Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis

Abstract

Caseworkers are trained to write detailed narratives about families in Child-Welfare (CW) which informs collaborative high-stakes decision-making. Unlike other administrative data, these narratives offer a more credible source of information with respect to workers' interactions with families as well as underscore the role of systemic factors in decision-making. SIGCHI researchers have emphasized the need to understand human discretion at the street-level to be able to design human-centered algorithms for the public sector. In this study, we conducted computational text analysis of casenotes at a child-welfare agency in the midwestern United States and highlight patterns of invisible street-level discretionary work and latent power structures that have direct implications for algorithm design. Casenotes offer a unique lens for policymakers and CW leadership towards understanding the experiences of on-the-ground caseworkers. As a result of this study, we highlight how street-level discretionary work needs to be supported by sociotechnical systems developed through worker-centered design. This study offers the first computational inspection of casenotes and introduces them to the SIGCHI community as a critical data source for studying complex sociotechnical systems.

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Cited by in corpus (9)

• Are We Asking the Right Questions?: Designing for Community Stakeholders' Interactions with AI in Policing
• How to Train a (Bad) Algorithmic Caseworker: A Quantitative Deconstruction of Risk Assessments in Child-Welfare
• "This is not a data problem": Algorithms and Power in Public Higher Education in Canada
• ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
• A Human-Centered Review of Algorithms in Homelessness Research
• Riveter: Measuring Power and Social Dynamics Between Entities
• The Datafication of Care in Public Homelessness Services
• Designing Human-Centered Algorithms for the Public Sector: A Case Study of the U.S. Child-Welfare System
• Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints