10 papers
CEL: Comprehensive Counterfactual Explanations Library and Benchmark
Oleksii Furman, Åukasz Lenkiewicz, Marcel MusiaÅek +1
Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's predict…
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…
V4FinBench: Benchmarking Tabular Foundation Models, LLMs, and Standard Methods on Corporate Bankruptcy Prediction
Marcin Kostrzewa, Sebastian Tomczak, Roman Furman +5
Corporate bankruptcy prediction is a high-stakes financial task characterized by severe class imbalance and multi-horizon forecasting demands. Public datasets supporting it remain…
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…
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…
Towards plausibility in time series counterfactual explanations
Marcin Kostrzewa, Krzysztof Galus, Maciej ZiÄba
We present a new method for generating plausible counterfactual explanations for time series classification problems. The approach performs gradient-based optimization directly in…