Presenting a predictive 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 their decision to use a model. As a result, models may fail in unexpected ways or go entirely unused, as subject matter experts disregard poorly presented models in favor of familiar, yet arguably substandard methods. In this paper, we describe an iterative study conducted with both subject matter experts and data scientists to understand the gaps in communication between these two groups. We find that, while the two groups share common goals of understanding the data and predictions of the model, friction can stem from unfamiliar terms, metrics, and visualizations - limiting the transfer of knowledge to SMEs and discouraging clarifying questions being asked during presentations. Based on our findings, we derive a set of communication guidelines that use visualization as a common medium for communicating the strengths and weaknesses of a model. We provide a demonstration of our guidelines in a regression modeling scenario and elicit feedback on their use from subject matter experts. From our demonstration, subject matter experts were more comfortable discussing a model's performance, more aware of the trade-offs for the presented model, and better equipped to assess the model's risks - ultimately informing and contextualizing the model's use beyond text and numbers.

翻译：呈现预测模型的性能是一个沟通瓶颈，它威胁着数据科学家与领域专家之间的合作。仅凭准确率和误差指标无法完整传达模型的风险、优势与局限性，这使得领域专家难以对使用模型的决定建立信心。因此，模型可能在意外情况下失效或被完全弃用，因为领域专家会摒弃呈现不当的模型，转而采用熟悉但可能欠佳的方法。本文描述了一项与领域专家和数据科学家共同开展的迭代研究，旨在理解这两类群体间的沟通鸿沟。我们发现，尽管双方共享理解数据和模型预测结果的共同目标，但沟通摩擦可能源于不熟悉的术语、指标和可视化方式——这限制了知识向领域专家的传递，并阻碍了演示过程中澄清性问题的提出。基于研究结果，我们提出了一套以可视化作为通用媒介的沟通指南，用于传达模型的优势与不足。我们在回归建模场景中演示了该指南的应用，并收集了领域专家对其使用的反馈。演示结果表明，领域专家在讨论模型性能时更为从容，对所呈现模型的权衡取舍有更清晰的认识，并能更好地评估模型风险——最终通过超越文字和数字的方式，为模型的使用提供了信息支撑和情境化解读。