Visualization Guidelines for Model Performance Communication Between Data Scientists and Subject Matter Experts

Presenting the complexities of a model's performance is a communication bottleneck that threatens collaborations between data scientists and subject matter experts. Accuracy and error metrics alone fail to tell the whole story of a model - its risks, strengths, and limitations - making it difficult for subject matter experts to feel confident in deciding to use a model. As a result, models may fail in unexpected ways if their weaknesses are not clearly understood. Alternatively, models may go unused, as subject matter experts disregard poorly presented models in favor of familiar, yet arguably substandard methods. In this paper, we propose effective use of visualization as a medium for communication between data scientists and subject matter experts. Our research addresses the gap between common practices in model performance communication and the understanding of subject matter experts and decision makers. We derive a set of communication guidelines and recommended visualizations for communicating model performance based on interviews of both data scientists and subject matter experts at the same organization. We conduct a follow-up study with subject matter experts to evaluate the efficacy of our guidelines in presentations of model performance with and without our recommendations. We find that our proposed guidelines made subject matter experts more aware of the tradeoffs of the presented model. Participants realized that current communication methods left them without a robust understanding of the model's performance, potentially giving them misplaced confidence in the use of the model.