Designing a Glyph-Based Polar Chart to Interpret the Results of Machine Learning Models

Explainable artificial intelligence practices can support data scientists in interpreting the results of machine learning (ML) models. However, current practices require effortful and time-consuming coding to compare explanations that either relate to different ML models of the same data, or that are generated with different computational methods. We report the development of a glyph-based polar chart (GPC), designed to support a more comprehensive interpretation of the results of ML models by allowing these comparisons. The results from our user experience evaluation indicated that the proposed GPC supported data scientists in identifying the most relevant model variables, comparing different explanation methods, and performing logic reviews.