  The algorithmic fairness of predictive analytic tools in the public sector
has increasingly become a topic of rigorous exploration. While instruments
pertaining to criminal recidivism and academic admissions, for example, have
garnered much attention, the predictive instruments of Child Welfare
jurisdictions have received considerably less attention. This is in part
because comparatively few such instruments exist and because even fewer have
been scrutinized through the lens of algorithmic fairness. In this work, we
seek to address both of these gaps. To this end, a novel classification
algorithm for predicting reunification success within Oregon Child Welfare is
presented, including all of the relevant details associated with building such
an instrument. The purpose of this tool is to maximize the number of stable
reunifications and identify potentially unstable reunifications which may
require additional resources and scrutiny. Additionally, because the
algorithmic fairness of the resulting tool, if left unaltered, is
unquestionably lacking, the utilized procedure for mitigating such unfairness
is presented, along with the rationale behind each difficult and unavoidable
choice. This procedure, though similar to other post-processing group-specific
thresholding methods, is novel in its use of a penalized optimizer and
contextually requisite subsampling. These novel methodological components yield
a rich and informative empirical understanding of the trade-off continuum
between fairness and accuracy. As the developed procedure is generalizable
across a variety of group-level definitions of algorithmic fairness, as well as
across an arbitrary number of protected attribute levels and risk thresholds,
the approach is broadly applicable both within and beyond Child Welfare.
