  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 that present
in the domain of child welfare screening through a series of collaborations
with child welfare screeners. Following the iterative design process between
the ML scientists, visualization researchers, and domain experts (child
screeners), we first identified four key ML challenges and honed in on one
promising explainable ML technique to address them (local factor
contributions). Then we implemented and evaluated our visual analytics tool,
Sibyl, to increase the interpretability and interactivity of local factor
contributions. The effectiveness of our tool is demonstrated by two formal user
studies with 12 non-expert participants and 13 expert participants
respectively. Valuable feedback was collected, from which we composed a list of
design implications as a useful guideline for researchers who aim to develop an
interpretable and interactive visualization tool for ML prediction models
deployed for child welfare screeners and other similar domain experts.
