13 citations · 38 across the 14 of their papers we have counts for
14 papers
Counterfactual Transition Graphs: Evaluating Cross-Class Transition Quality
Syed Muhammad Hamza Zaidi, Szymon Bobek, Grzegorz J. Nalepa +1
Counterfactual (CF) explanations for time-series classifiers are usually evaluated one example at a time: what minimal edit flips this single window's prediction? We argue that the…
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…
Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
Natalia Wojak-Strzelecka, Szymon Bobek, Grzegorz J. Nalepa +1
Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually prev…
Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
Szymon Bobek, Łukasz Bałec, Grzegorz J. Nalepa
Counterfactual explanations improve the actionable interpretability of machine learning models by identifying minimal changes required to achieve a desired outcome. However, existi…
Financial Management System for SMEs: Real-World Deployment of Accounts Receivable and Cash Flow Prediction
Bartłomiej Małkus, Szymon Bobek, Grzegorz J. Nalepa
Small and Medium Enterprises (SMEs), particularly freelancers and early-stage businesses, face unique financial management challenges due to limited resources, small customer bases…
ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts
Bartłomiej Małkus, Szymon Bobek, Grzegorz J. Nalepa
Time series data is one of the most popular data modalities in critical domains such as industry and medicine. The demand for algorithms that not only exhibit high accuracy but als…
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 Korycińska, 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 Korycińska, 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…
Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey
Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro +4
Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan…
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations
Szymon Bobek, Grzegorz J. Nalepa
Explainable artificial intelligence (XAI) is one of the most intensively developed area of AI in recent years. It is also one of the most fragmented with multiple methods that focu…
Explainable Predictive Maintenance
Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan +13
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including…
KnAC: an approach for enhancing cluster analysis with background knowledge and explanations
Szymon Bobek, Michał Kuk, Jakub Brzegowski +2
Pattern discovery in multidimensional data sets has been the subject of research for decades. There exists a wide spectrum of clustering algorithms that can be used for this purpos…
The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems
Krzysztof Kutt, Dominika Drążyk, Maciej Szelążek +2
The paper describes BIRAFFE2 data set, which is a result of an affective computing experiment conducted between 2019 and 2020, that aimed to develop computer models for classificat…