5 papers
Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations
Jacek Karolczak, Jerzy Stefanowski
Prototype-based explanations offer an intuitive, example-based approach to support the interpretability of machine learning black box classifiers but often lack feature-level granu…
PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
Salvatore Greco, Jacek Karolczak, Roman SÅowiÅski +1
Explainable artificial intelligence (XAI) has predominantly focused on generating model-centric explanations that approximate the behavior of black-box models. However, such explan…
An interpretable prototype parts-based neural network for medical tabular data
Jacek Karolczak, Jerzy Stefanowski
The ability to interpret machine learning model decisions is critical in such domains as healthcare, where trust in model predictions is as important as their accuracy. Inspired by…
This part looks alike this: identifying important parts of explained instances and prototypes
Jacek Karolczak, Jerzy Stefanowski
Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. W…
A-PETE: Adaptive Prototype Explanations of Tree Ensembles
Jacek Karolczak, Jerzy Stefanowski
The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles. An algorithm named Adaptive Prototype Explanatio…