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Updated: Jul 4, 2025
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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
IEEE Computer Graphics and Applications
|January 31, 2024
Summary
Visualization techniques are crucial for trustworthy machine learning (ML). This study analyzes 542 techniques, revealing a growing trend in using visualization to enhance ML model explainability and trust.
Area of Science:
- Information Visualization
- Visual Analytics
- Machine Learning
Background:
- Visualization for explainable and trustworthy machine learning is a key research area.
- A previous report in 2020 documented 200 techniques.
- This study builds upon that work by analyzing an expanded dataset.
Purpose of the Study:
- To present updated findings on visualization techniques for machine learning as of fall 2023.
- To analyze trends and insights from an expanded dataset of 542 techniques.
- To identify and discuss eight open challenges in the field.
Main Methods:
- Systematic collection and categorization of peer-reviewed articles on visualization techniques for machine learning.
- Analysis of an updated dataset of 542 techniques using a schema of 119 categories.
- Examination of trends and insights from fall 2023 data.
Main Results:
- The field shows a rapidly growing trend in visualization techniques for increasing trust in machine learning models over the past three years.
- Visualization aids in improving popular model explainability methods.
- Visualization is effective in examining new deep learning architectures.
Conclusions:
- Visualization plays a vital role in advancing explainable and trustworthy machine learning.
- Continued research is needed to address identified open challenges.
- The trend indicates increasing reliance on visual methods for ML development and validation.
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