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DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models.

Furui Cheng, Yao Ming, Huamin Qu

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    Summary
    This summary is machine-generated.

    This study introduces DECE, an interactive system using counterfactual explanations to explore machine learning model behavior. DECE helps users understand and customize model decisions for better transparency and actionable insights.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Machine learning models are widely used in decision-making, necessitating greater transparency and explainability.
    • Counterfactual explanations offer human-friendly and actionable insights into model predictions by identifying minimal input changes for desired outcomes.

    Purpose of the Study:

    • To develop an interactive visualization system, DECE, for understanding and exploring machine learning model behavior.
    • To support diverse users, from decision subjects to model developers, in analyzing model decisions.

    Main Methods:

    • Designed DECE, an interactive visualization system leveraging counterfactual explanations.
    • Integrated instance-level and subgroup-level counterfactual explanations for comprehensive analysis.
    • Introduced user-customizable interactions for generating actionable counterfactual explanations.

    Main Results:

    • DECE facilitates exploratory analysis of model decisions on individual instances and data subsets.
    • The system effectively combines instance- and subgroup-level counterfactual explanations.
    • User interactions enable tailored generation of actionable counterfactual explanations.

    Conclusions:

    • DECE demonstrates effectiveness in supporting decision exploration and instance explanation tasks.
    • The system enhances the understanding of machine learning model behavior through interactive counterfactual analysis.
    • DECE provides a valuable tool for improving transparency and trust in machine learning applications.