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Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts.

Ashley Suh, Gabriel Appleby, Erik W Anderson

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    Summary
    This summary is machine-generated.

    Effective communication of predictive model performance is crucial for collaboration. This study introduces visualization guidelines to help subject matter experts understand model risks and strengths, improving decision-making.

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    Area of Science:

    • Data Science
    • Human-Computer Interaction
    • Scientific Communication

    Background:

    • Predictive model performance presentation is a communication bottleneck hindering data scientist and subject matter expert (SME) collaboration.
    • Standard metrics (accuracy, error) inadequately convey model risks, strengths, and limitations, leading to SME distrust or underutilization.
    • Existing communication gaps stem from unfamiliar terminology, metrics, and visualizations, discouraging SME engagement and knowledge transfer.

    Purpose of the Study:

    • To investigate communication gaps between data scientists and SMEs regarding predictive model performance.
    • To develop and evaluate a set of communication guidelines using visualization to bridge these gaps.
    • To enhance SME confidence and understanding of model capabilities and limitations.

    Main Methods:

    • An iterative study involving data scientists and SMEs to identify communication challenges.
    • Derivation of communication guidelines centered on visual representations of model performance.
    • Demonstration of guidelines in a regression modeling context with subsequent SME feedback collection.

    Main Results:

    • SMEs reported increased comfort in discussing model performance after guideline implementation.
    • SMEs demonstrated greater awareness of model trade-offs and risks.
    • Visualizations facilitated a more contextualized understanding of model utility beyond raw numbers.

    Conclusions:

    • Visualization-based communication guidelines improve SME comprehension and trust in predictive models.
    • Bridging the communication gap enhances the effective adoption and application of data science models.
    • Contextualizing model performance visually empowers SMEs for informed decision-making.