Hubert Baniecki

find me @hbaniecki

I’m an MSc student in Data Science at Warsaw University of Technology; working in the MI² research lab lead by Przemysław Biecek.

My research interests include explainable machine learning, responsibility, evaluation and adversarial attacks.

Developing and maintaining open-source Python & R packages for explainability of predictive models.

news [archive]

2021-Oct new paper	Manipulating SHAP via Adversarial Data Perturbations (Student Abstract) Accepted for publication at the AAAI Conference on Artificial Intelligence (AAAI-22).
2021-Sep new paper	dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python Accepted for publication in the Journal of Machine Learning Research (JMLR).
2021-May	Contributing to the useR! 2021 conference with a talk Open the Machine Learning Black-Box with modelStudio & Arena, and through a workshop Introduction to Responsible Machine Learning.
2021-Feb	Started a master’s degree in Data Science at Warsaw University of Technology.
2021-Feb	Obtained a bachelor’s degree in Data Science at Faculty of Mathematics and Information Science, Warsaw University of Technology with a thesis Adversarial attacks on Explainable AI methods.

highlights

arXiv

Fooling Partial Dependence via Data Poisoning

H. Baniecki, W. Kretowicz, and P. Biecek

arXiv:2105.12837, 2021

Abstract PDF arXiv Code

Many methods have been developed to understand complex predictive models and high expectations are placed on post-hoc model explainability. It turns out that such explanations are not robust nor trustworthy, and they can be fooled. This paper presents techniques for attacking Partial Dependence (plots, profiles, PDP), which are among the most popular methods of explaining any predictive model trained on tabular data. We showcase that PD can be manipulated in an adversarial manner, which is alarming, especially in financial or medical applications where auditability became a must-have trait supporting black-box models. The fooling is performed via poisoning the data to bend and shift explanations in the desired direction using genetic and gradient algorithms. To the best of our knowledge, this is the first work performing attacks on variable dependence explanations. The novel approach of using a genetic algorithm for doing so is highly transferable as it generalizes both ways: in a model-agnostic and an explanation-agnostic manner.

JMLR MLOSS

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python

H. Baniecki, W. Kretowicz, P. Piatyszek, J. Wisniewski, and P. Biecek

Journal of Machine Learning Research (JMLR), 2021

Abstract HTML arXiv Code Website

In modern machine learning, we observe the phenomenon of opaqueness debt, which manifests itself by an increased risk of discrimination, lack of reproducibility, and deflated performance due to data drift. An increasing amount of available data and computing power results in the growing complexity of black-box predictive models. To manage these issues, good MLOps practice asks for better validation of model performance and fairness, higher explainability, and continuous monitoring. The necessity for deeper model transparency comes from both scientific and social domains and is also caused by emerging laws and regulations on artificial intelligence. To facilitate the responsible development of machine learning models, we introduce dalex, a Python package which implements a model-agnostic interface for interactive explainability and fairness. It adopts the design crafted through the development of various tools for explainable machine learning; thus, it aims at the unification of existing solutions. This library's source code and documentation are available under open license at https://python.drwhy.ai.
AAAI SA

Responsible Prediction Making of COVID-19 Mortality (Student Abstract)

H. Baniecki, and P. Biecek

AAAI Conference on Artificial Intelligence (AAAI), 2021

Abstract HTML Poster Website

For high-stakes prediction making, the Responsible Artificial Intelligence (RAI) is more important than ever. It builds upon Explainable Artificial Intelligence (XAI) to advance the efforts in providing fairness, model explainability, and accountability to the AI systems. During the literature review of COVID-19 related prognosis and diagnosis, we found out that most of the predictive models are not faithful to the RAI principles, which can lead to biassed results and wrong reasoning. To solve this problem, we show how novel XAI techniques boost transparency, reproducibility and quality of models.