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

arXiv: https://arxiv.org/abs/2203.05169 · HTML: https://mlsanity.com/paper/2203.05169/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases](https://mlsanity.com/paper/2401.10535/) (2024)
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- [Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge](https://mlsanity.com/paper/2308.15700/) (2023)
- ["This is not a data problem": Algorithms and Power in Public Higher Education in Canada](https://mlsanity.com/paper/2403.13969/) (2024)
- [Riveter: Measuring Power and Social Dynamics Between Entities](https://mlsanity.com/paper/2312.09536/) (2023)
- [What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparedness](https://mlsanity.com/paper/2509.21868/) (2025)
- [ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality](https://mlsanity.com/paper/2402.18797/) (2024)
- [Beyond Predictive Algorithms in Child Welfare](https://mlsanity.com/paper/2403.05573/) (2024)

## Abstract (arXiv)

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.