A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare

TL;DR

This paper develops a comprehensive framework for high-stakes algorithmic decision-making in the public sector, validated through an ethnographic case study in child-welfare, emphasizing trust, support for bureaucratic processes, and social ecological outcomes.

Contribution

It introduces the ADMAPS framework, integrating socio-technical factors in public sector algorithm design, and provides practical guidelines validated through real-world ethnographic research.

Findings

Need for strength-based, social ecological outcomes

Algorithms should support and augment human discretion

Trust at practitioner and bureaucratic levels is essential

Abstract

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.