  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.
