  Machine learning (ML) is being applied to a diverse and ever-growing set of
domains. In many cases, domain experts -- who often have no expertise in ML or
data science -- are asked to use ML predictions to make high-stakes decisions.
Multiple ML usability challenges can appear as result, such as lack of user
trust in the model, inability to reconcile human-ML disagreement, and ethical
concerns about oversimplification of complex problems to a single algorithm
output. In this paper, we investigate the ML usability challenges present in
the domain of child welfare screening through a series of collaborations with
child welfare screeners, which included field observations, interviews, and a
formal user study. Through our collaborations, we identified four key ML
challenges, and honed in on one promising ML augmentation tool to address them
(local factor contributions). We also composed a list of design considerations
to be taken into account when developing future augmentation tools for child
welfare screeners and similar domain experts.
