Visualization in Data Science (VDS at IEEE VIS 2016)

Explanatory Visual Analytics for Enhancing Human Interpretability of Machine Learning Models

Josua Krause, Aritra Dasgupta, Enrico Bertini


The recent successes of machine learning (ML) have exposed the need for making models more interpretable and accessible to different stakeholders in the data science ecosystem, like domain experts, data analysts, and even the general public. The assumption here is that higher interpretability will lead to more confident human decision-making based on model outcomes. In this talk, we report on two main contributions. First, we describe the role of model explanations by providing examples of well-known ML models. Second, we discuss how explanatory visual analytics systems can be instantiated for enhancing model interpretability by referring to our past and current research.

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