3 papers
cs.LG2026
PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
Salvatore Greco, Jacek Karolczak, Roman Słowiński +1
Explainable artificial intelligence (XAI) has predominantly focused on generating model-centric explanations that approximate the behavior of black-box models. However, such explan…
cs.LG2026
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…
cs.LG2025
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…