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    This study introduces visual analytics (VA) to bridge gaps between machine learning (ML) and explainable AI (XAI) and human cognition. VA leverages visualization to align AI outputs with human understanding and reasoning for better interaction.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Artificial Intelligence

    Background:

    • Existing research in machine learning (ML) and explainable artificial intelligence (XAI) faces challenges in aligning with human cognitive processes.
    • Conceptual mismatches and information overload hinder effective human understanding of ML/XAI outputs.

    Purpose of the Study:

    • To establish a new research area in visual analytics (VA) that bridges the gap between ML/XAI and human cognition.
    • To adapt ML and XAI methods to human goals, concepts, values, and reasoning patterns.
    • To explore visualization's potential in facilitating human perception and abstractive thinking for AI interpretability.

    Main Methods:

    • Proposing a cross-disciplinary research framework for visual analytics.
    • Formulating specific research directions at the intersection of VA, ML, and XAI.
    • Leveraging visualization techniques to enhance communication between AI systems and human users.

    Main Results:

    • Identified key gaps in conceptual alignment and information processing between ML/XAI and human minds.
    • Highlighted the critical role of visualization in addressing these gaps.
    • Proposed a novel framework to guide future research in this interdisciplinary area.

    Conclusions:

    • Visual analytics offers a promising approach to enhance the human-centricity of ML and XAI.
    • Further research is needed to develop effective VA methods tailored for AI interpretability.
    • Bridging the gap between AI and human cognition is essential for advancing AI adoption and trust.