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Algorithms have permeated throughout civil government and society, where theyare being used to make high-stakes decisions about human lives. In this paper,we first develop a cohesive framework of algorithmic decision-making adaptedfor the public sector (ADMAPS) that reflects the complex socio-technicalinteractions between \textit{human discretion}, \textit{bureaucraticprocesses}, and \textit{algorithmic decision-making} by synthesizing disparatebodies of work in the fields of Human-Computer Interaction (HCI), Science andTechnology Studies (STS), and Public Administration (PA). We then applied theADMAPS framework to conduct a qualitative analysis of an in-depth, eight-monthethnographic case study of the algorithms in daily use within a child-welfareagency that serves approximately 900 families and 1300 children in themid-western United States. Overall, we found there is a need to focus onstrength-based algorithmic outcomes centered in social ecological frameworks.In addition, algorithmic systems need to support existing bureaucraticprocesses and augment human discretion, rather than replace it. Finally,collective buy-in in algorithmic systems requires trust in the target outcomesat both the practitioner and bureaucratic levels. As a result of our study, wepropose guidelines for the design of high-stakes algorithmic decision-makingtools in the child-welfare system, and more generally, in the public sector. Weempirically validate the theoretically derived ADMAPS framework to demonstratehow it can be useful for systematically making pragmatic decisions about thedesign 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