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.
翻译:美国儿童福利体系(CWS)日益寻求效仿私营部门在效率、降低成本和通过采用算法进行创新方面的商业模式。 这些数据驱动系统据称改善了决策,然而,公共部门在算法决策的技术、理论、文化和社会影响方面提出了其自身的一系列挑战。为了填补这些空白,我的论文包括四项研究:(1) 个案工作者如何在日常自由裁量工作中与算法相互作用;(2) 算法决策对实践、组织和街头决策性质的影响;(3) 案例说明如何有助于解开无形劳工模式和背景化决策进程;(4) 案例说明如何有助于发现更深层次的系统性限制和风险因素,这些限制和因素难以量化,但直接影响家庭和街头决策。我的研究目标是调查系统性差异,设计并发展以实践理论为核心的算法系统,提高人类自由裁量工作的质量。这些研究为公共部门以人为中心的算法设计提供了可操作的步骤。