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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Visualization

    Background:

    • Deep learning models achieve state-of-the-art performance across various AI tasks.
    • Lack of transparency and control in deep learning decision-making poses significant challenges.
    • Critical domains like precision medicine and law enforcement require interpretable AI.

    Purpose of the Study:

    • To review current research on making deep learning models interpretable and controllable.
    • To discuss the integration of visual analytics, information visualization, and machine learning.
    • To identify challenges and future research directions in explainable AI.

    Main Methods:

    • Literature review of visual analytics, information visualization, and machine learning.
    • Analysis of techniques for enhancing deep learning interpretability.
    • Discussion of methods for human control over AI processes.

    Main Results:

    • Existing research offers various approaches to enhance deep learning interpretability.
    • Visual analytics and information visualization are key components for understanding AI.
    • Challenges remain in achieving full human control and understanding of complex AI.

    Conclusions:

    • Making deep learning interpretable and controllable is crucial for its adoption in critical applications.
    • Interdisciplinary approaches combining AI with visualization are promising.
    • Further research is needed to address the identified challenges and advance explainable AI.