8 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 ZiÄba
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…
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
Oleksii Furman, Patryk Wielopolski, Åukasz 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…
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…
A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
Marcin Kostrzewa, Maciej ZiÄba, 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…