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With the increasing sophistication of machine learning models, there aregrowing trends of developing model explanation techniques that focus on onlyone instance (local explanation) to ensure faithfulness to the original model.While these techniques provide accurate model interpretability on various dataprimitive (e.g., tabular, image, or text), a holistic Explainable ArtificialIntelligence (XAI) experience also requires a global explanation of the modeland dataset to enable sensemaking in different granularity. Thus, there is avast potential in synergizing the model explanation and visual analyticsapproaches. In this paper, we present MELODY, an interactive algorithm toconstruct an optimal global overview of the model and data behavior bysummarizing the local explanations using information theory. The result (i.e.,an explanation summary) does not require additional learning models,restrictions of data primitives, or the knowledge of machine learning from theusers. We also design MELODY UI, an interactive visual analytics system todemonstrate how the explanation summary connects the dots in various XAI tasksfrom a global overview to local inspections. We present three usage scenariosregarding tabular, image, and text classifications to illustrate how togeneralize model interpretability of different data. Our experiments show thatour approaches: (1) provides a better explanation summary compared to astraightforward information-theoretic summarization and (2) achieves asignificant speedup in the end-to-end data modeling pipeline.

Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together