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 \textit{human discretion}, \textit{bureaucratic processes}, and \textit{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.
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
University of Minnesota（明尼苏达大学）；Carnegie Mellon University（卡内基梅隆大学）；Singapore Management University（新加坡管理大学）；University of California, Berkeley（加州大学伯克利分校）；University of Southern California（南加州大学）
Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions"
这是我们的论文“儿童福利工作者如何减少种族差异的决策”的扩展分析，它着眼于阿勒格尼家庭筛选工具中的种族差异，这是一种用于帮助儿童福利工作者决定阿勒格尼县儿童福利机构（CYF）应该调查哪些家庭的算法。2022年4月27日，阿勒格尼县CYF向我们发送了更新的数据集和预处理步骤。在本文的扩展分析中，我们展示了使用新数据和预处理重新运行本文中所有定量分析的结果。我们发现，我们在论文中的主要发现对数据和预处理的变化具有鲁棒性。特别是，Allegheny Family Screening Tool本身会比工人做出更多种族差异的决定，工人使用该工具来减少这些算法差异。一些微小的结果发生了变化，包括我们的论文报道的AFST实施前后的筛选率略有增加。
The Promises and Perils of using LLMs for Effective Public Services
University of Toronto（多伦多大学）；Georgia Institute of Technology（佐治亚理工学院）；Child Welfare Institute, CAS of Toronto（多伦多儿童福利研究所）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
