4 citations · 4 across the 10 of their papers we have counts for
11 papers
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…
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…
Improving Online Bagging for Complex Imbalanced Data Stream
Bartosz Przybyl, Jerzy Stefanowski
Learning classifiers from imbalanced and concept drifting data streams is still a challenge. Most of the current proposals focus on taking into account changes in the global imbala…
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…
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
Oleksii Furman, Patryk Wielopolski, Łukasz Lenkiewicz +2
The growing complexity of AI systems has intensified the need for transparency through Explainable AI (XAI). Counterfactual explanations (CFs) offer actionable "what-if" scenarios…
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
Patryk Wielopolski, Oleksii Furman, Jerzy Stefanowski +1
We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic f…