算法已經滲透到整個民間政府和社會中，它們被用來對人類生活做出高風險的決定。在本文中，我們首先開發了一個适用于公共部門的算法決策架構（ADMAPS），通過綜合人機互動（HCI）、科技研究（STS）和公共管理（PA）等領域的不同工作，反映了人類自由裁量權、官僚程式和算法決策之間複雜的社會技術互動關系。然後，我們應用ADMAPS架構對一個深入的、為期8個月的人種學案例研究進行了定性分析，該機構為美國中西部的約900個家庭和1300名兒童提供服務，日常使用的算法。總的來說，我們發現有必要把重點放在以社會生态架構為中心的基于力量的算法結果上。此外，算法系統需要支援現有的官僚程式，增強人類的判斷力，而不是取代它。最後，算法系統的集體買入需要在實踐者和官僚層面對目标結果的信任。作為我們研究的結果，我們為兒童福利系統中高風險的算法決策工具的設計提出了指導方針，更普遍的是，在公共部門。我們從經驗上驗證了從理論上得出的ADMAPS架構，以證明它如何有助于系統地對公共部門的算法設計做出務實的決定。
原文标題：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