  This paper introduces a new classification tool named Silas, which is built
to provide a more transparent and dependable data analytics service. A focus of
Silas is on providing a formal foundation of decision trees in order to support
logical analysis and verification of learned prediction models. This paper
describes the distinct features of Silas: The Model Audit module formally
verifies the prediction model against user specifications, the Enforcement
Learning module trains prediction models that are guaranteed correct, the Model
Insight and Prediction Insight modules reason about the prediction model and
explain the decision-making of predictions. We also discuss implementation
details ranging from programming paradigm to memory management that help
achieve high-performance computation.
