  Machine learning systems are ubiquitous in various kinds of digital
applications and have a huge impact on our everyday life. But a lack of
explainability and interpretability of such systems hinders meaningful
participation by people, especially by those without a technical background.
Interactive visual interfaces (e.g., providing means for manipulating
parameters in the user interface) can help tackle this challenge. In this paper
we present PreCall, an interactive visual interface for ORES, a machine
learning-based web service for Wikimedia projects such as Wikipedia. While ORES
can be used for a number of settings, it can be challenging to translate
requirements from the application domain into formal parameter sets needed to
configure the ORES models. Assisting Wikipedia editors in finding damaging
edits, for example, can be realized at various stages of automatization, which
might impact the precision of the applied model. Our prototype PreCall attempts
to close this translation gap by interactively visualizing the relationship
between major model metrics (recall, precision, false positive rate) and a
parameter (the threshold between valuable and damaging edits). Furthermore,
PreCall visualizes the probable results for the current model configuration to
improve the human's understanding of the relationship between metrics and
outcome when using ORES. We describe PreCall's components and present a use
case that highlights the benefits of our approach. Finally, we pose further
research questions we would like to discuss during the workshop.
