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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Related Experiment Video

Updated: May 16, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Visagreement: Visualizing and Exploring Explanations (Dis)Agreement.

Priscylla Silva, Vitoria Guardieiro, Brian Barr

IEEE Transactions on Visualization and Computer Graphics

|April 4, 2025

Summary

This summary is machine-generated.

Investigating disagreements between machine learning explanation methods is crucial. Visagreement, a new visualization tool, helps users understand when and why these explanations differ, improving trust in AI models.

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Area of Science:

Artificial Intelligence
Machine Learning
Data Visualization

Background:

Distinct machine learning explanation methods can produce conflicting outputs.
Understanding explanation method agreement/disagreement is vital for building trust in AI.
Few studies have investigated the disagreement problem in machine learning explanations.

Purpose of the Study:

To introduce Visagreement, a novel visualization tool for investigating the disagreement problem among machine learning explanation methods.
To enable quantitative comparison and visual exploration of explanation agreements and disagreements.
To assist practitioners in understanding the causes and implications of differing explanations.

Main Methods:

Developed Visagreement, a visualization tool for the disagreement problem.
Integrated quantitative metrics for comparing and evaluating explanations.
Focused on local feature importance methods for tabular data with binary classification.
Conducted expert evaluations assessing effectiveness, usability, and impact on decision-making.

Main Results:

Visagreement effectively reveals phenomena related to explanation disagreements.
Disagreements correlate with explanation quality and machine learning model accuracy.
The tool aids users in determining when to trust AI explanations.
Expert evaluations confirmed Visagreement's effectiveness and user-friendliness.

Conclusions:

Visagreement is a valuable tool for analyzing and exploring disagreements in machine learning explanations.
The tool enhances trust in AI by clarifying explanation variability.
Effective visualization of explanation discrepancies is essential for practical AI deployment.