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    Area of Science:

    • Computer Science
    • Human-Computer Interaction

    Background:

    • Machine learning algorithms generate vast amounts of data.
    • Understanding these complex algorithms is challenging for non-experts.
    • Information visualization offers a potential solution for data interpretation.

    Purpose of the Study:

    • To design and evaluate information visualizations for the FaceLift deep-learning algorithm.
    • To help practitioners understand algorithmic image beautification processes.
    • To derive general design guidelines for visualizing complex machine learning.

    Main Methods:

    • Development of a visualization suite for the FaceLift algorithm.
    • Comparison of original and algorithmically beautified urban images.
    • User study involving practitioners to evaluate visualization effectiveness.

    Main Results:

    • Visualizations effectively aided practitioners in understanding the FaceLift algorithm's beautification process.
    • Survey results indicated a positive reception and perceived utility of the visualizations.
    • Insights were gathered on how to make machine learning more accessible through visualization.

    Conclusions:

    • Information visualization is a powerful tool for demystifying complex machine learning.
    • The developed visualizations and derived guidelines can enhance practitioner understanding of AI.
    • Future work can explore broader applications of visualization in explaining AI systems.