Machine learning models can make basic errors that are easily hidden within vast amounts of data. Such errors often run counter to human intuition referred to as "common sense". We thereby seek to characterize common sense for data-driven models, and quantify the extent to which a model has learned common sense. We propose a framework that integrates logic-based methods with statistical inference to derive common sense rules from a model's training data without supervision. We further show how t…