5 papers
Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution
Karol Dobiczek, Maciej Mozolewski, Szymon Bobek +3
Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required for clinical integration. Sta…
From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification
Maciej Mozolewski, Betül Bayrak, Kerstin Bach +1
In eXplainable Artificial Intelligence (XAI), instance-based explanations for time series have gained increasing attention due to their potential for actionable and interpretable i…
Explaining Time Series Classifiers with PHAR: Rule Extraction and Fusion from Post-hoc Attributions
Maciej Mozolewski, Szymon Bobek, Grzegorz J. Nalepa
Explaining machine learning (ML) models for time series (TS) classification remains challenging due to the difficulty of interpreting raw time series and the high dimensionality of…
Dataset resulting from the user study on comprehensibility of explainable AI algorithms
Szymon Bobek, Paloma KoryciÅska, Monika Krakowska +5
This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were rec…
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
Szymon Bobek, Paloma KoryciÅska, Monika Krakowska +5
This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intellig…