4 citations · 4 across the 10 of their papers we have counts for
6 papers · 1 filter
Challenges in Evaluating Explanation Methods for Static and Evolving Data
Jerzy Stefanowski
This paper addresses the limitations of Explainable Artificial Intelligence (XAI) with respect to insufficient evaluation. They are illustrated through the DetoxAI image recognitio…
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…
Counterfactual Explanations Under Concept Drift
Marcin Kostrzewa, Jerzy Stefanowski, Maciej Zięba
Counterfactual explanations (CFEs) provide actionable recourse, but most methods assume a static framework with fixed data and a trained classifier. This assumption breaks in evolv…
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…
A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
Marcin Kostrzewa, Maciej Zięba, Jerzy Stefanowski
Counterfactual explanations (CFEs) are essential for interpreting black-box models, yet they often become invalid when models are slightly changed. Existing methods for generating…
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…