论文标题
为公共部门设计以人为本的算法：美国儿童福利系统的案例研究
Designing Human-Centered Algorithms for the Public Sector: A Case Study of the U.S. Child-Welfare System
论文作者
论文摘要
美国儿童福利系统（CWS）越来越多地寻求通过采用算法以效率，降低成本和创新为中心的私营部门的商业模式。这些数据驱动的系统据称改善了决策，但是，公共部门在算法决策的技术，理论，文化和社会意义方面构成了自己的挑战。为了填补这些空白，我的论文包括四项研究：1）案例工作者如何与算法相互作用，在日常的日常自由工作中，2）算法决策对实践，组织和街头决策的性质的影响，3）Casenotes如何帮助洞察力的洞察力，并如何帮助洞察力，并在洞察力上进行详尽的启动，并在4个洞察力的模式，并在4个洞察力的模式，并在4个洞察力的模式。难以量化但直接影响家庭和街头决策的约束和风险因素。这项研究的目的是研究系统性差异和设计，并开发算法系统，这些系统以实践理论为中心，并提高了人类酌情工作的质量。这些研究为公共部门的以人为本算法设计提供了可行的步骤。
The U.S. Child Welfare System (CWS) is increasingly seeking to emulate business models of the private sector centered in efficiency, cost reduction, and innovation through the adoption of algorithms. These data-driven systems purportedly improve decision-making, however, the public sector poses its own set of challenges with respect to the technical, theoretical, cultural, and societal implications of algorithmic decision-making. To fill these gaps, my dissertation comprises four studies that examine: 1) how caseworkers interact with algorithms in their day-to-day discretionary work, 2) the impact of algorithmic decision-making on the nature of practice, organization, and street-level decision-making, 3) how casenotes can help unpack patterns of invisible labor and contextualize decision-making processes, and 4) how casenotes can help uncover deeper systemic constraints and risk factors that are hard to quantify but directly impact families and street-level decision-making. My goal for this research is to investigate systemic disparities and design and develop algorithmic systems that are centered in the theory of practice and improve the quality of human discretionary work. These studies have provided actionable steps for human-centered algorithm design in the public sector.
