、科技研究（STS）和公共管理（PA）等领域的不同工作，反映了人类自由裁量权、官僚程序和算法决策之间复杂的社会技术互动关系。然后，我们应用ADMAPS框架对一个深入的、为期8个月的人种学案例研究进行了定性分析，该机构为美国中西部的约900个家庭和1300名儿童提供服务，日常使用的算法。总的来说，我们发现有必要把重点放在以社会生态框架为中心的基于力量的算法结果上。此外，算法系统需要支持现有的官僚程序，增强人类的判断力，而不是取代它。最后，算法系统的集体买入需要在实践者和官僚层面对目标结果的信任。作为我们研究的结果，我们为儿童福利系统中高风险的算法决策工具的设计提出了指导方针，更普遍的是，在公共部门。我们从经验上验证了从理论上得出的ADMAPS框架，以证明它如何有助于系统地对公共部门的算法设计做出务实的决定。
原文标题：A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare
原文：
Algorithms have permeated throughout civil government and society, where they are being used to make high-stakes decisions about human lives. In this paper, we first develop a cohesive framework of algorithmic decision-making adapted for the public sector (ADMAPS) that reflects the complex socio-technical interactions between human discretion, bureaucratic processes, and algorithmic decision-making by synthesizing disparate bodies of work in the fields of Human-Computer Interaction (HCI), Science and Technology Studies (STS), and Public Administration (PA). We then applied the ADMAPS framework to conduct a qualitative analysis of an in-depth, eight-month ethnographic case study of the algorithms in daily use within a child-welfare agency that serves approximately 900 families and 1300 children in the mid-western United States. Overall, we found there is a need to focus on strength-based algorithmic outcomes centered in social ecological frameworks. In addition, algorithmic systems need to support existing bureaucratic processes and augment human discretion, rather than replace it. Finally, collective buy-in in algorithmic systems requires trust in the target outcomes at both the practitioner and bureaucratic levels. As a result of our study, we propose guidelines for the design of high-stakes algorithmic decision-making tools in the child-welfare system, and more generally, in the public sector. We empirically validate the theoretically derived ADMAPS framework to demonstrate how it can be useful for systematically making pragmatic decisions about the design of algorithms for the public sector
A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare.pdf