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Human-in-the-loop data analysis applications necessitate greater transparencyin machine learning models for experts to understand and trust their decisions.To this end, we propose a visual analytics workflow to help data scientists anddomain experts explore, diagnose, and understand the decisions made by a binaryclassifier. The approach leverages "instance-level explanations", measures oflocal feature relevance that explain single instances, and uses them to build aset of visual representations that guide the users in their investigation. Theworkflow is based on three main visual representations and steps: one based onaggregate statistics to see how data distributes across correct / incorrectdecisions; one based on explanations to understand which features are used tomake these decisions; and one based on raw data, to derive insights onpotential root causes for the observed patterns. The workflow is derived from along-term collaboration with a group of machine learning and healthcareprofessionals who used our method to make sense of machine learning models theydeveloped. The case study from this collaboration demonstrates that theproposed workflow helps experts derive useful knowledge about the model and thephenomena it describes, thus experts can generate useful hypotheses on how amodel can be improved.

A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations