Summary of Developing Explainable Machine Learning Model Using Augmented Concept Activation Vector, by Reza Hassanpour et al.
Developing Explainable Machine Learning Model using Augmented Concept Activation Vector
by Reza Hassanpour, Kasim Oztoprak, Niels Netten, Tony Busker, Mortaza S. Bargh, Sunil Choenni, Beyza Kizildag, Leyla Sena Kilinc
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models use high-dimensional feature spaces to map inputs to class labels, but these features don’t always align with physical concepts humans can understand. This lack of transparency hinders explanation of model decisions. Our method measures correlation between high-level concepts and model decisions, isolating the impact of a given concept and quantifying it accurately. We also explore frequent patterns in machine learning models that occur in imbalanced datasets. By applying our method to fundus images, we successfully measured the impact of radiomic patterns on model decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can be really good at recognizing pictures or classifying objects, but they don’t always explain why they make certain decisions. This makes it hard for humans to understand what’s going on inside the model. Our research proposes a new way to measure how much different concepts in the data affect the model’s choices. We used this method to study how patterns in medical images help or hurt the model’s accuracy. |
Keywords
» Artificial intelligence » Machine learning
