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    Understanding machine learning (ML) models is crucial for societal decisions. This study introduces SUBPLEX, a visual analytics approach for analyzing ML local explanations across datasets by identifying interpretable subpopulations.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Interpreting machine learning (ML) models is vital for high-stakes decisions in areas like healthcare and finance.
    • Local explanation techniques offer insights into single ML model predictions but do not scale for dataset-wide understanding.
    • Existing methods lack effective ways to analyze patterns within local explanations across entire datasets.

    Purpose of the Study:

    • To address the limitations of local explanation analysis for understanding ML model behavior on a whole dataset.
    • To propose SUBPLEX, a novel visual analytics approach for interpreting local ML explanations.
    • To enable users to identify and understand subpopulations within local explanations.

    Main Methods:

    • Development of SUBPLEX, a visual analytics approach incorporating steerable clustering.
    • Integration of projection visualization techniques for exploring local explanations.
    • Facilitation of user-driven subpopulation identification leveraging domain expertise.

    Main Results:

    • SUBPLEX enables users to visually analyze and derive interpretable subpopulations from local ML explanations.
    • The approach facilitates a more comprehensive understanding of ML model behavior beyond single instances.
    • Evaluation through use cases and expert feedback demonstrates the utility of SUBPLEX.

    Conclusions:

    • SUBPLEX effectively tackles the challenge of analyzing local explanations at a dataset scale.
    • The visual analytics approach empowers users to uncover meaningful patterns and subpopulations in ML model interpretations.
    • This work contributes to more transparent and trustworthy ML applications in critical domains.