  The 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 4) 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.
