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Eliciting Model Steering Interactions From Users via Data and Visual Design Probes.

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    Summary
    This summary is machine-generated.

    This study introduces data and visual design probes to help users steer machine learning models in interactive machine learning (IML) systems. Findings reveal how users interact with visual encodings to guide ML model behavior.

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    Area of Science:

    • Human-Computer Interaction (HCI)
    • Machine Learning (ML)
    • Data Visualization

    Background:

    • Interactive Machine Learning (IML) systems are increasingly common, enabling diverse users to analyze data.
    • A key challenge in IML is aligning user interactions with visual elements to effectively steer machine learning models.
    • Existing methods for user-guided ML model steering require further refinement for intuitive interaction design.

    Purpose of the Study:

    • To explore the use of data and visual design probes for eliciting user intentions in steering ML models within IML interfaces.
    • To understand user interaction patterns with visual encodings for model control.
    • To identify factors influencing user interaction choices, including non-interaction, in IML systems.

    Main Methods:

    • An elicitation study was conducted with 20 data analysts possessing varied machine learning expertise.
    • Data and visual design probes were employed to gather user preferences for steering ML models.
    • User interactions with visual encodings were systematically recorded and analyzed.

    Main Results:

    • Findings were summarized as target-interaction pairs, providing insights into desired user steering actions.
    • The utility of the probes was assessed by comparing elicited interactions with those of prior systems.
    • Key factors influencing user engagement with visual encodings, including reasons for refraining from interaction, were identified.

    Conclusions:

    • Data and visual design probes are valuable tools for understanding user intent in IML.
    • Empirical evidence gathered through these probes can inform the development of more effective IML prototypes.
    • Understanding user interaction dynamics is crucial for designing intuitive and powerful IML systems.