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Challenges in Evaluating Interactive Visual Machine Learning Systems.

N Boukhelifa, A Bezerianos, R Chang

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    |October 23, 2020
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    Summary
    This summary is machine-generated.

    Interactive visual machine learning (IVML) involves human-AI collaboration. Evaluating these systems presents challenges in understanding intelligibility, trust, and usability, requiring further research.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Machine Learning

    Background:

    • Interactive visual machine learning (IVML) integrates human and machine intelligence via visual interfaces.
    • This human-in-the-loop approach raises critical issues in intelligibility, trust, and usability.
    • Holistic evaluation of IVML systems, encompassing both human and AI components, remains an open research area.

    Purpose of the Study:

    • To identify and describe the key challenges and research gaps in evaluating IVML systems.
    • To synthesize findings from an IEEE VIS workshop focused on IVML evaluation.
    • To guide future research directions in the field of interactive machine learning.

    Main Methods:

    • The study is based on discussions and findings from an IEEE VIS workshop.
    • It involves identifying and categorizing challenges and research gaps presented by experts.
    • The methodology focuses on a qualitative synthesis of expert opinions and identified issues.

    Main Results:

    • Significant challenges exist in evaluating the combined human and machine intelligence within IVML systems.
    • Specific gaps were identified in assessing intelligibility, trust, and usability.
    • There is a need for standardized evaluation methodologies for IVML.

    Conclusions:

    • The evaluation of IVML systems is complex, requiring consideration of both individual components and the holistic human-AI interaction.
    • Further research is crucial to develop robust frameworks and metrics for IVML system evaluation.
    • Addressing these challenges will enhance the development and adoption of effective IVML solutions.