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Updated: Jul 6, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

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On the interpretability of part-prototype based classifiers: a human centric analysis.

Omid Davoodi¹, Shayan Mohammadizadehsamakosh², Majid Komeili³

¹School of Computer Science, Carleton University, Ottawa, ON, Canada. omid.davoudi@carleton.ca.

Scientific Reports

|December 28, 2023

Summary

This summary is machine-generated.

We developed a framework to evaluate how well humans understand part-prototype networks, a type of interpretable AI. Our comprehensive experiments confirm its effectiveness in assessing model interpretability.

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Part-prototype networks offer an interpretable alternative to black-box image classifiers.
The human-centric interpretability of these models remains underexplored.
Previous research often suffers from flawed experimental design, limiting reliability.

Purpose of the Study:

To propose a robust framework for evaluating the human-perceived interpretability of part-prototype models.
To address limitations in experimental design and task representation in prior work.
To provide reliable and valid metrics for assessing model interpretability.

Main Methods:

Developed a novel framework with three actionable metrics and experimental procedures.
Conducted extensive human-subject experiments using Amazon Mechanical Turk.
Focused on human-centered evaluation of part-prototype network interpretability.

Main Results:

The proposed framework effectively assesses the interpretability of various part-prototype models.
Experiments yielded reliable insights into the interpretability properties of these models.
Demonstrated the framework's capability to overcome previous methodological issues.

Conclusions:

The developed framework provides a reliable method for evaluating human interpretability of part-prototype networks.
This work represents a comprehensive assessment of part-prototype model interpretability within a unified structure.
The findings pave the way for more trustworthy and understandable AI systems.