Papers and Books

Explanatory Model Analysis.
Explore, Explain, and Examine Predictive Models. With examples in R and Python.
Przemysław Biecek, Tomasz Burzykowski.
Chapman and Hall/CRC, New York (2021).



Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies.
Weronika Hryniewska, Przemysław Bombiński, Patryk Szatkowski, Paulina Tomaszewska, Artur Przelaskowski, Przemysław Biecek.
Pattern Recognition (2021).

Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering.
Alicja Gosiewska, Anna Kozak, Przemysław Biecek.
Decision Support Systems (2021).



The first SARS-CoV-2 genetic variants of concern (VOC) in Poland: The concept of a comprehensive approach to monitoring and surveillance of emerging variants.
Radosław Charkiewicz, Jacek Nikliński, Przemysław Biecek, Joanna Kiśluk, Sławomir Pancewicz, Anna Moniuszko-Malinowska, Robert Flisiak, Adam Krętowski, Janusz Dzięcioł, Marcin Moniuszko, Rafał Gierczyński, Grzegorz Juszczyk, Joanna Reszeć
Advances in Medical Sciences (2021).

Responsible Prediction Making of COVID-19 Mortality (Student Abstract).
Hubert Baniecki, Przemyslaw Biecek.
AAAI Conference (2021).

Paper and package: XAI with DALEX for R and Python
Package: Fairness with fairmodels
< Paper: Landscape of R packages for XAI
Software: COVID-19 Risk Score
Paper and package: Model governance with R (MLOps)