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    We developed a framework and system for interactive and explainable machine learning (XAI). This approach helps users understand, diagnose, and refine AI models, leading to a more informed machine learning process.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Machine learning models can be complex and opaque.
    • Understanding model behavior and limitations is crucial for reliable AI deployment.
    • Existing explainable AI (XAI) methods often lack integration and interactivity.

    Purpose of the Study:

    • To propose a framework for interactive and explainable machine learning.
    • To enable users to understand, diagnose, and refine machine learning models.
    • To operationalize the framework with a visual analytics system.

    Main Methods:

    • Developed an iterative XAI pipeline with eight monitoring and steering mechanisms.
    • Integrated quality monitoring, provenance tracking, model comparison, and trust-building.
    • Created explAIner, a visual analytics system within the TensorBoard environment.

    Main Results:

    • The explAIner system operationalizes the proposed interactive XAI framework.
    • A user study with nine participants evaluated the system's usability and perception.
    • The integrated system was found to facilitate an informed machine learning process.

    Conclusions:

    • The proposed framework and explAIner system enhance user understanding and control over machine learning models.
    • The system effectively integrates XAI methods for diagnosing and refining models.
    • User feedback highlights opportunities for future extensions and improvements in interactive XAI.