preprints in review, journal articles, and papers published in conference proceedings

  1. Be careful when evaluating explanations regarding ground truth
    H. Baniecki*, M. Chrabaszcz*, A. Holzinger, B. Pfeifer, A. Saranti, P. Biecek
  2. Explaining and visualizing black-box models through counterfactual paths
    B. Pfeifer, M. Krzyzinski, H. Baniecki, A. Saranti, A. Holzinger, P. Biecek
  3. Performance is not enough: the story told by a Rashomon quartet
    P. Biecek, H. Baniecki, M. Krzyzinski, D. Cook



  1. Inf. Fus.
    Adversarial attacks and defenses in explainable artificial intelligence: A survey
    H. Baniecki, P. Biecek
    Information Fusion, 2024
    Review of adversarial attacks on explanations and how to defend them.
  2. Bioinf.
    survex: an R package for explaining machine learning survival models
    M. Spytek, M. Krzyzinski, S. H. Langbein, H. Baniecki, M. N. Wright, P. Biecek
    Bioinformatics, 2023
  3. Towards evaluating explanations of vision transformers for medical imaging
    P. Komorowski, H. Baniecki, P. Biecek
    CVPR 2023 Workshops
  4. Hospital length of stay prediction based on multi-modal data towards trustworthy human-AI collaboration in radiomics
    H. Baniecki, B. Sobieski, P. Bombinski, P. Szatkowski, P. Biecek
    AIME 2023
  5. Kno. Sys.
    SurvSHAP(t): Time-dependent explanations of machine learning survival models
    M. Krzyzinski, M. Spytek, H. Baniecki, P. Biecek
    Knowledge-Based Systems, 2023
  6. The grammar of interactive explanatory model analysis
    H. Baniecki, D. Parzych, P. Biecek
    Data Mining and Knowledge Discovery, 2023
    Interactive explanation of a model improves the performance of human decision making.
  7. Multi-omics disease module detection with an explainable greedy decision forest
    B. Pfeifer, H. Baniecki, A. Saranti, P. Biecek, A. Holzinger
    Scientific Reports, 2022
  8. Fooling partial dependence via data poisoning
    H. Baniecki, W. Kretowicz, P. Biecek
    ECML PKDD 2022
    Feature effect explanations can be manipulated in an adversarial manner.
  9. Manipulating SHAP via adversarial data perturbations (student abstract)
    H. Baniecki, P. Biecek
    AAAI 2022
  10. dalex: Responsible machine learning with interactive explainability and fairness in Python
    H. Baniecki, W. Kretowicz, P. Piatyszek, J. Wisniewski, P. Biecek
    Journal of Machine Learning Research, 2021
    2022 John M. Chambers Statistical Software Award
  11. Responsible prediction making of COVID-19 mortality (student abstract)
    H. Baniecki, P. Biecek
    AAAI 2021
  12. modelStudio: Interactive studio with explanations for ML predictive models
    H. Baniecki, P. Biecek
    Journal of Open Source Software, 2019