Designing Human-Centered Algorithms for the Public Sector A Case Study of the U.S. Child-Welfare System

dc.contributor.authorSaxena, Devansh
dc.date.accessioned2023-03-17T22:49:02Z
dc.date.available2023-03-17T22:49:02Z
dc.date.issued2023
dc.description.abstractThe 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 3) 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.en
dc.identifier.doi10.1145/3565967.3571759
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4627
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofCompanion Proceedings of the 2023 ACM International Conference on Supporting Group Work
dc.subjectchild-welfare system
dc.subjectdiscretion
dc.subjectbureaucracy
dc.subjectalgorithmic decision-making
dc.subjectcomputational narrative analysis
dc.titleDesigning Human-Centered Algorithms for the Public Sector A Case Study of the U.S. Child-Welfare Systemen
dc.typeText/Conference Paper
gi.citation.startPage66–68
gi.citations.count2
gi.citations.elementSeyun Kim, Jonathan Ho, Yinan Li, Bonnie Fan, Willa Yunqi Yang, Jessie Ramey, Sarah E. Fox, Haiyi Zhu, John Zimmerman, Motahhare Eslami (2024): Integrating Equity in Public Sector Data-Driven Decision Making: Exploring the Desired Futures of Underserved Stakeholders, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(8), doi:10.1145/3686905
gi.citations.elementSeyun Kim, Bonnie Fan, Willa Yunqi Yang, Jessie Ramey, Sarah E Fox, Haiyi Zhu, John Zimmerman, Motahhare Eslami (2024): Public Technologies Transforming Work of the Public and the Public Sector, In: Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work, doi:10.1145/3663384.3663407
gi.conference.locationHilton Head, SC, USA

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