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    Explainable guidance enhances user trust in data analysis systems. Clear explanations for system interventions are crucial for effective guidance, improving user-system relationships and data interpretation in visual analytics.

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    Area of Science:

    • Human-Computer Interaction
    • Information Visualization
    • Explainable AI

    Background:

    • Guidance-enhanced visual analytics (VA) aids users in data interpretation.
    • Lack of clear explanations for system interventions can reduce guidance effectiveness.
    • Effective guidance requires context-specific explanations to build user trust.

    Purpose of the Study:

    • To introduce the concept of explainable guidance in VA.
    • To propose a model for designing explainability strategies for VA guidance.
    • To investigate the impact of explainable guidance on user trust and system adoption.

    Main Methods:

    • Literature review on explainable AI, VA guidance, and user-system trust.
    • Development of a model for designing explainable guidance strategies.
    • Application of the model to two VA scenarios and a design walk-through with an expert.

    Main Results:

    • The proposed model facilitates the design of context-aware explanations for VA guidance.
    • Explainable guidance positively influences user trust in VA systems.
    • The model aids designers in articulating the rationale behind system interventions.

    Conclusions:

    • Explainable guidance is essential for effective VA systems.
    • The proposed model provides a framework for designing trustworthy and context-effective guidance.
    • Future research should explore the empirical validation of explainable guidance strategies in diverse VA contexts.