Designing Human-Centered Algorithms for the Public Sector A Case Study of the U.S. Child-Welfare System Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.1145/3565967.3571759
· OA: W4311408396
The U.S. Child Welfare System (CWS) is increasingly seeking to emulate\nbusiness models of the private sector centered in efficiency, cost reduction,\nand innovation through the adoption of algorithms. These data-driven systems\npurportedly improve decision-making, however, the public sector poses its own\nset of challenges with respect to the technical, theoretical, cultural, and\nsocietal implications of algorithmic decision-making. To fill these gaps, my\ndissertation comprises four studies that examine: 1) how caseworkers interact\nwith algorithms in their day-to-day discretionary work, 2) the impact of\nalgorithmic decision-making on the nature of practice, organization, and\nstreet-level decision-making, 3) how casenotes can help unpack patterns of\ninvisible labor and contextualize decision-making processes, and 4) how\ncasenotes can help uncover deeper systemic constraints and risk factors that\nare hard to quantify but directly impact families and street-level\ndecision-making. My goal for this research is to investigate systemic\ndisparities and design and develop algorithmic systems that are centered in the\ntheory of practice and improve the quality of human discretionary work. These\nstudies have provided actionable steps for human-centered algorithm design in\nthe public sector.\n
