  While there are many machine learning methods to classify and cluster
sequences, they fail to explain what are the differences in groups of sequences
that make them distinguishable. Although in some cases having a black box model
is sufficient, there is a need for increased explainability in research areas
focused on human behaviors. For example, psychologists are less interested in
having a model that predicts human behavior with high accuracy and more
concerned with identifying differences between actions that lead to divergent
human behavior. This paper presents techniques for understanding differences
between classes of discrete sequences. Approaches introduced in this paper can
be utilized to interpret black box machine learning models on sequences. The
first approach compares k-gram representations of sequences using the
silhouette score. The second method characterizes differences by analyzing the
distance matrix of subsequences. As a case study, we trained black box
supervised learning methods to classify sequences of GitHub teams and then
utilized our sequence analysis techniques to measure and characterize
differences between event sequences of teams with bots and teams without bots.
In our second case study, we classified Minecraft event sequences to infer
their high-level actions and analyzed differences between low-level event
sequences of actions.
